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Sample records for cancer network study

  1. Profiling metabolic networks to study cancer metabolism.

    Science.gov (United States)

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention.

  2. The Rare Cancer Network: ongoing studies and future strategy

    Directory of Open Access Journals (Sweden)

    Mahmut Ozsahin

    2014-08-01

    Full Text Available The Rare Cancer Network (RCN was formed in the early 1990’s to create a global network that could pool knowledge and resources in the studies of rare malignancies whose infrequency prevented both their study with prospective clinical trials. To date, the RCN has initiated 74 studies resulting in 46 peer reviewed publications. The First International Symposium of the Rare Cancer Network took place in Nice in March of 2014. Status updates and proposals for new studies were heard for fifteen topics. Ongoing studies continue for cardiac sarcomas, thyroid cancers, glomus tumors, and adult medulloblastomas. New proposals were presented at the symposium for primary hepatic lymphoma, solitary fibrous tumors, Rosai-Dorfman disease, tumors of the ampulla of Vater, salivary gland tumors, anorectal melanoma, midline nuclear protein in testes carcinoma, pulmonary lymphoepithelioma-like carcinoma, adenoid cystic carcinoma of the trachea, osteosarcomas of the mandible, and extra-cranial hemangiopericytoma. This manuscript presents the abstracts of those proposals and updates on ongoing studies, as well a brief summary of the vision and future of the RCN.

  3. Bladder Cancer Advocacy Network

    Science.gov (United States)

    ... future bladder cancer research through the Patient Survey Network. Read More... The JPB Foundation 2016 Bladder Cancer ... 2016 Young Investigator Awardees The Bladder Cancer Advocacy Network (BCAN) has announced the recipients of the 2016 ...

  4. Primary Hepatic Lymphoma: A Retrospective, Multicenter Rare Cancer Network Study

    Science.gov (United States)

    Ugurluer, Gamze; Miller, Robert C.; Li, Yexiong; Thariat, Juliette; Ghadjar, Pirus; Schick, Ulrike; Ozsahin, Mahmut

    2016-01-01

    Primary hepatic lymphoma (PHL) is a rare malignancy. We aimed to assess the clinical profile, outcome and prognostic factors in PHL through the Rare Cancer Network (RCN). A retrospective analysis of 41 patients was performed. Median age was 62 years (range, 23-86 years) with a male-to-female ratio of 1.9:1.0. Abdominal pain or discomfort was the most common presenting symptom. Regarding B-symptoms, 19.5% of patients had fever, 17.1% weight loss, and 9.8% night sweats. The most common radiological presentation was multiple lesions. Liver function tests were elevated in 56.1% of patients. The most common histopathological diagnosis was diffuse large B-cell lymphoma (65.9%). Most of the patients received Chop-like (cyclophosphamide, doxorubicin, vincristine, and prednisone) regimens; 4 patients received radiotherapy (dose range, 30.6-40.0 Gy). Median survival was 163 months, and 5- and 10-year overall survival rates were 77 and 59%, respectively. The 5- and 10-year disease-free and lymphoma-specific survival rates were 69, 56, 87 and 70%, respectively. Multivariate analysis revealed that fever, weight loss, and normal hemoglobin level were the independent factors influencing the outcome. In this retrospective multicenter RCN study, patients with PHL had a relatively better prognosis than that reported elsewhere. Multicenter prospective studies are still warranted to establish treatment guidelines, outcome, and prognostic factors. PMID:27746888

  5. Primary hepatic lymphoma: a retrospective, multicenter Rare Cancer Network study

    Directory of Open Access Journals (Sweden)

    Gamze Ugurluer

    2016-10-01

    Full Text Available Primary hepatic lymphoma (PHL is a rare malignancy. We aimed to assess the clinical profile, outcome and prognostic factors in PHL through the Rare Cancer Network (RCN. A retrospective analysis of 41 patients was performed. Median age was 62 years (range, 23- 86 years with a male-to-female ratio of 1.9:1.0. Abdominal pain or discomfort was the most common presenting symptom. Regarding B-symptoms, 19.5% of patients had fever, 17.1% weight loss, and 9.8% night sweats. The most common radiological presentation was multiple lesions. Liver function tests were elevated in 56.1% of patients. The most common histopathological diagnosis was diffuse large B-cell lymphoma (65.9%. Most of the patients received Chop-like (cyclophosphamide, doxorubicin, vincristine, and prednisone regimens; 4 patients received radiotherapy (dose range, 30.6-40.0 Gy. Median survival was 163 months, and 5- and 10-year overall survival rates were 77 and 59%, respectively. The 5- and 10-year disease-free and lymphoma-specific survival rates were 69, 56, 87 and 70%, respectively. Multivariate analysis revealed that fever, weight loss, and normal hemoglobin level were the independent factors influencing the outcome. In this retrospective multicenter RCN study, patients with PHL had a relatively better prognosis than that reported elsewhere. Multicenter prospective studies are still warranted to establish treatment guidelines, outcome, and prognostic factors.

  6. Mucosal Kaposi sarcoma, a Rare Cancer Network study

    Directory of Open Access Journals (Sweden)

    Robert C. Miller

    2012-10-01

    Full Text Available Kaposi’s sarcoma (KS most often affect the skin but occasionally affect the mucosa of different anatomic sites. The management of mucosal KS is seldom described in the literature. Data from 15 eligible patients with mucosal KS treated between 1994 and 2008 in five institutions within three countries of the Rare Cancer Network group were collected. The inclusion criteria were as follows: age >16 years, confirmed pathological diagnosis, mucosal stages I and II, and a minimum of 6 months’ follow-up after treatment. Head and neck sites were the most common (66%. Eleven cases were HIV-positive. CD4 counts correlated with disease stage. Twelve patients had biopsy only while three patients underwent local resection. Radiotherapy (RT was delivered whatever their CD4 status was. Median total radiation dose was 16.2 Gy (0-45 delivered in median 17 days (0-40 with four patients receiving no RT. Six patients underwent chemotherapy and received from 1 to 11 cycles of various regimens namely vinblastin, caelyx, bleomycine, or interferon, whatever their CD4 counts was. Five-year disease free survival were 81.6% and 75.0% in patients undergoing RT or not, respectively. Median survival was 66.9 months. Radiation-induced toxicity was at worse grade 1-2 and was manageable whatever patients’ HIV status. This small series of mucosal KSs revealed that relatively low-dose RT is overall safe and efficient in HIV-positive and negative patients. Since there are distant relapses either in multicentric cutaneous or visceral forms in head and neck cases, the role of systemic treatments may be worth investigations in addition to RT of localized disease. Surgery may be used for symptomatic lesions, with caution given the risk of bleeding.

  7. Network structure and the role of key players in a translational cancer research network: a study protocol

    OpenAIRE

    Long, Janet C; Cunningham, Frances C.; Braithwaite, Jeffrey

    2012-01-01

    Introduction Translational research networks are a deliberate strategy to bridge the gulf between biomedical research and clinical practice through interdisciplinary collaboration, supportive funding and infrastructure. The social network approach examines how the structure of the network and players who hold important positions within it constrain or enable function. This information can be used to guide network management and optimise its operations. The aim of this study was to describe th...

  8. Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy

    Institute of Scientific and Technical Information of China (English)

    HE Jia; HE Xian-min; ZHANG Zhi-jian

    2002-01-01

    Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected.There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.

  9. Introduction: Cancer Gene Networks.

    Science.gov (United States)

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  10. Network Topologies Decoding Cervical Cancer.

    Directory of Open Access Journals (Sweden)

    Sarika Jalan

    Full Text Available According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

  11. Management of Adenoid Cystic Carcinoma of the Breast: A Rare Cancer Network Study

    Energy Technology Data Exchange (ETDEWEB)

    Khanfir, Kaouthar, E-mail: kaouthar.khanfir@rsv-gnw.ch [Hopital de Sion, CHCVs, Sion (Switzerland); Kallel, Adel [Institut Gustave Roussy, Villejuif (France); Villette, Sylviane [Centre Rene Huguenin, Paris (France); Belkacemi, Yazid [CHU Henri Mondor, Centre Oscar Lambret, Lille (France); Vautravers, Claire [Centre George Francois Leclerc, Dijon (France); Nguyen, TanDat [Institut Jean Gaudinot, Reims (France); Miller, Robert [Mayo Clinic, Rochester, Minnesota (United States); Li Yexiong [Peking Union Medical College, Beijing (China); Taghian, Alphonse G. [Massachusetts General Hospital, Boston, Massachusetts (United States); Boersma, Liesbeth [Maastricht University Medical Center (MAASTRO clinic), Maastricht (Netherlands); Poortmans, Philip [Dr. Bernard Verbeeten Institute, Tilburg (Netherlands); Goldberg, Hadassah [Western Galilee Hospital-Nahariya, Nahariya (Israel); Vees, Hansjorg [Hopitaux Universitaires de Geneve, Geneva (Switzerland); Senkus, Elzbieta [Medical University of Gdansk, Gdansk (Poland); Igdem, Sefik; Ozsahin, Mahmut [Istanbul Bilim University, Istanbul (Turkey); Jeanneret Sozzi, Wendy [Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)

    2012-04-01

    Background: Mammary adenoid cystic carcinoma (ACC) is a rare breast cancer. The aim of this retrospective study was to assess prognostic factors and patterns of failure, as well as the role of radiation therapy (RT), in ACC. Methods: Between January 1980 and December 2007, 61 women with breast ACC were treated at participating centers of the Rare Cancer Network. Surgery consisted of lumpectomy in 41 patients and mastectomy in 20 patients. There were 51(84%) stage pN0 and 10 stage cN0 (16%) patients. Postoperative RT was administered to 40 patients (35 after lumpectomy, 5 after mastectomy). Results: With a median follow-up of 79 months (range, 6-285), 5-year overall and disease-free survival rates were 94% (95% confidence interval [CI], 88%-100%) and 82% (95% CI, 71%-93%), respectively. The 5-year locoregional control (LRC) rate was 95% (95% CI, 89%-100%). Axillary lymph node dissection or sentinel node biopsy was performed in 84% of cases. All patients had stage pN0 disease. In univariate analysis, survival was not influenced by the type of surgery or the use of postoperative RT. The 5-year LRC rate was 100% in the mastectomy group versus 93% (95% CI, 83%-100%) in the breast-conserving surgery group, respectively (p = 0.16). For the breast-conserving surgery group, the use of RT significantly correlated with LRC (p = 0.03); the 5-year LRC rates were 95% (95% CI, 86%-100%) for the RT group versus 83% (95% CI, 54%-100%) for the group receiving no RT. No local failures occurred in patients with positive margins, all of whom received postoperative RT. Conclusion: Breast-conserving surgery is the treatment of choice for patients with ACC breast cancer. Axillary lymph node dissection or sentinel node biopsy might not be recommended. Postoperative RT should be proposed in the case of breast-conserving surgery.

  12. Cancer association study of aminoacyl-tRNA synthetase signaling network in glioblastoma.

    Directory of Open Access Journals (Sweden)

    Yong-Wan Kim

    Full Text Available Aminoacyl-tRNA synthetases (ARSs and ARS-interacting multifunctional proteins (AIMPs exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM, the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN, 124 cancer-associated druggable target genes (DTGs and 404 protein-protein interactors (PPIs of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05. The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS, and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc. Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM.

  13. Diabetes mellitus and cancer risk in a network of case-control studies.

    Science.gov (United States)

    Bosetti, Cristina; Rosato, Valentina; Polesel, Jerry; Levi, Fabio; Talamini, Renato; Montella, Maurizio; Negri, Eva; Tavani, Alessandra; Zucchetto, Antonella; Franceschi, Silvia; Corrao, Giovanni; La Vecchia, Carlo

    2012-01-01

    Diabetes has been associated to the risk of a few cancer sites, though quantification of this association in various populations remains open to discussion. We analyzed the relation between diabetes and the risk of various cancers in an integrated series of case-control studies conducted in Italy and Switzerland between 1991 and 2009. The studies included 1,468 oral and pharyngeal, 505 esophageal, 230 gastric, 2,390 colorectal, 185 liver, 326 pancreatic, 852 laryngeal, 3,034 breast, 607 endometrial, 1,031 ovarian, 1,294 prostate, and 767 renal cell cancer cases and 12,060 hospital controls. The multivariate odds ratios (OR) for subjects with diabetes as compared to those without-adjusted for major identified confounding factors for the cancers considered through logistic regression models-were significantly elevated for cancers of the oral cavity/pharynx (OR = 1.58), esophagus (OR = 2.52), colorectum (OR = 1.23), liver (OR = 3.52), pancreas (OR = 3.32), postmenopausal breast (OR = 1.76), and endometrium (OR = 1.70). For cancers of the oral cavity, esophagus, colorectum, liver, and postmenopausal breast, the excess risk persisted over 10 yr since diagnosis of diabetes. Our data confirm and further quantify the association of diabetes with colorectal, liver, pancreatic, postmenopausal breast, and endometrial cancer and suggest forthe first time that diabetes may also increase the risk of oral/pharyngeal and esophageal cancer.

  14. Early-Stage Primary Bone Lymphoma: A Retrospective, Multicenter Rare Cancer Network (RCN) Study

    Energy Technology Data Exchange (ETDEWEB)

    Cai Ling [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland); Sun Yat-sen University Cancer Center, Guangzhou, Guangdong (China); Stauder, Michael C. [Mayo Clinic, Rochester, MN (United States); Zhang Yujing [Sun Yat-sen University Cancer Center, Guangzhou, Guangdong (China); Poortmans, Philip [Verbeeten Institute, Tilburg (Netherlands); Li Yexiong [Cancer Hospital, Chinese Academy of Medical Sciences, Beijing (China); Constantinou, Nicolaos [Theagenio Cancer Hospital, Thessaloniki, Macedonia (Greece); Thariat, Juliette [Centre Anti-Cancereux Antoine-Lacassagne, Nice, Cote d' Azur (France); Kadish, Sidney P. [University of Massachusetts Medical School, Worcester, MA (United States); Nguyen, Tan Dat [Institut Jean-Godinot, Reims, Champagne-Ardenne (France); Kirova, Youlia M. [Institut Curie, Paris (France); Ghadjar, Pirus [Inselspital, Bern University Hospital, and University of Bern (Switzerland); Weber, Damien C. [Hopitaux Universitaires de Geneve (Switzerland); Bertran, Victoria Tuset [Hospital Universitari Germans Trias i Pujol, Barcelona (Spain); Ozsahin, Mahmut [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland); Mirimanoff, Rene-Olivier, E-mail: Rene-Olivier.Mirimanoff@chuv.ch [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland)

    2012-05-01

    Purpose: Primary bone lymphoma (PBL) represents less than 1% of all malignant lymphomas. In this study, we assessed the disease profile, outcome, and prognostic factors in patients with Stages I and II PBL. Patients and Methods: Thirteen Rare Cancer Network (RCN) institutions enrolled 116 consecutive patients with PBL treated between 1987 and 2008 in this study. Eighty-seven patients underwent chemoradiotherapy (CXRT) without (78) or with (9) surgery, 15 radiotherapy (RT) without (13) or with (2) surgery, and 14 chemotherapy (CXT) without (9) or with (5) surgery. Median RT dose was 40 Gy (range, 4-60). The median number of CXT cycles was six (range, 2-8). Median follow-up was 41 months (range, 6-242). Results: The overall response rate at the end of treatment was 91% (complete response [CR] 74%, partial response [PR] 17%). Local recurrence or progression was observed in 12 (10%) patients and systemic recurrence in 17 (15%). The 5-year overall survival (OS), lymphoma-specific survival (LSS), and local control (LC) were 76%, 78%, and 92%, respectively. In univariate analyses (log-rank test), favorable prognostic factors for OS and LSS were International Prognostic Index (IPI) score {<=}1 (p = 0.009), high-grade histology (p = 0.04), CXRT (p = 0.05), CXT (p = 0.0004), CR (p < 0.0001), and RT dose >40 Gy (p = 0.005). For LC, only CR and Stage I were favorable factors. In multivariate analysis, IPI score, RT dose, CR, and CXT were independently influencing the outcome (OS and LSS). CR was the only predicting factor for LC. Conclusion: This large multicenter retrospective study confirms the good prognosis of early-stage PBL treated with combined CXRT. An adequate dose of RT and complete CXT regime were associated with better outcome.

  15. Outcome and Prognostic Factors in Endometrial Stromal Tumors: A Rare Cancer Network Study

    Energy Technology Data Exchange (ETDEWEB)

    Schick, Ulrike, E-mail: Ulrike.schick@icr.ac.uk [Department of Radiation Oncology, University Hospital, Geneva (Switzerland); Bolukbasi, Yasmin [Department of Radiation Oncology, Ege University Hospital, Izmir (Turkey); Thariat, Juliette [Department of Radiation Oncology, Antoine Lacassagne Center, Nice (France); Abdah-Bortnyak, Roxolyana; Kuten, Abraham [Department of Radiation Oncology, Rambam Medical Center, Haifa (Israel); Igdem, Sefik [Department of Radiation Oncology, Metropolitan Hospital, Istanbul (Turkey); Caglar, Hale [Department of Radiation Oncology, Marmara University Hospital, Istanbul (Turkey); Ozsaran, Zeynep [Department of Radiation Oncology, Ege University Hospital, Izmir (Turkey); Loessl, Kristina [Department of Radiation Oncology, University Hospital, Bern (Switzerland); Schleicher, Ursula [Department of Radiation Oncology, Dueren Hospital, Dueren (Germany); Zwahlen, Daniel [Department of Radiation Oncology, William Buckland Radiotherapy Centre, Melbourne (Australia); Villette, Sylviane [Department of Radiation Oncology, Rene Huguenin Center, Saint-Cloud (France); Vees, Hansjoerg [Department of Radiation Oncology, University Hospital, Geneva (Switzerland); Department of Radiation Oncology, Sion Hospital, Sion (Switzerland)

    2012-04-01

    Purpose: To provide further understanding regarding outcome and prognostic factors of endometrial stromal tumors (EST). Methods and Materials: A retrospective analysis was performed on the records of 59 women diagnosed with EST and treated with curative intent between 1983 and 2007 in the framework of the Rare Cancer Network. Results: Endometrial stromal sarcomas (ESS) were found in 44% and undifferentiated ESS (UES) in 49% of the cases. In 7% the grading was unclear. Of the total number of patients, 33 had Stage I, 4 Stage II, 20 Stage III, and 1 presented with Stage IVB disease. Adjuvant chemotherapy was administered to 12 patients, all with UES. External-beam radiotherapy (RT) was administered postoperatively to 48 women. The median follow-up was 41.4 months. The 5-year overall survival (OS) rate was 96.2% and 64.8% for ESS and UES, respectively, with a corresponding 5-year disease-free survival (DFS) rate of 49.4% and 43.4%, respectively. On multivariate analysis, adjuvant RT was an independent prognostic factor for OS (p = 0.007) and DFS (p = 0.013). Locoregional control, DFS, and OS were significantly associated with age ({<=}60 vs. >60 years), grade (ESS vs. UES), and International Federation of Gynecology and Obstetrics stage (I-II vs. III-IV). Positive lymph node staging had an impact on OS (p < 0.001). Conclusion: The prognosis of ESS differed from that of UES. Endometrial stromal sarcomas had an excellent 5-year OS, whereas the OS in UES was rather low. However, half of ESS patients had a relapse. For this reason, adjuvant treatment such as RT should be considered even in low-grade tumors. Multicenter randomized studies are still warranted to establish clear guidelines.

  16. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2015-12-01

    disease. The Network combines considerable expertise in multi-disciplinary tissue- based PCa research, excellence in PCa histopathology and molecular ... Memorial Sloan Kettering and University of Washington that successfully collaborated on a PCBN competitive renewal application. 15. SUBJECT TERMS... Memorial Sloan-Kettering Cancer Center (MSKCC: PI Anuradha Gopalan, MD, Co-PI Howard Scher, MD). These 2 Network Sites were chosen deliberately to add

  17. Pathway and network analysis of cancer genomes

    DEFF Research Database (Denmark)

    Creixell, Pau; Reimand, Jueri; Haider, Syed

    2015-01-01

    Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been...... large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations....

  18. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  19. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.

    Science.gov (United States)

    Lancashire, Lee J; Lemetre, Christophe; Ball, Graham R

    2009-05-01

    Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

  20. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2013-07-01

    May after a long illness. Her responsibilities have been subsumed by Helen Fedor and Medha Darshan, and will be taken over by a Clinical...of the Prostate Cancer Biorepository Network Medha Darshan1*, Qizhi Zheng1*, Helen L. Fedor1*, Nicolas Wyhs2, Srinivasan Yegnasubramanian2...samples using the DNeasy Blood &Tissue kit (Qiagen). DNA quantification and 260:280 ratios were obtained by Nanodrop (Thermo Fisher Scientific Inc

  1. [Scientific production and cancer-related collaboration networks in Peru 2000-2011: a bibliometric study in Scopus and Science Citation Index].

    Science.gov (United States)

    Mayta-Tristán, Percy; Huamaní, Charles; Montenegro-Idrogo, Juan José; Samanez-Figari, César; González-Alcaide, Gregorio

    2013-03-01

    A bibliometric study was carried out to describe the scientific production on cancer written by Peruvians and published in international health journals, as well as to assess the scientific collaboration networks. It included articles on cancer written in Peru between the years 2000 and 2011 and published in health journals indexed in SCOPUS or Science Citation Index Expanded. In the 358 articles identified, an increase in the production was seen, from 4 articles in 2000 to 57 in 2011.The most studied types were cervical cancer (77 publications); breast cancer (53), and gastric cancer (37). The National Institute of Neoplastic Diseases (INEN) was the most productive institution (121 articles) and had the highest number of collaborations (180 different institutions). 52 clinical trials were identified, 29 of which had at least one author from INEN. We can conclude that, cancer research is increasing in Peru, the INEN being the most productive institution, with an important participation in clinical trials.

  2. The attention network changes in breast cancer patients receiving neoadjuvant chemotherapy: Evidence from an arterial spin labeling perfusion study

    Science.gov (United States)

    Chen, Xingui; He, Xiaoxuan; Tao, Longxiang; Cheng, Huaidong; Li, Jingjing; Zhang, Jingjie; Qiu, Bensheng; Yu, Yongqiang; Wang, Kai

    2017-01-01

    To investigate the neural mechanisms underlying attention deficits that are related to neoadjuvant chemotherapy in combination with cerebral perfusion. Thirty one patients with breast cancer who were scheduled to receive neoadjuvant chemotherapy and 34 healthy control subjects were included. The patients completed two assessments of the attention network tasks (ANT), neuropsychological background tests, and the arterial spin labeling scan, which were performed before neoadjuvant chemotherapy and after completing chemotherapy. After neoadjuvant chemotherapy, the patients exhibited reduced performance in the alerting and executive control attention networks but not the orienting network (p breast cancer. The results demonstrated that neoadjuvant chemotherapy influences hemodynamic activity in different brain areas through increasing cerebral perfusion, which reduces the attention abilities in breast cancer patients. PMID:28209975

  3. The Hepatitis Viral Status in Patients With Hepatocellular Carcinoma: a Study of 3843 Patients From Taiwan Liver Cancer Network

    Science.gov (United States)

    Chang, Il-Chi; Huang, Shiu-Feng; Chen, Pei-Jer; Chen, Chi-Ling; Chen, Chao-Long; Wu, Cheng-Chung; Tsai, Cheng-Chung; Lee, Po-Huang; Chen, Miin-Fu; Lee, Chuan-Mo; Yu, Hsien-Chung; Lo, Gin-Ho; Yeh, Chau-Ting; Hong, Chih-Chen; Eng, Hock-Liew; Wang, John; Tseng, Hui-Hwa; Hsiao, Cheng-Hsiang; Wu, Hong-Dar Isaac; Yen, Tseng-Chang; Liaw, Yun-Fan

    2016-01-01

    Abstract Hepatocellular carcinoma (HCC) is the leading cancer death in Taiwan. Chronic viral hepatitis infections have long been considered as the most important risk factors for HCC in Taiwan. The previously published reports were either carried out by individual investigators with small patient numbers or by large endemic studies with limited viral marker data. Through collaboration with 5 medical centers across Taiwan, Taiwan liver cancer network (TLCN) was established in 2005. All participating centers followed a standard protocol to recruit liver cancer patients along with their biosamples and clinical data. In addition, detailed viral marker analysis for hepatitis B virus (HBV) and hepatitis C virus (HCV) were also performed. This study included 3843 HCC patients with available blood samples in TLCN (recruited from November 2005 to April 2011). There were 2153 (56.02%) patients associated with HBV (HBV group); 969 (25.21%) with HCV (HCV group); 310 (8.07%) with both HBV and HCV (HBV+HCV group); and 411 (10.69%) were negative for both HBV and HCV (non-B non-C group). Two hundred two of the 2463 HBV patients (8.20%) were HBsAg(-), but HBV DNA (+). The age, gender, cirrhosis, viral titers, and viral genotypes were all significantly different between the above 4 groups of patients. The median age of the HBV group was the youngest, and the cirrhotic rate was lowest in the non-B non-C group (only 25%). This is the largest detailed viral hepatitis marker study for HCC patients in the English literatures. Our study provided novel data on the interaction of HBV and HCV in the HCC patients and also confirmed that the HCC database of TLCN is highly representative for Taiwan and an important resource for HCC research. PMID:27082566

  4. Network systems biology for targeted cancer therapies

    Institute of Scientific and Technical Information of China (English)

    Ting-Ting Zhou

    2012-01-01

    The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

  5. Differential network analysis in human cancer research.

    Science.gov (United States)

    Gill, Ryan; Datta, Somnath; Datta, Susmita

    2014-01-01

    A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.

  6. Decoding network dynamics in cancer

    DEFF Research Database (Denmark)

    Linding, Rune

    2014-01-01

    models through computational integration of systematic, large-scale, high-dimensional quantitative data sets. I will review our latest advances in methods for exploring phosphorylation networks. In particular I will discuss how the combination of quantitative mass-spectrometry, systems...... in comparative phospho-proteomics and network evolution [Tan et al. Science Signaling 2009, Tan et al. Science 2009, Tan et al. Science 2011]. Finally, I will discuss our most recent work in analyzing genomic sequencing data from NGS studies and how we have developed new powerful algorithms to predict the impact......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...

  7. Sequencing the transcriptional network of androgen receptor in prostate cancer.

    Science.gov (United States)

    Chng, Kern Rei; Cheung, Edwin

    2013-11-01

    The progression of prostate cancer is largely dependent on the activity of the androgen receptor (AR), which in turn, correlates with the net output of the AR transcriptional regulatory network. A detailed and thorough understanding of the AR transcriptional regulatory network is therefore critical in the strategic manipulation of AR activity for the targeted eradication of prostate cancer cells. In this mini-review, we highlight some of the novel and unexpected mechanistic and functional insights of the AR transcriptional network derived from recent targeted sequencing (ChIP-Seq) studies of AR and its coregulatory factors in prostate cancer cells.

  8. TP53 mutations, expression and interaction networks in human cancers.

    Science.gov (United States)

    Wang, Xiaosheng; Sun, Qingrong

    2017-01-03

    Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

  9. The association between active participation in a sports club, physical activity and social network on the development of lung cancer in smokers: a case-control study

    Directory of Open Access Journals (Sweden)

    Schmidt Anna

    2012-01-01

    Full Text Available Abstract Background This study analyses the effect of active participation in a sports club, physical activity and social networks on the development of lung cancer in patients who smoke. Our hypothesis is that study participants who lack social networks and do not actively participate in a sports club are at a greater risk for lung cancer than those who do. Methods Data for the study were taken from the Cologne Smoking Study (CoSmoS, a retrospective case-control study examining potential psychosocial risk factors for the development of lung cancer. Our sample consisted of n = 158 participants who had suffered lung cancer (diagnosis in the patient document and n = 144 control group participants. Both groups had a history of smoking. Data on social networks were collected by asking participants whether they participated in a sports club and about the number of friends and relatives in their social environment. In addition, sociodemographic data (gender, age, education, marital status, residence and religion, physical activity and data on pack years (the cumulative number of cigarettes smoked by an individual, calculated by multiplying the number of cigarettes smoked per day by the number of years the person has smoked divided by 20 were collected to control for potential confounders. Logistic regression was used for the statistical analysis. Results The results reveal that participants who are physically active are at a lower risk of lung cancer than those who are not (adjusted OR = 0.53*; CI = 0.29-0.97. Older age and lower education seem also to be risk factors for the development of lung cancer. The extent of smoking, furthermore, measured by pack years is statistically significant. Active participation in a sports club, number of friends and relatives had no statistically significant influence on the development of the cancer. Conclusions The results of the study suggest that there is a lower risk for physically active participants to develop

  10. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  11. Discovering cancer genes by integrating network and functional properties

    Directory of Open Access Journals (Sweden)

    Davis David P

    2009-09-01

    Full Text Available Abstract Background Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO annotations, to facilitate the identification of cancer genes. Methods Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.

  12. Breast cancer prognosis predicted by nuclear receptor-coregulator networks.

    Science.gov (United States)

    Doan, Tram B; Eriksson, Natalie A; Graham, Dinny; Funder, John W; Simpson, Evan R; Kuczek, Elizabeth S; Clyne, Colin; Leedman, Peter J; Tilley, Wayne D; Fuller, Peter J; Muscat, George E O; Clarke, Christine L

    2014-07-01

    Although molecular signatures based on transcript expression in breast cancer samples have provided new insights into breast cancer classification and prognosis, there are acknowledged limitations in current signatures. To provide rational, pathway-based signatures of disrupted physiology in cancer tissues that may be relevant to prognosis, this study has directly quantitated changed gene expression, between normal breast and cancer tissue, as a basis for signature development. The nuclear receptor (NR) family of transcription factors, and their coregulators, are fundamental regulators of every aspect of metazoan life, and were rigorously quantified in normal breast tissues and ERα positive and ERα negative breast cancers. Coregulator expression was highly correlated with that of selected NR in normal breast, particularly from postmenopausal women. These associations were markedly decreased in breast cancer, and the expression of the majority of coregulators was down-regulated in cancer tissues compared with normal. While in cancer the loss of NR-coregulator associations observed in normal breast was common, a small number of NR (Rev-ERBβ, GR, NOR1, LRH-1 and PGR) acquired new associations with coregulators in cancer tissues. Elevated expression of these NR in cancers was associated with poorer outcome in large clinical cohorts, as well as suggesting the activation of ERα -related, but ERα-independent, pathways in ERα negative cancers. In addition, the combined expression of small numbers of NR and coregulators in breast cancer was identified as a signature predicting outcome in ERα negative breast cancer patients, not linked to proliferation and with predictive power superior to existing signatures containing many more genes. These findings highlight the power of predictive signatures derived from the quantitative determination of altered gene expression between normal breast and breast cancers. Taken together, the findings of this study identify networks

  13. Reprogramming of miRNA networks in cancer and leukemia

    Science.gov (United States)

    Volinia, Stefano; Galasso, Marco; Costinean, Stefan; Tagliavini, Luca; Gamberoni, Giacomo; Drusco, Alessandra; Marchesini, Jlenia; Mascellani, Nicoletta; Sana, Maria Elena; Abu Jarour, Ramzey; Desponts, Caroline; Teitell, Michael; Baffa, Raffaele; Aqeilan, Rami; Iorio, Marilena V.; Taccioli, Cristian; Garzon, Ramiro; Di Leva, Gianpiero; Fabbri, Muller; Catozzi, Marco; Previati, Maurizio; Ambs, Stefan; Palumbo, Tiziana; Garofalo, Michela; Veronese, Angelo; Bottoni, Arianna; Gasparini, Pierluigi; Harris, Curtis C.; Visone, Rosa; Pekarsky, Yuri; de la Chapelle, Albert; Bloomston, Mark; Dillhoff, Mary; Rassenti, Laura Z.; Kipps, Thomas J.; Huebner, Kay; Pichiorri, Flavia; Lenze, Dido; Cairo, Stefano; Buendia, Marie-Annick; Pineau, Pascal; Dejean, Anne; Zanesi, Nicola; Rossi, Simona; Calin, George A.; Liu, Chang-Gong; Palatini, Jeff; Negrini, Massimo; Vecchione, Andrea; Rosenberg, Anne; Croce, Carlo M.

    2010-01-01

    We studied miRNA profiles in 4419 human samples (3312 neoplastic, 1107 nonmalignant), corresponding to 50 normal tissues and 51 cancer types. The complexity of our database enabled us to perform a detailed analysis of microRNA (miRNA) activities. We inferred genetic networks from miRNA expression in normal tissues and cancer. We also built, for the first time, specialized miRNA networks for solid tumors and leukemias. Nonmalignant tissues and cancer networks displayed a change in hubs, the most connected miRNAs. hsa-miR-103/106 were downgraded in cancer, whereas hsa-miR-30 became most prominent. Cancer networks appeared as built from disjointed subnetworks, as opposed to normal tissues. A comparison of these nets allowed us to identify key miRNA cliques in cancer. We also investigated miRNA copy number alterations in 744 cancer samples, at a resolution of 150 kb. Members of miRNA families should be similarly deleted or amplified, since they repress the same cellular targets and are thus expected to have similar impacts on oncogenesis. We correctly identified hsa-miR-17/92 family as amplified and the hsa-miR-143/145 cluster as deleted. Other miRNAs, such as hsa-miR-30 and hsa-miR-204, were found to be physically altered at the DNA copy number level as well. By combining differential expression, genetic networks, and DNA copy number alterations, we confirmed, or discovered, miRNAs with comprehensive roles in cancer. Finally, we experimentally validated the miRNA network with acute lymphocytic leukemia originated in Mir155 transgenic mice. Most of miRNAs deregulated in these transgenic mice were located close to hsa-miR-155 in the cancer network. PMID:20439436

  14. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.

    Science.gov (United States)

    Niida, Atsushi; Imoto, Seiya; Nagasaki, Masao; Yamaguchi, Rui; Miyano, Satoru

    2010-01-01

    Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.

  15. International network of cancer genome projects

    NARCIS (Netherlands)

    Hudson, Thomas J.; Anderson, Warwick; Aretz, Axel; Barker, Anna D.; Bell, Cindy; Bernabe, Rosa R.; Bhan, M. K.; Calvo, Fabien; Eerola, Iiro; Gerhard, Daniela S.; Guttmacher, Alan; Guyer, Mark; Hemsley, Fiona M.; Jennings, Jennifer L.; Kerr, David; Klatt, Peter; Kolar, Patrik; Kusuda, Jun; Lane, David P.; Laplace, Frank; Lu, Youyong; Nettekoven, Gerd; Ozenberger, Brad; Peterson, Jane; Rao, T. S.; Remacle, Jacques; Schafer, Alan J.; Shibata, Tatsuhiro; Stratton, Michael R.; Vockley, Joseph G.; Watanabe, Koichi; Yang, Huanming; Yuen, Matthew M. F.; Knoppers, M.; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn G.; Dyke, Stephanie O. M.; Joly, Yann; Kato, Kazuto; Kennedy, Karen L.; Nicolas, Pilar; Parker, Michael J.; Rial-Sebbag, Emmanuelle; Romeo-Casabona, Carlos M.; Shaw, Kenna M.; Wallace, Susan; Wiesner, Georgia L.; Zeps, Nikolajs; Lichter, Peter; Biankin, Andrew V.; Chabannon, Christian; Chin, Lynda; Clement, Bruno; de Alava, Enrique; Degos, Francoise; Ferguson, Martin L.; Geary, Peter; Hayes, D. Neil; Johns, Amber L.; Nakagawa, Hidewaki; Penny, Robert; Piris, Miguel A.; Sarin, Rajiv; Scarpa, Aldo; Shibata, Tatsuhiro; van de Vijver, Marc; Futreal, P. Andrew; Aburatani, Hiroyuki; Bayes, Monica; Bowtell, David D. L.; Campbell, Peter J.; Estivill, Xavier; Grimmond, Sean M.; Gut, Ivo; Hirst, Martin; Lopez-Otin, Carlos; Majumder, Partha; Marra, Marco; Nakagawa, Hidewaki; Ning, Zemin; Puente, Xose S.; Ruan, Yijun; Shibata, Tatsuhiro; Stratton, Michael R.; Stunnenberg, Hendrik G.; Swerdlow, Harold; Velculescu, Victor E.; Wilson, Richard K.; Xue, Hong H.; Yang, Liu; Spellman, Paul T.; Bader, Gary D.; Boutros, Paul C.; Campbell, Peter J.; Flicek, Paul; Getz, Gad; Guigo, Roderic; Guo, Guangwu; Haussler, David; Heath, Simon; Hubbard, Tim J.; Jiang, Tao; Jones, Steven M.; Li, Qibin; Lopez-Bigas, Nuria; Luo, Ruibang; Pearson, John V.; Puente, Xose S.; Quesada, Victor; Raphael, Benjamin J.; Sander, Chris; Shibata, Tatsuhiro; Speed, Terence P.; Stuart, Joshua M.; Teague, Jon W.; Totoki, Yasushi; Tsunoda, Tatsuhiko; Valencia, Alfonso; Wheeler, David A.; Wu, Honglong; Zhao, Shancen; Zhou, Guangyu; Stein, Lincoln D.; Guigo, Roderic; Hubbard, Tim J.; Joly, Yann; Jones, Steven M.; Lathrop, Mark; Lopez-Bigas, Nuria; Ouellette, B. F. Francis; Spellman, Paul T.; Teague, Jon W.; Thomas, Gilles; Valencia, Alfonso; Yoshida, Teruhiko; Kennedy, Karen L.; Axton, Myles; Dyke, Stephanie O. M.; Futreal, P. Andrew; Gunter, Chris; Guyer, Mark; McPherson, John D.; Miller, Linda J.; Ozenberger, Brad; Kasprzyk, Arek; Zhang, Junjun; Haider, Syed A.; Wang, Jianxin; Yung, Christina K.; Cross, Anthony; Liang, Yong; Gnaneshan, Saravanamuttu; Guberman, Jonathan; Hsu, Jack; Bobrow, Martin; Chalmers, Don R. C.; Hasel, Karl W.; Joly, Yann; Kaan, Terry S. H.; Kennedy, Karen L.; Knoppers, Bartha M.; Lowrance, William W.; Masui, Tohru; Nicolas, Pilar; Rial-Sebbag, Emmanuelle; Rodriguez, Laura Lyman; Vergely, Catherine; Yoshida, Teruhiko; Grimmond, Sean M.; Biankin, Andrew V.; Bowtell, David D. L.; Cloonan, Nicole; Defazio, Anna; Eshleman, James R.; Etemadmoghadam, Dariush; Gardiner, Brooke A.; Kench, James G.; Scarpa, Aldo; Sutherland, Robert L.; Tempero, Margaret A.; Waddell, Nicola J.; Wilson, Peter J.; Gallinger, Steve; Tsao, Ming-Sound; Shaw, Patricia A.; Petersen, Gloria M.; Mukhopadhyay, Debabrata; Chin, Lynda; DePinho, Ronald A.; Thayer, Sarah; Muthuswamy, Lakshmi; Shazand, Kamran; Beck, Timothy; Sam, Michelle; Timms, Lee; Ballin, Vanessa; Lu, Youyong; Ji, Jiafu; Zhang, Xiuqing; Chen, Feng; Hu, Xueda; Zhou, Guangyu; Yang, Qi; Tian, Geng; Zhang, Lianhai; Xing, Xiaofang; Li, Xianghong; Zhu, Zhenggang; Yu, Yingyan; Yu, Jun; Yang, Huanming; Lathrop, Mark; Tost, Joerg; Brennan, Paul; Holcatova, Ivana; Zaridze, David; Brazma, Alvis; Egevad, Lars; Prokhortchouk, Egor; Banks, Rosamonde Elizabeth; Uhlen, Mathias; Cambon-Thomsen, Anne; Viksna, Juris; Ponten, Fredrik; Skryabin, Konstantin; Stratton, Michael R.; Futreal, P. Andrew; Birney, Ewan; Borg, Ake; Borresen-Dale, Anne-Lise; Caldas, Carlos; Foekens, John A.; Martin, Sancha; Reis-Filho, Jorge S.; Richardson, Andrea L.; Sotiriou, Christos; Stunnenberg, Hendrik G.; Thomas, Gilles; van de Vijver, Marc; van't Veer, Laura; Birnbaum, Daniel; Blanche, Helene; Boucher, Pascal; Boyault, Sandrine; Chabannon, Christian; Gut, Ivo; Masson-Jacquemier, Jocelyne D.; Lathrop, Mark; Pauporte, Iris; Pivot, Xavier; Vincent-Salomon, Anne; Tabone, Eric; Theillet, Charles; Thomas, Gilles; Tost, Joerg; Treilleux, Isabelle; Bioulac-Sage, Paulette; Clement, Bruno; Decaens, Thomas; Degos, Francoise; Franco, Dominique; Gut, Ivo; Gut, Marta; Heath, Simon; Lathrop, Mark; Samuel, Didier; Thomas, Gilles; Zucman-Rossi, Jessica; Lichter, Peter; Eils, Roland; Brors, Benedikt; Korbel, Jan O.; Korshunov, Andrey; Landgraf, Pablo; Lehrach, Hans; Pfister, Stefan; Radlwimmer, Bernhard; Reifenberger, Guido; Taylor, Michael D.; von Kalle, Christof; Majumder, Partha P.; Sarin, Rajiv; Scarpa, Aldo; Pederzoli, Paolo; Lawlor, Rita T.; Delledonne, Massimo; Bardelli, Alberto; Biankin, Andrew V.; Grimmond, Sean M.; Gress, Thomas; Klimstra, David; Zamboni, Giuseppe; Shibata, Tatsuhiro; Nakamura, Yusuke; Nakagawa, Hidewaki; Kusuda, Jun; Tsunoda, Tatsuhiko; Miyano, Satoru; Aburatani, Hiroyuki; Kato, Kazuto; Fujimoto, Akihiro; Yoshida, Teruhiko; Campo, Elias; Lopez-Otin, Carlos; Estivill, Xavier; Guigo, Roderic; de Sanjose, Silvia; Piris, Miguel A.; Montserrat, Emili; Gonzalez-Diaz, Marcos; Puente, Xose S.; Jares, Pedro; Valencia, Alfonso; Himmelbaue, Heinz; Quesada, Victor; Bea, Silvia; Stratton, Michael R.; Futreal, P. Andrew; Campbell, Peter J.; Vincent-Salomon, Anne; Richardson, Andrea L.; Reis-Filho, Jorge S.; van de Vijver, Marc; Thomas, Gilles; Masson-Jacquemier, Jocelyne D.; Aparicio, Samuel; Borg, Ake; Borresen-Dale, Anne-Lise; Caldas, Carlos; Foekens, John A.; Stunnenberg, Hendrik G.; van't Veer, Laura; Easton, Douglas F.; Spellman, Paul T.; Martin, Sancha; Chin, Lynda; Collins, Francis S.; Compton, Carolyn C.; Ferguson, Martin L.; Getz, Gad; Gunter, Chris; Guyer, Mark; Hayes, D. Neil; Lander, Eric S.; Ozenberger, Brad; Penny, Robert; Peterson, Jane; Sander, Chris; Speed, Terence P.; Spellman, Paul T.; Wheeler, David A.; Wilson, Richard K.; Chin, Lynda; Knoppers, Bartha M.; Lander, Eric S.; Lichter, Peter; Stratton, Michael R.; Bobrow, Martin; Burke, Wylie; Collins, Francis S.; DePinho, Ronald A.; Easton, Douglas F.; Futreal, P. Andrew; Green, Anthony R.; Guyer, Mark; Hamilton, Stanley R.; Hubbard, Tim J.; Kallioniemi, Olli P.; Kennedy, Karen L.; Ley, Timothy J.; Liu, Edison T.; Lu, Youyong; Majumder, Partha; Marra, Marco; Ozenberger, Brad; Peterson, Jane; Schafer, Alan J.; Spellman, Paul T.; Stunnenberg, Hendrik G.; Wainwright, Brandon J.; Wilson, Richard K.; Yang, Huanming

    2010-01-01

    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic

  16. Radiotherapy for marginally resected, unresectable or recurrent giant cell tumor of the bone: a rare cancer network study

    Directory of Open Access Journals (Sweden)

    Robert C. Miller

    2011-10-01

    Full Text Available The role of radiotherapy for local control of marginally resected, unresectable, and recurrent giant cell tumors of bone (GCToB has not been well defined. The number of patients affected by this rare disease is low. We present a series of 58 patients with biopsy proven GCToB who were treated with radiation therapy. A retrospective review of the role of radiotherapy in the treatment of GCToB was conducted in participating institutions of the Rare Cancer Network. Eligibility criteria consisted of the use of radiotherapy for marginally resected, unresectable, and recurrent GCToB. Fifty-eight patients with biopsy proven GCToB were analyzed from 9 participating North American and European institutions. Forty-five patients had a primary tumor and 13 patients had a recurrent tumor. Median radiation dose was 50 Gy in a median of 25 fractions. Indication for radiation therapy was marginal resection in 33 patients, unresectable tumor in 13 patients, recurrence in 9 patients and palliation in 2 patients. Median tumor size was 7.0 cm. A significant proportion of the tumors involved critical structures. Median follow- up was 8.0 years. Five year local control was 85% . Of the 7 local failures, 3 were treated successfully with salvage surgery. All patients who received palliation achieved symptom relief. Five year overall survival was 94%. None of the patients experienced grade 3 or higher acute toxicity. This study reports a large published experience in the treatment of GCToB with radiotherapy. Radiotherapy can provide excellent local control for incompletely resected, unresectable or recurrent GCToB with acceptable morbidity.

  17. Study Points to Genetic Subtypes of Esophageal Cancer

    Science.gov (United States)

    A Cancer Currents blog post about a study by The Cancer Genome Atlas Research Network that identified distinct genetic and molecular changes in esophageal cancers that could improve their classification and identify potential new treatments.

  18. A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST.

    Directory of Open Access Journals (Sweden)

    Panuwat Trairatphisan

    Full Text Available Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation.In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF signalling pathway in Gastrointestinal Stromal Tumour (GIST. We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion.

  19. Primary Mucosa-Associated Lymphoid Tissue Lymphoma of the Salivary Glands: A Multicenter Rare Cancer Network Study

    Energy Technology Data Exchange (ETDEWEB)

    Anacak, Yavuz, E-mail: yavuz.anacak@ege.edu.tr [Department of Radiation Oncology, Ege University Medical School, Izmir (Turkey); Miller, Robert C. [Department of Radiation Oncology, Mayo Clinic, Rochester, MN (United States); Constantinou, Nikos [Department of Hematology, Theagenion Cancer Center, Thessaloniki (Greece); Mamusa, Angela M. [Division of Hematology, Armando Businco Cancer Center, Cagliari (Italy); Epelbaum, Ron [Department of Oncology, Rambam Medical Center, Haifa (Israel); Li Yexiong [Department of Radiation Oncology, Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing (China); Calduch, Anna Lucas [Servicio de Oncologia Radioterapica, Institut Catala d' Oncologia, Barcelona (Spain); Kowalczyk, Anna [Department of Oncology and Radiotherapy, Medical University of Gdansk (Poland); Weber, Damien C. [Department of Radiation Oncology, Geneva University Hospital (Switzerland); Kadish, Sidney P. [Department of Radiation Oncology, University of Massachusetts Medical School/Center, North Worcester, MA (United States); Bese, Nuran [Department of Radiation Oncology, Istanbul University Cerrahpasa Medical School, Istanbul (Turkey); Poortmans, Philip [Institute Verbeeten, Tilburg (Netherlands); Kamer, Serra [Department of Radiation Oncology, Ege University Medical School, Izmir (Turkey); Ozsahin, Mahmut [Department of Radiation Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)

    2012-01-01

    Purpose: Involvement of salivary glands with mucosa-associated lymphoid tissue (MALT) lymphoma is rare. This retrospective study was performed to assess the clinical profile, treatment outcome, and prognostic factors of MALT lymphoma of the salivary glands. Methods and Materials: Thirteen member centers of the Rare Cancer Network from 10 countries participated, providing data on 63 patients. The median age was 58 years; 47 patients were female and 16 were male. The parotid glands were involved in 49 cases, submandibular in 15, and minor glands in 3. Multiple glands were involved in 9 patients. Staging was as follows: IE in 34, IIE in 12, IIIE in 2, and IV in 15 patients. Results: Surgery (S) alone was performed in 9, radiotherapy (RT) alone in 8, and chemotherapy (CT) alone in 4 patients. Forty-one patients received combined modality treatment (S + RT in 23, S + CT in 8, RT + CT in 4, and all three modalities in 6 patients). No active treatment was given in one case. After initial treatment there was no tumor in 57 patients and residual tumor in 5. Tumor progression was observed in 23 (36.5%) (local in 1, other salivary glands in 10, lymph nodes in 11, and elsewhere in 6). Five patients died of disease progression and the other 5 of other causes. The 5-year disease-free survival, disease-specific survival, and overall survival were 54.4%, 93.2%, and 81.7%, respectively. Factors influencing disease-free survival were use of RT, stage, and residual tumor (p < 0.01). Factors influencing disease-specific survival were stage, recurrence, and residual tumor (p < 0.01). Conclusions: To our knowledge, this report represents the largest series of MALT lymphomas of the salivary glands published to date. This disease may involve all salivary glands either initially or subsequently in 30% of patients. Recurrences may occur in up to 35% of patients at 5 years; however, survival is not affected. Radiotherapy is the only treatment modality that improves disease-free survival.

  20. Small Cell Carcinoma of the Urinary Bladder: A Retrospective, Multicenter Rare Cancer Network Study of 107 Patients

    Energy Technology Data Exchange (ETDEWEB)

    Pasquier, David, E-mail: d-pasquier@o-lambret.fr [Academic Radiation Oncology Department, Centre Oscar Lambret, Lille (France); Barney, Brandon [Mayo Clinic, Rochester, Minnesota (United States); Sundar, Santhanam [Department of Oncology, Nottingham University Hospitals National Health Service Trust, Nottingham (United Kingdom); Poortmans, Philip [Department of Radiation Oncology, Radboud university medical center, Nijmegen (Netherlands); Villa, Salvador [Radiation Oncology, Catalan Institute of Oncology, H. Universitari Germans Trías, Badalona, Barcelona (Spain); Nasrallah, Haitam [Division of Oncology, Rambam Health Care Campus and Faculty of Medicine, Technion-Israel Institute of Technology, Haifa (Israel); Boujelbene, Noureddine [Department of Radiation Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Ghadjar, Pirus [Department of Radiation Oncology, Bern University Hospital, Bern (Switzerland); Lassen-Ramshad, Yasmin [Department of Oncology, Aarhus University Hospital, Aarhus (Denmark); Senkus, Elżbieta [Department of Oncology and Radiotherapy, Medical University of Gdansk, Gdansk (Poland); Oar, Andrew [Genesis Cancer Care, Southport (Australia); Roelandts, Martine [Institut Jules Bordet, Brussels (Belgium); Amichetti, Maurizio [Provincial Agency for Proton Therapy, Trento (Italy); Vees, Hansjoerg [Department of Radiation Oncology, Hopital de Sion, Sion (Switzerland); Zilli, Thomas [Department of Radiation Oncology, Geneva University Hospital, Geneva (Switzerland); Ozsahin, Mahmut [Department of Radiation Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)

    2015-07-15

    Purpose: Small cell carcinomas of the bladder (SCCB) account for fewer than 1% of all urinary bladder tumors. There is no consensus regarding the optimal treatment for SCCB. Methods and Materials: Fifteen academic Rare Cancer Network medical centers contributed SCCB cases. The eligibility criteria were as follows: pure or mixed SCC; local, locoregional, and metastatic stages; and age ≥18 years. The overall survival (OS) and disease-free survival (DFS) were calculated from the date of diagnosis according to the Kaplan-Meier method. The log-rank and Wilcoxon tests were used to analyze survival as functions of clinical and therapeutic factors. Results: The study included 107 patients (mean [±standard deviation, SD] age, 69.6 [±10.6] years; mean follow-up time, 4.4 years) with primary bladder SCC, with 66% of these patients having pure SCC. Seventy-two percent and 12% of the patients presented with T2-4N0M0 and T2-4N1-3M0 stages, respectively, and 16% presented with synchronous metastases. The most frequent curative treatments were radical surgery and chemotherapy, sequential chemotherapy and radiation therapy, and radical surgery alone. The median (interquartile range, IQR) OS and DFS times were 12.9 months (IQR, 7-32 months) and 9 months (IQR, 5-23 months), respectively. The metastatic, T2-4N0M0, and T2-4N1-3M0 groups differed significantly (P=.001) in terms of median OS and DFS. In a multivariate analysis, impaired creatinine clearance (OS and DFS), clinical stage (OS and DFS), a Karnofsky performance status <80 (OS), and pure SCC histology (OS) were independent and significant adverse prognostic factors. In the patients with nonmetastatic disease, the type of treatment (ie radical surgery with or without adjuvant chemotherapy vs conservative treatment) did not significantly influence OS or DFS (P=.7). Conclusions: The prognosis for SCCB remains poor. The finding that radical cystectomy did not influence DFS or OS in the patients with nonmetastatic disease

  1. Drawbacks and benefits associated with inter-organizational collaboration along the discovery-development-delivery continuum: a cancer research network case study

    Directory of Open Access Journals (Sweden)

    Harris Jenine K

    2012-07-01

    Full Text Available Abstract Background The scientific process around cancer research begins with scientific discovery, followed by development of interventions, and finally delivery of needed interventions to people with cancer. Numerous studies have identified substantial gaps between discovery and delivery in health research. Team science has been identified as a possible solution for closing the discovery to delivery gap; however, little is known about effective ways of collaborating within teams and across organizations. The purpose of this study was to determine benefits and drawbacks associated with organizational collaboration across the discovery-development-delivery research continuum. Methods Representatives of organizations working on cancer research across a state answered a survey about how they collaborated with other cancer research organizations in the state and what benefits and drawbacks they experienced while collaborating. We used exponential random graph modeling to determine the association between these benefits and drawbacks and the presence of a collaboration tie between any two network members. Results Different drawbacks and benefits were associated with discovery, development, and delivery collaborations. The only consistent association across all three was with the drawback of difficulty due to geographic differences, which was negatively associated with collaboration, indicating that those organizations that had collaborated were less likely to perceive a barrier related to geography. The benefit, enhanced access to other knowledge, was positive and significant in the development and delivery networks, indicating that collaborating organizations viewed improved knowledge exchange as a benefit of collaboration. ‘Acquisition of additional funding or other resources’ and ‘development of new tools and methods’ were negatively significantly related to collaboration in these networks. So, although improved knowledge access was an

  2. Networked Microgrids Scoping Study

    Energy Technology Data Exchange (ETDEWEB)

    Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Dobriansky, Larisa [General MicroGrids, San Diego, CA (United States); Glover, Steve [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Liu, Chen-Ching [Washington State Univ., Pullman, WA (United States); Looney, Patrick [Brookhaven National Lab. (BNL), Upton, NY (United States); Mashayekh, Salman [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pratt, Annabelle [National Renewable Energy Lab. (NREL), Golden, CO (United States); Schneider, Kevin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Starke, Michael [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wang, Jianhui [Argonne National Lab. (ANL), Argonne, IL (United States); Yue, Meng [Brookhaven National Lab. (BNL), Upton, NY (United States)

    2016-12-05

    Much like individual microgrids, the range of opportunities and potential architectures of networked microgrids is very diverse. The goals of this scoping study are to provide an early assessment of research and development needs by examining the benefits of, risks created by, and risks to networked microgrids. At this time there are very few, if any, examples of deployed microgrid networks. In addition, there are very few tools to simulate or otherwise analyze the behavior of networked microgrids. In this setting, it is very difficult to evaluate networked microgrids systematically or quantitatively. At this early stage, this study is relying on inputs, estimations, and literature reviews by subject matter experts who are engaged in individual microgrid research and development projects, i.e., the authors of this study The initial step of the study gathered input about the potential opportunities provided by networked microgrids from these subject matter experts. These opportunities were divided between the subject matter experts for further review. Part 2 of this study is comprised of these reviews. Part 1 of this study is a summary of the benefits and risks identified in the reviews in Part 2 and synthesis of the research needs required to enable networked microgrids.

  3. Transcriptional master regulator analysis in breast cancer genetic networks.

    Science.gov (United States)

    Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2015-12-01

    Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

  4. Transcriptional networks inferred from molecular signatures of breast cancer.

    Science.gov (United States)

    Tongbai, Ron; Idelman, Gila; Nordgard, Silje H; Cui, Wenwu; Jacobs, Jonathan L; Haggerty, Cynthia M; Chanock, Stephen J; Børresen-Dale, Anne-Lise; Livingston, Gary; Shaunessy, Patrick; Chiang, Chih-Hung; Kristensen, Vessela N; Bilke, Sven; Gardner, Kevin

    2008-02-01

    Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-kappaB in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.

  5. Social Networks Across Common Cancer Types: The Evidence, Gaps, and Areas of Potential Impact.

    Science.gov (United States)

    Rice, L J; Halbert, C H

    2017-01-01

    Although the association between social context and health has been demonstrated previously, much less is known about network interactions by gender, race/ethnicity, and sociodemographic characteristics. Given the variability in cancer outcomes among groups, research on these relationships may have important implications for addressing cancer health disparities. We examined the literature on social networks and cancer across the cancer continuum among adults. Relevant studies (N=16) were identified using two common databases: PubMed and Google Scholar. Most studies used a prospective cohort study design (n=9), included women only (n=11), and were located in the United States (n=14). Seventy-five percent of the studies reviewed used a validated scale or validated items to measure social networks (n=12). Only one study examined social network differences by race, 57.1% (n=8) focused on breast cancer alone, 14.3% (n=2) explored colorectal cancer or multiple cancers simultaneously, and 7.1% (n=1) only prostate cancer. More than half of the studies included multiple ethnicities in the sample, while one study included only low-income subjects. Despite findings of associations between social networks and cancer survival, risk, and screening, none of the studies utilized social networks as a mechanism for reducing health disparities; however, such an approach has been utilized for infectious disease control. Social networks and the support provided within these networks have important implications for health behaviors and ultimately cancer disparities. This review serves as the first step toward dialog on social networks as a missing component in the social determinants of cancer disparities literature that could move the needle upstream to target adverse cancer outcomes among vulnerable populations.

  6. Navigating cancer network attractors for tumor-specific therapy

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin; Erler, Janine Terra

    2012-01-01

    Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach...... these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper...... understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....

  7. Transcriptional network of androgen receptor in prostate cancer progression.

    Science.gov (United States)

    Takayama, Ken-ichi; Inoue, Satoshi

    2013-08-01

    The androgen receptor belongs to the nuclear receptor superfamily and functions as a ligand-dependent transcription factor. It binds to the androgen responsive element and recruits coregulatory factors to modulate gene transcription. In addition, the androgen receptor interacts with other transcription factors, such as forkhead box A1, and other oncogenic signaling pathway molecules that bind deoxyribonucleic acid and regulate transcription. Androgen receptor signaling plays an important role in the development of prostate cancer. Prostate cancer cells proliferate in an androgen-dependent manner, and androgen receptor blockade is effective in prostate cancer therapy. However, patients often progress to castration-resistant prostate cancer with elevated androgen receptor expression and hypersensitivity to androgen. Recently, comprehensive analysis tools, such as complementary DNA microarray, chromatin immunoprecipitation-on-chip and chromatin immunoprecipitation-sequence, have described the androgen-mediated diverse transcriptional program and gene networks in prostate cancer. Furthermore, functional and clinical studies have shown that some of the androgen receptor-regulated genes could be prognostic markers and potential therapeutic targets for the treatment of prostate cancer, particularly castration-resistant prostate cancer. Thus, identifying androgen receptor downstream signaling events and investigating the regulation of androgen receptor activity is critical for understanding the mechanism of carcinogenesis and progression to castration-resistant prostate cancer.

  8. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

    Science.gov (United States)

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E.; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer. PMID:27920729

  9. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

    Directory of Open Access Journals (Sweden)

    Guillermo de Anda-Jáuregui

    2016-11-01

    Full Text Available Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes.In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples is also inferred and analyzed.Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e. CNR2 or to a particular subtype (such as LCK. Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance.With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

  10. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes.

    Science.gov (United States)

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

  11. Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

    Science.gov (United States)

    Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

    2010-01-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

  12. Evolution and Controllability of Cancer Networks: A Boolean Perspective.

    Science.gov (United States)

    Srihari, Sriganesh; Raman, Venkatesh; Leong, Hon Wai; Ragan, Mark A

    2014-01-01

    Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of

  13. Altered small-world properties of gray matter networks in breast cancer

    Directory of Open Access Journals (Sweden)

    Hosseini S M

    2012-05-01

    Full Text Available Abstract Background Breast cancer survivors, particularly those treated with chemotherapy, are at significantly increased risk for long-term cognitive and neurobiologic impairments. These deficits tend to involve skills that are subserved by distributed brain networks. Additionally, neuroimaging studies have shown a diffuse pattern of brain structure changes in chemotherapy-treated breast cancer survivors that might impact large-scale brain networks. Methods We therefore applied graph theoretical analysis to compare the gray matter structural networks of female breast cancer survivors with a history of chemotherapy treatment and healthy age and education matched female controls. Results Results revealed reduced clustering coefficient and small-world index in the brain network of the breast cancer patients across a range of network densities. In addition, the network of the breast cancer group had less highly interactive nodes and reduced degree/centrality in the frontotemporal regions compared to controls, which may help explain the common impairments of memory and executive functioning among these patients. Conclusions These results suggest that breast cancer and chemotherapy may decrease regional connectivity as well as global network organization and integration, reducing efficiency of the network. To our knowledge, this is the first report of altered large-scale brain networks associated with breast cancer and chemotherapy.

  14. The Essential Role of Radiotherapy in the Treatment of Merkel Cell Carcinoma: A Study From the Rare Cancer Network

    Energy Technology Data Exchange (ETDEWEB)

    Ghadjar, Pirus, E-mail: pirus.ghadjar@insel.ch [Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern (Switzerland); Kaanders, Johannes H. [Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, Institute of Oncology (Netherlands); Poortmans, Philipp [Department of Radiation Oncology, Institute Verbeeten, Tilburg (Netherlands); Zaucha, Renata [Department of Oncology and Radiotherapy, Medical University, Gdansk (Poland); Krengli, Marco [Department of Radiotherapy, University Hospital Maggiore della Carita, Novara (Italy); Lagrange, Jean L. [Service de Radiotherapie, Hopital Henri-Mondor, Creteil (France); Oezsoy, Orhan [Department of Radiation Oncology, CHCVs-RSV, Sion (Switzerland); Nguyen, Tan D. [Department of Radiation Oncology, Institut Jean Godinot, Reims (France); Miralbell, Raymond [Department of Radiation Oncology, Hopitaux Universitaires de Geneve, Geneva (Switzerland); Baize, Adele [Department de Radio-Oncologie, Institut Jules Bordet, Bruxelles (Belgium); Boujelbene, Noureddine [Department of Radiation Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Collen, Timothy [Department of Radiation Oncology, Kantonsspital St. Gallen (Switzerland); Scandolaro, Luciano [Radioterapia, Azienda Ospedale Sant' Anna, Como (Italy); Untereiner, Michel [Centre Francois Baclesse, Luxembourg (Luxembourg); Goldberg, Hadassah [Oncology Departement, Rambam Medical Center, Haifa (Israel); Pesce, Gianfranco A. [Department of Radiation Oncology, Oncology Institute of Southern Switzerland, Opedale San Giovanni, Bellinzona (Switzerland); Anacak, Yavuz [Department of Radiation Oncology, EGE University, Izmir (Turkey); Friedrich, Esther E.; Aebersold, Daniel M. [Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern (Switzerland); Beer, Karl T. [Radio Onkologiezentrum Biel (Switzerland)

    2011-11-15

    Purpose: To evaluate the role of postoperative radiotherapy (RT) in Merkel cell carcinoma (MCC). Methods and Materials: A retrospective multicenter study was performed in 180 patients with MCC treated between February 1988 and September 2009. Patients who had had surgery alone were compared with patients who received surgery and postoperative RT or radical RT. Local relapse-free survival (LRFS), regional relapse-free survival (RRFS), and distant metastasis-free survival (DMFS) rates were assessed together with disease-free survival (DFS), cancer-specific survival (CSS), and overall survival (OS) rates. Results: Seventy-nine patients were male and 101 patients were female, and the median age was 73 years old (range, 38-93 years). The majority of patients had localized disease (n = 146), and the remaining patients had regional lymph node metastasis (n = 34). Forty-nine patients underwent surgery for the primary tumor without postoperative RT to the primary site; the other 131 patients received surgery for the primary tumor, followed by postoperative RT (n = 118) or a biopsy of the primary tumor followed by radical RT (n = 13). Median follow-up was 5 years (range, 0.2-16.5 years). Patients in the RT group had improved LRFS (93% vs. 64%; p < 0.001), RRFS (76% vs. 27%; p < 0.001), DMFS (70% vs. 42%; p = 0.01), DFS (59% vs. 4%; p < 0.001), and CSS (65% vs. 49%; p = 0.03) rates compared to patients who underwent surgery for the primary tumor alone; LRFS, RRFS, DMFS, and DFS rates remained significant with multivariable Cox regression analysis. However OS was not significantly improved by postoperative RT (56% vs. 46%; p = 0.2). Conclusions: After multivariable analysis, postoperative RT was associated with improved outcome and seems to be an important component in the multimodality treatment of MCC.

  15. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Yun-Xia Zhang

    2016-01-01

    Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

  16. Case Studies - Cervical Cancer

    Centers for Disease Control (CDC) Podcasts

    2010-10-15

    Dr. Alan Waxman, a professor of obstetrics and gynecology at the University of New Mexico and chair of the American College of Obstetricians and Gynecologists (ACOG) committee for the underserved, talks about several case studies for cervical cancer screening and management.  Created: 10/15/2010 by National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), Division of Cancer Prevention and Control (DCPC).   Date Released: 6/9/2010.

  17. A Medical Center Network for Optimized Lung Cancer Biospecimen Banking

    Science.gov (United States)

    2014-10-01

    1 Award Number: W81XWH-10-1-0818 TITLE: A Medical Center Network for Optimized Lung Cancer Biospecimen Banking PRINCIPAL...Biospecimen Resource Network: A medical center network for optimized lung cancer biospecimen banking 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-10-1-0818... audit sampling the source forms of 15 randomly selected patients from each Resource Site. The purpose of the audit was make certain required clinical

  18. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    Science.gov (United States)

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  19. A cloud-based data network approach for translational cancer research.

    Science.gov (United States)

    Xing, Wei; Tsoumakos, Dimitrios; Ghanem, Moustafa

    2015-01-01

    We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.

  20. A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.

    Science.gov (United States)

    Jacobson, Rebecca S; Becich, Michael J; Bollag, Roni J; Chavan, Girish; Corrigan, Julia; Dhir, Rajiv; Feldman, Michael D; Gaudioso, Carmelo; Legowski, Elizabeth; Maihle, Nita J; Mitchell, Kevin; Murphy, Monica; Sakthivel, Mayurapriyan; Tseytlin, Eugene; Weaver, JoEllen

    2015-12-15

    Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network.

  1. A computational model for cancer growth by using complex networks

    Science.gov (United States)

    Galvão, Viviane; Miranda, José G. V.

    2008-09-01

    In this work we propose a computational model to investigate the proliferation of cancerous cell by using complex networks. In our model the network represents the structure of available space in the cancer propagation. The computational scheme considers a cancerous cell randomly included in the complex network. When the system evolves the cells can assume three states: proliferative, non-proliferative, and necrotic. Our results were compared with experimental data obtained from three human lung carcinoma cell lines. The computational simulations show that the cancerous cells have a Gompertzian growth. Also, our model simulates the formation of necrosis, increase of density, and resources diffusion to regions of lower nutrient concentration. We obtain that the cancer growth is very similar in random and small-world networks. On the other hand, the topological structure of the small-world network is more affected. The scale-free network has the largest rates of cancer growth due to hub formation. Finally, our results indicate that for different average degrees the rate of cancer growth is related to the available space in the network.

  2. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    Science.gov (United States)

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  3. History of the Rare Cancer Network and past research

    Directory of Open Access Journals (Sweden)

    René-Olivier Mirimanoff

    2014-08-01

    Full Text Available Approximately, twenty years ago, the Rare Cancer Network (RCN was formed in Lausanne, Switzerland, to support the study of rare malignancies. The RCN has grown over the years and now includes 130 investigators from twenty-four nations on six continents. The network held its first international symposium in Nice, France, on March 21-22, 2014. The proceedings of that meeting are presented in two companion papers. This manuscript reviews the history of the growth of the RCN and contains the abstracts of fourteen oral presentations made at the meeting of prior RCN studies. From 1993 to 2014, 74 RCN studies have been initiated, of which 54 were completed, 10 are in progress or under analysis, and 9 were stopped due to poor accrual. Forty-four peer reviewed publications have been written on behalf of the RCN.

  4. The Rare Cancer Network: achievements from 1993 to 2012

    Directory of Open Access Journals (Sweden)

    Kenneth Chang

    2012-06-01

    Full Text Available The Rare Cancer Network (RCN, founded in 1993, performs research involving rare tumors that are not common enough to be the focus of prospective study. Over 55 studies have either been completed or are in progress. The aim of the paper is to present an overview of the 30 studies done through the RCN to date, organized by disease site. Five studies focus on breast pathology, including sarcoma, lymphoma, phyllodes tumor, adenoid cystic carcinoma, and ductal carcinoma in situ in young women. Three studies on prostate cancer address prostatic small cell carcinoma and adenocarcinoma of young and elderly patients. Six studies on head and neck cancers include orbital and intraocular lymphoma, mucosal melanoma, pediatric nasopharyngeal carcinoma, olfactory neuroblastoma, and mucosa-associated lymphoid tissue lymphoma of the salivary glands. There were 4 central nervous system studies on patients with cerebellar glioblastoma multiforme, atypical and malignant meningioma, spinal epidural lymphoma and myxopapillary ependymoma. Outside of these disease sites, there is a wide variety of other studies on tumors ranging from uterine leiomyosarcoma to giant cell tumors of the bone. The studies done by the RCN represent a wide range of rare pathologies that were previously only studied in small series or case reports. With further growth of the RCN and collaboration between members our ability to analyze rare tumors will increase and result in better understanding of their behavior and ultimately help direct research that may improve patient outcomes.

  5. Gene Expression Correlation for Cancer Diagnosis: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Binbing Ling

    2014-01-01

    Full Text Available Poor prognosis for late-stage, high-grade, and recurrent cancers has been motivating cancer researchers to search for more efficient biomarkers to identify the onset of cancer. Recent advances in constructing and dynamically analyzing biomolecular networks for different types of cancer have provided a promising novel strategy to detect tumorigenesis and metastasis. The observation of different biomolecular networks associated with normal and cancerous states led us to hypothesize that correlations for gene expressions could serve as valid indicators of early cancer development. In this pilot study, we tested our hypothesis by examining whether the mRNA expressions of three randomly selected cancer-related genes PIK3C3, PIM3, and PTEN were correlated during cancer progression and the correlation coefficients could be used for cancer diagnosis. Strong correlations (0.68≤r≤1.0 were observed between PIK3C3 and PIM3 in breast cancer, between PIK3C3 and PTEN in breast and ovary cancers, and between PIM3 and PTEN in breast, kidney, liver, and thyroid cancers during disease progression, implicating that the correlations for cancer network gene expressions could serve as a supplement to current clinical biomarkers, such as cancer antigens, for early cancer diagnosis.

  6. Identifying module biomarkers from gastric cancer by differential correlation network

    Directory of Open Access Journals (Sweden)

    Liu X

    2016-09-01

    Full Text Available Xiaoping Liu,1–3,* Xiao Chang1,3,* 1College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People’s Republic of China; 2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China; 3Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan *These authors contributed equally to this work Abstract: Gastric cancer (stomach cancer is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. Keywords: biomarkers, gastric cancer, stomach cancer, differential network

  7. MicroRNA and transcription factor mediated regulatory network for ovarian cancer: regulatory network of ovarian cancer.

    Science.gov (United States)

    Ying, Huanchun; Lv, Jing; Ying, Tianshu; Li, Jun; Yang, Qing; Ma, Yuan

    2013-10-01

    A better understanding on the regulatory interactions of microRNA (miRNA) target genes and transcription factor (TF) target genes in ovarian cancer may be conducive for developing early diagnosis strategy. Thus, gene expression data and miRNA expression data were downloaded from The Cancer Genome Atlas in this study. Differentially expressed genes and miRNAs were selected out with t test, and Gene Ontology enrichment analysis was performed with DAVID tools. Regulatory interactions were retrieved from miRTarBase, TRED, and TRANSFAC, and then networks for miRNA target genes and TF target genes were constructed to globally present the mechanisms. As a result, a total of 1,939 differentially expressed genes were identified, and they were enriched in 28 functions, among which cell cycle was affected to the most degree. Besides, 213 differentially expressed miRNAs were identified. Two regulatory networks for miRNA target genes and TF target genes were established and then both were combined, in which E2F transcription factor 1, cyclin-dependent kinase inhibitor 1A, cyclin E1, and miR-16 were the hub genes. These genes may be potential biomarkers for ovarian cancer.

  8. Cancer talk on twitter: community structure and information sources in breast and prostate cancer social networks.

    Science.gov (United States)

    Himelboim, Itai; Han, Jeong Yeob

    2014-01-01

    This study suggests taking a social networks theoretical approach to predict and explain patterns of information exchange among Twitter prostate and breast cancer communities. The authors collected profiles and following relationship data about users who posted messages about either cancer over 1 composite week. Using social network analysis, the authors identified the main clusters of interconnected users and their most followed hubs (i.e., information sources sought). Findings suggest that users who populated the persistent-across-time core cancer communities created dense clusters, an indication of taking advantage of the technology to form relationships with one another in ways that traditional one-to-many communication technologies cannot support. The major information sources sought were very specific to the community health interest and were grassroots oriented (e.g., a blog about prostate cancer treatments). Accounts associated with health organizations and news media, despite their focus on health, did not play a role in these core health communities. Methodological and practical implications for researchers and health campaigners are discussed.

  9. MicroRNA regulation network in colorectal cancer metastasis

    Institute of Scientific and Technical Information of China (English)

    Jiao-Jiao; Zhou; Shu; Zheng; Li-Feng; Sun; Lei; Zheng

    2014-01-01

    Colorectal cancer is the third most common cancer worldwide. Metastasis is a major cause of colorectal cancer-related death. Mechanisms of metastasis remain largely obscure. MicroRNA is one of the most important epigenetic regulators by targeting mRNAs posttranscriptionally. Accumulated evidence has supported its significant role in the metastasis of colorectal cancer, including epithelial-mesenchymal transition and angiogenesis. Dissecting microRNAome potentially identifies specific microRNAs as biomarkers of colorectal cancer metastasis. Better understanding of the complex network of microRNAs in colorectal cancer metastasis provide new insights in the biological process of metastasis and in the potential targets for colorectal cancer therapies and for diagnosis of recurrent and metastatic colorectal cancer.

  10. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  11. A SOCIAL NETWORK ANALYSIS APPROACH TO UNDERSTAND CHANGES IN A CANCER DISPARITIES COMMUNITY PARTNERSHIP NETWORK.

    Science.gov (United States)

    Luque, John S; Tyson, Dinorah Martinez; Bynum, Shalanda A; Noel-Thomas, Shalewa; Wells, Kristen J; Vadaparampil, Susan T; Gwede, Clement K; Meade, Cathy D

    2011-11-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005-10) by the National Cancer Institute's Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial-ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles-in education, training, and research-over a span of three years (2007-09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community-academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition building efforts. A

  12. Campus network security model study

    Science.gov (United States)

    Zhang, Yong-ku; Song, Li-ren

    2011-12-01

    Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

  13. Radiology Network (ACRIN) - Cancer Imaging Program

    Science.gov (United States)

    ACRIN is funded to improve the quality and utility of imaging in cancer research and cancer care through expert, multi-institutional clinical evaluation of discoveries and technological innovations relevant to imaging science as applied in clinical oncology.

  14. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

    Science.gov (United States)

    Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

    2010-05-01

    Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

  15. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  16. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

    Directory of Open Access Journals (Sweden)

    Junyi Li

    2016-01-01

    Full Text Available With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA based on gene coexpression network (GCN increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

  17. Artificial Neural Network Analysis in Preclinical Breast Cancer

    Directory of Open Access Journals (Sweden)

    Gholamreza Motalleb

    2013-01-01

    Full Text Available Objective: In this study, artificial neural network (ANN analysis of virotherapy in preclinical breast cancer was investigated.Materials and Methods: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP and Levenberg-Marquardt (LM, were used to train ANN.Results: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2 between the actual and predicted values was determined as 0.897118 for all data.Conclusion: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week.

  18. Effects of tomato- and soy-rich diets on the IGF-I hormonal network: a crossover study of postmenopausal women at high risk for breast cancer.

    Science.gov (United States)

    McLaughlin, John M; Olivo-Marston, Susan; Vitolins, Mara Z; Bittoni, Marisa; Reeves, Katherine W; Degraffinreid, Cecilia R; Schwartz, Steven J; Clinton, Steven K; Paskett, Electra D

    2011-05-01

    To determine whether dietary modifications with tomato products and/or a soy supplement affected circulating levels of insulin-like growth factor (IGF)-1 and other markers of cell signaling in postmenopausal women at risk for developing breast cancer. Eligible and consented postmenopausal women at high risk for developing breast cancer were enrolled in a 26-week, two-arm (tomato and soy, 10 weeks each) longitudinal dietary intervention study in which each woman served as her own control. Changes in biochemical endpoints including IGF-I, IGF-binding protein (IGFBP)-3, estradiol, sex hormone-binding globulin (SHBG), C-peptide, and insulin were measured for each intervention arm. Carotenoid and isoflavone levels were measured to assess adherence. Significant increases in carotenoid and isoflavone levels during the tomato and soy study arms, respectively, suggested that women were adherent to both arms of the intervention. The tomato-rich diet had little effect on cell-signaling biomarkers previously associated with breast cancer risk. However, results of the soy intervention showed that concentrations of IGF-I and IGFBP-3 increased by 21.6 and 154.7 μmol/L, respectively (P = 0.001 for both) and SHBG decreased by 5.4 μmol/L (P protein intake may lead to small, but significant, increases in IGF-I and IGFBP-3. Soy consumption also led to a significant decrease in SHBG, which has been hypothesized to promote, rather than prevent, cancer growth. Previous epidemiologic studies, however, have confirmed protective effect of soy on breast cancer. Additional investigation about the effect of soy on breast cancer risk and its mechanism of action is warranted.

  19. Cell cycle-dependent gene networks relevant to cancer

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The analysis of sophisticated interplays between cell cycle-dependent genes in a disease condition is one of the largely unexplored areas in modern tumor biology research. Many cell cycle-dependent genes are either oncogenes or suppressor genes, or are closely asso- ciated with the transition of a cell cycle. However, it is unclear how the complicated relationships between these cell cycle-dependent genes are, especially in cancers. Here, we sought to identify significant expression relationships between cell cycle-dependent genes by analyzing a HeLa microarray dataset using a local alignment algorithm and constructed a gene transcriptional network specific to the cancer by assembling these newly identified gene-gene relationships. We further characterized this global network by partitioning the whole network into several cell cycle phase-specific sub-networks. All generated networks exhibited the power-law node-degree dis- tribution, and the average clustering coefficients of these networks were remarkably higher than those of pure scale-free networks, indi- cating a property of hierarchical modularity. Based on the known protein-protein interactions and Gene Ontology annotation data, the proteins encoded by cell cycle-dependent interacting genes tended to share the same biological functions or to be involved in the same biological processes, rather than interacting by physical means. Finally, we identified the hub genes related to cancer based on the topo- logical importance that maintain the basic structure of cell cycle-dependent gene networks.

  20. Studying Dynamics in Business Networks

    DEFF Research Database (Denmark)

    Andersen, Poul Houman; Anderson, Helen; Havila, Virpi;

    1998-01-01

    This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland...

  1. Networked Microgrids Scoping Study

    Energy Technology Data Exchange (ETDEWEB)

    Starke, Michael R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-10-01

    While the utilization of a microgrid for local power reliability during grid outage and emergencies is a well-known benefit, the integration of microgrids with the broader electrical distribution system will allow for seamless interaction with distribution system operations, contributing to resource and economic optimization, enhanced reliability and resiliency, and improved power quality. By virtue of integration with the distribution system, multiple microgrids should be networked and collectively known as networked microgrids. As a follow-up to the work conducted by Oak Ridge National Laboratory on a microgrid controller [the Complete System-level Efficient and Interoperable Solution for Microgrid Integrated Controls (CSEISMIC)], the main goal of this work is to identify the next steps for bringing microgrid research to the utility industry, particularly as a resource for enhancing efficiency, reliability, and resilience. Various R&D needs for the integration of microgrids into the distribution system have been proposed, including interconnection types, communications, control architectures, quantification of benefits, functional requirements, and various operational issues.

  2. Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence.

    Science.gov (United States)

    Delfino, Kristin R; Rodriguez-Zas, Sandra L

    2013-01-01

    The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs), transcription factors (TFs), and target genes. A novel approach that integrates multivariate survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. Three miRNAs (hsa-miR-16, hsa-miR-22*, and ebv-miR-BHRF1-2*) were associated with both ovarian cancer survival and recurrence and 27 miRNAs were associated with either one hazard. Two miRNAs (hsa-miR-521 and hsa-miR-497) were cohort-dependent, while 28 were cohort-independent. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value cancer survival and recurrence, respectively. Functional analysis highlighted the association between cellular and nucleotide metabolic processes and ovarian cancer. The more direct connections and higher centrality of the miRNAs, TFs and target genes in the survival network studied suggest that network-based approaches to prognosticate or predict ovarian cancer survival may be more effective than those for ovarian cancer recurrence. This study demonstrated the feasibility to infer reliable miRNA-TF-target gene networks associated with survival and recurrence of ovarian cancer based on the simultaneous analysis of co-expression profiles and consideration of the clinical characteristics of the patients.

  3. Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence.

    Directory of Open Access Journals (Sweden)

    Kristin R Delfino

    Full Text Available The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs, transcription factors (TFs, and target genes. A novel approach that integrates multivariate survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. Three miRNAs (hsa-miR-16, hsa-miR-22*, and ebv-miR-BHRF1-2* were associated with both ovarian cancer survival and recurrence and 27 miRNAs were associated with either one hazard. Two miRNAs (hsa-miR-521 and hsa-miR-497 were cohort-dependent, while 28 were cohort-independent. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value <0.05 with ovarian cancer survival and recurrence, respectively. Functional analysis highlighted the association between cellular and nucleotide metabolic processes and ovarian cancer. The more direct connections and higher centrality of the miRNAs, TFs and target genes in the survival network studied suggest that network-based approaches to prognosticate or predict ovarian cancer survival may be more effective than those for ovarian cancer recurrence. This study demonstrated the feasibility to infer reliable miRNA-TF-target gene networks associated with survival and recurrence of ovarian cancer based on the simultaneous analysis of co-expression profiles and consideration of the clinical characteristics of the patients.

  4. Involvement of the CREB5 regulatory network in colorectal cancer metastasis.

    Science.gov (United States)

    Qi, Lu; Ding, Yanqing

    2014-07-01

    The signal regulatory network involved in colorectal cancer metastasis is complicated and thus the search for key control steps in the network is of great significance for unraveling colorectal cancer metastasis mechanism and finding drug-target site. Previous studies suggested that CREB5 (cAMP responsive element binding protein 5) might play key role in the metastatic signal network of colorectal cancer. Through colorectal cancer expression profile and enriching analysis of the effect of CREB5 gene expression levels on colorectal cancer molecular events, we found that these molecular events are correlated with tumor metastasis. Based on the feature that CREB5 could combine with c-Jun to form heterodimer, together with enriched binding sites for transcription factor AP-1, we identified 16 genes which were up-regulated in the CREB5 high-expression group, contained AP-1 binding sites, and participated in cancer pathway. The molecular network involving these 16 genes, in particular, CSF1R, MMP9, PDGFRB, FIGF and IL6, regulates cell migration. Therefore, CREB5 might accelerate the metastasis of colorectal cancer by regulating these five key genes.

  5. Incorporating gene co-expression network in identification of cancer prognosis markers

    Directory of Open Access Journals (Sweden)

    Li Yang

    2010-05-01

    Full Text Available Abstract Background Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them. Results We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives. Conclusions The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.

  6. Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

    OpenAIRE

    Biglarian, A; E. Hajizadeh; Kazemnejad, A; Zali, MR

    2011-01-01

    "nBackground: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients."nMethods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal...

  7. Network Pharmacology of Ayurveda Formulation Triphala with Special Reference to Anti-Cancer Property.

    Science.gov (United States)

    Chandran, Uma; Mehendale, Neelay; Tillu, Girish; Patwardhan, Bhushan

    2015-01-01

    Network pharmacology is an emerging technique, which integrates systems biology and computational biology to study multi-component and multi-targeted formulations. Ayurveda, the traditional system of Indian medicine, uses intelligent formulations; however, their scientific rationale and mechanisms remain largely unexplored. This paper presents the potential of network pharmacology to understand the rationale of a commonly used Ayurveda formulation known as Triphala. We have developed pharmacology networks of Triphala based on the information gathered from different databases and using the software Cytoscape. The networks depict the interaction of bioactives with molecular targets and their relation with diseases, especially cancer. The network pharmacology analysis of Triphala has offered new relationships among bioactives, targets and putative applications of cancer etiology. This pioneering effort might open new possibilities to know pharmacodynamics of Ayurvedic drugs like Triphala and also help in the discovery of new leads and targets for various diseases.

  8. Identifying dysregulated pathways in cancers from pathway interaction networks

    Directory of Open Access Journals (Sweden)

    Liu Ke-Qin

    2012-06-01

    Full Text Available Abstract Background Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes. Results In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network. Our contribution is three-fold. Firstly, we present a new method to construct pathway interaction network based on gene expression, protein-protein interactions and cellular pathways. Secondly, the identification of dysregulated pathways in cancer is treated as a feature selection problem, which is biologically reasonable and easy to interpret. Thirdly, the dysregulated pathways are identified as subnetworks from the pathway interaction networks, where the subnetworks characterize very well the functional dependency or crosstalk between pathways. The benchmarking results on several distinct cancer datasets demonstrate that our method can obtain more reliable and accurate results compared with existing state of the art methods. Further functional analysis and independent literature evidence also confirm that our identified potential pathogenic pathways are biologically reasonable, indicating the effectiveness of our method. Conclusions Dysregulated pathways can serve as better biomarkers compared with single genes. In this work, by utilizing pathway interaction networks and gene expression data, we propose a novel approach that effectively identifies dysregulated pathways, which can not only be used

  9. Network medicine strikes a blow against breast cancer.

    Science.gov (United States)

    Erler, Janine T; Linding, Rune

    2012-05-11

    Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative breast tumors vulnerable to a second, later hit with DNA-damaging drugs, demonstrating that time- and order-dependent drug combinations can be more efficacious in killing cancer cells.

  10. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2016-10-20

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  11. Evaluation of a community-academic partnership: lessons from Latinos in a network for cancer control.

    Science.gov (United States)

    Corbin, J Hope; Fernandez, Maria E; Mullen, Patricia D

    2015-05-01

    Established in 2002, Latinos in a Network for Cancer Control is a community-academic network supported by the Centers for Disease Control and Prevention and the National Cancer Institute. The network includes >130 individuals from 65 community and academic organizations committed to reducing cancer-related health disparities. Using an empirically derived systems model--the Bergen Model of Collaborative Functioning--as the analytic frame, we interviewed 19 partners to identify challenges and successful processes. Findings indicated that sustained partner interaction created "meaningful relationships" that were routinely called on for collaboration. The leadership was regarded positively on vision, charisma, and capacity. Limitations included overreliance on a single leader. Suggestions supported more delegation of decision making, consistent communication, and more equitable resource distribution. The study highlighted new insights into dynamics of collaboration: Greater inclusiveness of inputs (partners, finances, mission) and loosely defined roles and structure produced strong connections but less network-wide productivity (output). Still, this profile enabled the creation of more tightly defined and highly productive subgroups, with clear goals and roles but less inclusive of inputs than the larger network. Important network outputs included practice-based research publications, cancer control intervention materials, and training to enhance the use of evidence-based interventions, as well as continued and diversified funding.

  12. Comparison of gene regulatory networks of benign and malignant breast cancer samples with normal samples.

    Science.gov (United States)

    Chen, D B; Yang, H J

    2014-11-11

    The aim of this study was to explain the pathogenesis and deterioration process of breast cancer. Breast cancer expression profile data GSE27567 was downloaded from the Gene Expression Omnibus (GEO) database, and breast cancer-related genes were extracted from databases, including Cancer-Resource and Online Mendelian Inheritance In Man (OMIM). Next, h17 transcription factor data were obtained from the University of California, Santa Cruz. Database for Annotation, Visualization, and Integrated Discovery (DAVID)-enrichment analysis was applied and gene-regulatory networks were constructed by double-two-way t-tests in 3 states, including normal, benign, and malignant. Furthermore, network topological properties were compared between 2 states, and breast cancer-related bub genes were ranked according to their different degrees between each of the two states. A total of 2380 breast cancer-related genes and 215 transcription factors were screened by exploring databases; the genes were mainly enriched in their functions, such as cell apoptosis and proliferation, and pathways, such as p53 signaling and apoptosis, which were related with carcinogenesis. In addition, gene-regulatory networks in the 3 conditions were constructed. By comparing their network topological properties, we found that there is a larger transition of differences between malignant and benign breast cancer. Moreover, 8 hub genes (YBX1, ZFP36, YY1, XRCC5, XRCC4, ZFHX3, ZMAT3, and XPC) were identified in the top 10 genes ranked by different degrees. Through comparative analysis of gene-regulation networks, we identified the link between related genes and the pathogenesis of breast cancer. However, further experiments are needed to confirm our results.

  13. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  14. Prediction and testing of biological networks underlying intestinal cancer.

    Directory of Open Access Journals (Sweden)

    Vishal N Patel

    Full Text Available Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called "driver" genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections--both precedented and novel--between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21, known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc(1638N+/- or Cdkn1a (Cdkn1a(-/-, followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional, then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.

  15. Epidemiological studies of oral cancer.

    Science.gov (United States)

    Pindborg, J J

    1977-06-01

    The FDI has shown considerable interest in the oral cancer and has in recent years arranged three symposia on the subject. The incidence of oral cancer shows marked geographic differences mostly depending upon environmental factors. In the present paper the epidemiology of oral cancer is illustrated by the relative frequency to total number of cancers and incidence rates from a number of countries. Canada has the highest rate of cancer of the vermilion border, which is extremely rare among dark-skinned people. Even within one country differences may be found, a fact which is illustrated by findings from Czechoslovakia and India. In most of the studies dealing with the etiology of oral cancer tobacco usage in its various forms is shown to be the outstanding factor.

  16. Capillary Network, Cancer and Kleiber Law

    CERN Document Server

    Dattoli, G; Licciardi, S; Guiot, C; Deisboeck, T S

    2014-01-01

    We develop a heuristic model embedding Kleiber and Murray laws to describe mass growth, metastasis and vascularization in cancer. We analyze the relevant dynamics using different evolution equations (Verhulst, Gompertz and others). Their extension to reaction diffusion equation of the Fisher type is then used to describe the relevant metastatic spreading in space. Regarding this last point, we suggest that cancer diffusion may be regulated by Levy flights mechanisms and discuss the possibility that the associated reaction diffusion equations are of the fractional type, with the fractional coefficient being determined by the fractal nature of the capillary evolution.

  17. The National Cancer Institute's Physical Sciences - Oncology Network

    Science.gov (United States)

    Espey, Michael Graham

    In 2009, the NCI launched the Physical Sciences - Oncology Centers (PS-OC) initiative with 12 Centers (U54) funded through 2014. The current phase of the Program includes U54 funded Centers with the added feature of soliciting new Physical Science - Oncology Projects (PS-OP) U01 grant applications through 2017; see NCI PAR-15-021. The PS-OPs, individually and along with other PS-OPs and the Physical Sciences-Oncology Centers (PS-OCs), comprise the Physical Sciences-Oncology Network (PS-ON). The foundation of the Physical Sciences-Oncology initiative is a high-risk, high-reward program that promotes a `physical sciences perspective' of cancer and fosters the convergence of physical science and cancer research by forming transdisciplinary teams of physical scientists (e.g., physicists, mathematicians, chemists, engineers, computer scientists) and cancer researchers (e.g., cancer biologists, oncologists, pathologists) who work closely together to advance our understanding of cancer. The collaborative PS-ON structure catalyzes transformative science through increased exchange of people, ideas, and approaches. PS-ON resources are leveraged to fund Trans-Network pilot projects to enable synergy and cross-testing of experimental and/or theoretical concepts. This session will include a brief PS-ON overview followed by a strategic discussion with the APS community to exchange perspectives on the progression of trans-disciplinary physical sciences in cancer research.

  18. Offline Social Relationships and Online Cancer Communication: Effects of Social and Family Support on Online Social Network Building.

    Science.gov (United States)

    Namkoong, Kang; Shah, Dhavan V; Gustafson, David H

    2016-11-08

    This study investigates how social support and family relationship perceptions influence breast cancer patients' online communication networks in a computer-mediated social support (CMSS) group. To examine social interactions in the CMSS group, we identified two types of online social networks: open and targeted communication networks. The open communication network reflects group communication behaviors (i.e., one-to-many or "broadcast" communication) in which the intended audience is not specified; in contrast, the targeted communication network reflects interpersonal discourses (i.e., one-to-one or directed communication) in which the audience for the message is specified. The communication networks were constructed by tracking CMSS group usage data of 237 breast cancer patients who participated in one of two National Cancer Institute-funded randomized clinical trials. Eligible subjects were within 2 months of a diagnosis of primary breast cancer or recurrence at the time of recruitment. Findings reveal that breast cancer patients who perceived less availability of offline social support had a larger social network size in the open communication network. In contrast, those who perceived less family cohesion had a larger targeted communication network in the CMSS group, meaning they were inclined to use the CMSS group for developing interpersonal relationships.

  19. Network-based reading system for lung cancer screening CT

    Science.gov (United States)

    Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio

    2006-03-01

    This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.

  20. Network for Translational Research - Cancer Imaging Program

    Science.gov (United States)

    Cooperative agreement (U54) awards to establish Specialized Research Resource Centers that will participate as members of a network of inter-disciplinary, inter-institutional research teams for the purpose of supporting translational research in optical imaging and/or spectroscopy in vivo, with an emphasis on multiple modalities.

  1. Transcription factor FOXA2-centered transcriptional regulation network in non-small cell lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Sang-Min; An, Joo-Hee; Kim, Chul-Hong; Kim, Jung-Woong, E-mail: jungkim@cau.ac.kr; Choi, Kyung-Hee, E-mail: khchoi@cau.ac.kr

    2015-08-07

    Lung cancer is the leading cause of cancer-mediated death. Although various therapeutic approaches are used for lung cancer treatment, these mainly target the tumor suppressor p53 transcription factor, which is involved in apoptosis and cell cycle arrest. However, p53-targeted therapies have limited application in lung cancer, since p53 is found to be mutated in more than half of lung cancers. In this study, we propose tumor suppressor FOXA2 as an alternative target protein for therapies against lung cancer and reveal a possible FOXA2-centered transcriptional regulation network by identifying new target genes and binding partners of FOXA2 by using various screening techniques. The genes encoding Glu/Asp-rich carboxy-terminal domain 2 (CITED2), nuclear receptor subfamily 0, group B, member 2 (NR0B2), cell adhesion molecule 1 (CADM1) and BCL2-associated X protein (BAX) were identified as putative target genes of FOXA2. Additionally, the proteins including highly similar to heat shock protein HSP 90-beta (HSP90A), heat shock 70 kDa protein 1A variant (HSPA1A), histone deacetylase 1 (HDAC1) and HDAC3 were identified as novel interacting partners of FOXA2. Moreover, we showed that FOXA2-dependent promoter activation of BAX and p21 genes is significantly reduced via physical interactions between the identified binding partners and FOXA2. These results provide opportunities to understand the FOXA2-centered transcriptional regulation network and novel therapeutic targets to modulate this network in p53-deficient lung cancer. - Highlights: • Identification of new target genes of FOXA2. • Identifications of novel interaction proteins of FOXA2. • Construction of FOXA2-centered transcriptional regulatory network in non-small cell lung cancer.

  2. Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets.

    Science.gov (United States)

    Lu, Junyan; Zeng, Hanlin; Liang, Zhongjie; Chen, Limin; Zhang, Liyi; Zhang, Hao; Liu, Hong; Jiang, Hualiang; Shen, Bairong; Huang, Ming; Geng, Meiyu; Spiegel, Sarah; Luo, Cheng

    2015-10-08

    The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner.

  3. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Science.gov (United States)

    Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

    2013-01-01

    Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research

  4. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  5. Berberine alters epigenetic modifications, disrupts microtubule network, and modulates HPV-18 E6-E7 oncoproteins by targeting p53 in cervical cancer cell HeLa: a mechanistic study including molecular docking.

    Science.gov (United States)

    Saha, Santu Kumar; Khuda-Bukhsh, Anisur Rahman

    2014-12-05

    Increased evidence of chemo-resistance, toxicity and carcinogenicity necessitates search for alternative approaches for determining next generation cancer therapeutics and targets. We therefore tested the efficacy of plant alkaloid berberine on human papilloma virus (HPV) -18 positive cervical cancer cell HeLa systematically-involving certain cellular, viral and epigenetic factors. We observed disruptions of microtubule network and changes in membrane topology due to berberine influx through confocal and atomic force microscopies (AFM). We examined nuclear uptake, internucleosomal DNA damages, mitochondrial membrane potential (MMP) alterations and cell migration assays to validate possible mode of cell death events. Analytical data on interactions of berberine with pBR322 through fourier transform infrared (FTIR) and gel migration assay strengthen berberine׳s biologically significant DNA binding abilities. We measured cellular uptake, DNA ploidy and DNA strand-breaks through fluorescence activated cell sorting (FACS). To elucidate epigenetic modifications, in support of DNA binding associated processes, if any, we conducted methylation-specific restriction enzyme (RE) assay, methylation specific-PCR (MSP) and expression studies of histone proteins. We also analyzed differential interactions and localization of cellular tumor suppressor p53 and viral oncoproteins HPV-18 E6-E7 through siRNA approach. We further made in-silico approaches to determine possible binding sites of berberine on histone proteins. Overall results indicated cellular uptake of berberine through cell membrane depolarization causing disruption of microtubule networks and its biological DNA binding abilities that probably contributed to epigenetic modifications. Results of modulation in p53 and viral oncoproteins HPV-18 E6-E7 by berberine further proved its potential as a promising chemotherapeutic agent in cervical cancer.

  6. Detection of gene communities in multi-networks reveals cancer drivers

    Science.gov (United States)

    Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

    2015-12-01

    We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

  7. Changes in Female Support Network Systems and Adaptation after Breast Cancer Diagnosis: Differences between Older and Younger Patients

    Science.gov (United States)

    Ashida, Sato; Palmquist, Aunchalee E. L.; Basen-Engquist, Karen; Singletary, S. Eva; Koehly, Laura M.

    2009-01-01

    Purpose: This study evaluates the changes in social networks of older and younger breast cancer patients over a 6-month period following their first diagnosis and how such modifications are associated with changes in the patients' mood state. Design and Methods: Newly diagnosed breast cancer patients were interviewed shortly after their diagnosis…

  8. [Meta analysis of the use of Bayesian networks in breast cancer diagnosis].

    Science.gov (United States)

    Simões, Priscyla Waleska; Silva, Geraldo Doneda da; Moretti, Gustavo Pasquali; Simon, Carla Sasso; Winnikow, Erik Paul; Nassar, Silvia Modesto; Medeiros, Lidia Rosi; Rosa, Maria Inês

    2015-01-01

    The aim of this study was to determine the accuracy of Bayesian networks in supporting breast cancer diagnoses. Systematic review and meta-analysis were carried out, including articles and papers published between January 1990 and March 2013. We included prospective and retrospective cross-sectional studies of the accuracy of diagnoses of breast lesions (target conditions) made using Bayesian networks (index test). Four primary studies that included 1,223 breast lesions were analyzed, 89.52% (444/496) of the breast cancer cases and 6.33% (46/727) of the benign lesions were positive based on the Bayesian network analysis. The area under the curve (AUC) for the summary receiver operating characteristic curve (SROC) was 0.97, with a Q* value of 0.92. Using Bayesian networks to diagnose malignant lesions increased the pretest probability of a true positive from 40.03% to 90.05% and decreased the probability of a false negative to 6.44%. Therefore, our results demonstrated that Bayesian networks provide an accurate and non-invasive method to support breast cancer diagnosis.

  9. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  10. Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.

    Science.gov (United States)

    Yao, Lijing; Shen, Hui; Laird, Peter W; Farnham, Peggy J; Berman, Benjamin P

    2015-05-21

    Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.

  11. Reconstructing the Prostate Cancer Transcriptional Regulatory Network

    Science.gov (United States)

    2010-09-01

    Jul 3;4(7):e6146. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P...Adrienne Pollack for the DR-Integrator logo art. Funding: National Institutes of Health (CA97139 and CA112016 to J.R.P.); Paul & Daisy Soros...RO1 AG14358, NIH RO1 CA098415, NIH RO1 CA95717, NIH U24 CA80295, DOD PC060595, Fred Hutchinson Cancer Research Center Institutional Funds, Paul and

  12. The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

    Science.gov (United States)

    Krogan, Nevan J.; Lippman, Scott; Agard, David A.; Ashworth, Alan; Ideker, Trey

    2017-01-01

    Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, called The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these Cancer Cell Maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine. PMID:26000852

  13. Prediction of near-term breast cancer risk using a Bayesian belief network

    Science.gov (United States)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (pbreast cancer risk than with a single feature.

  14. A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Gang Ruan; Jin-Lian Wang; Jian-Geng Li

    2006-01-01

    Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules' functions changed with their structures.

  15. Alliance Against Cancer, the network of Italian cancer centers bridging research and care.

    Science.gov (United States)

    De Paoli, Paolo; Ciliberto, Gennaro; Ferrarini, Manlio; Pelicci, PierGiuseppe; Dellabona, Paolo; De Lorenzo, Francesco; Mantovani, Alberto; Musto, Pellegrino; Opocher, Giuseppe; Picci, Piero; Ricciardi, Walter; De Maria, Ruggero

    2015-11-14

    Alliance Against Cancer (ACC) was established in Rome in 2002 as a consortium of six Italian comprehensive cancer centers (Founders). The aims of ACC were to promote a network among Italian oncologic institutions in order to develop specific, advanced projects in clinical and translational research. During the following years, many additional full and associate members joined ACC, that presently includes the National Institute of Health, 17 research-oriented hospitals, scientific and patient organizations. Furthermore, in the last three years ACC underwent a reorganization process that redesigned the structure, governance and major activities. The present goal of ACC is to achieve high standards of care across Italy, to implement and harmonize principles of modern personalized and precision medicine, by developing cost effective processes and to provide tailored information to cancer patients. We herein summarize some of the major initiatives that ACC is currently developing to reach its goal, including tumor genetic screening programs, establishment of clinical trial programs for cancer patients treated in Italian cancer centers, facilitate their access to innovative drugs under development, improve quality through an European accreditation process (European Organization of Cancer Institutes), and develop international partnerships. In conclusion, ACC is a growing organization, trying to respond to the need of networking in Italy and may contribute significantly to improve the way we face cancer in Europe.

  16. Bayesian network approach for modeling local failure in lung cancer

    Science.gov (United States)

    Oh, Jung Hun; Craft, Jeffrey; Al-Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; Naqa, Issam El

    2011-01-01

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins’ role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which is comprised of clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogenous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients. PMID:21335651

  17. 人工神经网络建立食管癌发病预测模型的比较研究%THE COMPARATIVE STUDY OF BUILDING FORECASTING MODEL OF ESOPHAGEAL CANCER BY USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    徐继承; 黄水平; 李磊; 刘桂红; 周风娟; 苗慧; 孙桂香; 赵华硕; 张训保; 金英良

    2011-01-01

    [Objective] Through the establishment of artificial neural network prediction model of esophageal cancer, so as to screen esophageal cancer in wide scale. [Methods] Investigated esophageal cancer patients with case-control study in Xuzhou during 2007-2008. Applies pruning algorithm BP neural network, C5.0 decision tree model and Logistic regression to establish a prediction model for gastric cancer and evaluated the practical application oi each model in the prediction accuracy. [Results] Established prediction model by using single hidden layer pruning algorithm BP neural network, 75% purity of decision tree and Logistic regression, prediction accuracy were 97.82%, 96.73%, 94.82%. There were significant differences between them. Compared area under the ROC curve, pruning algorithm BP neural network model was superior to CS.O decision tree and Logistic regression (χ2 = 7.440 5, P = 0.024 2). [Conclusion] The established ANN model can be used for early screening of esophageal cancer.%[目的]通过人工神经网络建立食管癌预测模型,为大规模筛检食管癌奠定基础.[方法]对2007~2008年徐州地区食管癌患者进行病例对照研究,应用修剪算法BP神经网络、C5.0决策树、传统Logistic回归3种办法建立预测模型,并比较3种模型的预测精度.[结果]分别选择单隐层的修剪算法BP神经网络模型、修剪纯度为75%的C5.0决策树模型和Logistic回归模型建立预测模型,预测精度分别为97.82%、96.73%、94.82%,差异有统计学意义.ROC曲线下面积比较修剪算法BP神经网络模型优于C5.0决策树和Logistic回归,各曲线下面积比较差异有统计学意义(x2=7.440 5,P=0.024 2).[结论]应用侈剪算法BP神经网络建立好的预测模型相比用C5.0决策树和Logisfic回归建立的模型用于食管癌的初筛效果较好.

  18. Cooperation among cancer cells as public goods games on Voronoi networks.

    Science.gov (United States)

    Archetti, Marco

    2016-05-07

    Cancer cells produce growth factors that diffuse and sustain tumour proliferation, a form of cooperation that can be studied using mathematical models of public goods in the framework of evolutionary game theory. Cell populations, however, form heterogeneous networks that cannot be described by regular lattices or scale-free networks, the types of graphs generally used in the study of cooperation. To describe the dynamics of growth factor production in populations of cancer cells, I study public goods games on Voronoi networks, using a range of non-linear benefits that account for the known properties of growth factors, and different types of diffusion gradients. The results are surprisingly similar to those obtained on regular graphs and different from results on scale-free networks, revealing that network heterogeneity per se does not promote cooperation when public goods diffuse beyond one-step neighbours. The exact shape of the diffusion gradient is not crucial, however, whereas the type of non-linear benefit is an essential determinant of the dynamics. Public goods games on Voronoi networks can shed light on intra-tumour heterogeneity, the evolution of resistance to therapies that target growth factors, and new types of cell therapy.

  19. A research about breast cancer detection using different neural networks and K-MICA algorithm.

    Science.gov (United States)

    Kalteh, A A; Zarbakhsh, Payam; Jirabadi, Meysam; Addeh, Jalil

    2013-01-01

    Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and the simulation results show that the recommended system has high accuracy.

  20. A research about breast cancer detection using different neural networks and K-MICA algorithm

    Directory of Open Access Journals (Sweden)

    A A Kalteh

    2013-01-01

    Full Text Available Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC database and the simulation results show that the recommended system has high accuracy.

  1. Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

    Science.gov (United States)

    Yuan, Fei; Zhou, You; Wang, Meng; Yang, Jing; Wu, Kai; Lu, Changhong; Kong, Xiangyin; Cai, Yu-Dong

    2015-01-01

    Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486

  2. SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness.

    Directory of Open Access Journals (Sweden)

    Hui-Yi Lin

    Full Text Available Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs (MMP16+ ROBO1, MMP16+ CSF1, and MMP16+ EGFR were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs (rs1477908+ rs1387665, rs1467251+ rs7625555, and rs1824717+ rs7625555 were in MMP16 and ROBO1, one pair (rs2176771+ rs333970 in MMP16 and CSF1, and one pair (rs1401862+ rs6964705 in MMP16 and EGFR. The results suggest that MMP16 may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.

  3. Construction of pancreatic cancer double-factor regulatory network based on chip data on the transcriptional level.

    Science.gov (United States)

    Zhao, Li-Li; Zhang, Tong; Liu, Bing-Rong; Liu, Tie-Fu; Tao, Na; Zhuang, Li-Wei

    2014-05-01

    Transcription factor (TF) and microRNA (miRNA) have been discovered playing crucial roles in cancer development. However, the effect of TFs and miRNAs in pancreatic cancer pathogenesis remains vague. We attempted to reveal the possible mechanism of pancreatic cancer based on transcription level. Using GSE16515 datasets downloaded from gene expression omnibus database, we first identified the differentially expressed genes (DEGs) in pancreatic cancer by the limma package in R. Then the DEGs were mapped into DAVID to conduct the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. TFs and miRNAs that DEGs significantly enriched were identified by Fisher's test, and then the pancreatic cancer double-factor regulatory network was constructed. In our study, total 1117 DEGs were identified and they significantly enriched in 4 KEGG pathways. A double-factor regulatory network was established, including 29 DEGs, 24 TFs, 25 miRNAs. In the network, LAMC2, BRIP1 and miR155 were identified which may be involved in pancreatic cancer development. In conclusion, the double-factor regulatory network was found to play an important role in pancreatic cancer progression and our results shed new light on the molecular mechanism of pancreatic cancer.

  4. Role of the lncRNA-p53 regulatory network in cancer.

    Science.gov (United States)

    Zhang, Ali; Xu, Min; Mo, Yin-Yuan

    2014-06-01

    Advances in functional genomics have led to discovery of a large group of previous uncharacterized long non-coding RNAs (lncRNAs). Emerging evidence indicates that lncRNAs may serve as master gene regulators through various mechanisms. Dysregulation of lncRNAs is often associated with a variety of human diseases including cancer. Of significant interest, recent studies suggest that lncRNAs participate in the p53 tumor suppressor regulatory network. In this review, we discuss how lncRNAs serve as p53 regulators or p53 effectors. Further characterization of these p53-associated lncRNAs in cancer will provide a better understanding of lncRNA-mediated gene regulation in the p53 pathway. As a result, lncRNAs may prove to be valuable biomarkers for cancer diagnosis or potential targets for cancer therapy.

  5. Vorinostat and bortezomib as third-line therapy in patients with advanced non-small cell lung cancer: a Wisconsin Oncology Network Phase II study

    Science.gov (United States)

    Campbell, Toby C.; Zhang, Chong; Kim, KyungMann; Kolesar, Jill M.; Oettel, Kurt R.; Blank, Jules H.; Robinson, Emily G.; Ahuja, Harish G.; Kirschling, Ron J.; Johnson, Peter H.; Huie, Michael S.; Wims, Mary E.; Larson, Martha M.; Hernan, Hilary R.; Traynor, Anne M.

    2014-01-01

    Summary Introduction The primary objective of this phase II trial was to evaluate the efficacy and tolerability of vorinostat and bortezomib as third-line therapy in advanced non-small cell lung cancer (NSCLC) patients. Methods Eligibility criteria included recurrent/metastatic NSCLC, having received 2 prior systemic regimens, and performance status 0–2. Patients took vorinostat 400 mg PO daily days 1–14 and bortezomib 1.3 mg/m2 IV day 1, 4, 8 and 11 in a 21-day cycle. Primary endpoint was 3-month progression free survival (3m-PFS), with a goal of at least 40 % of patients being free of progression at that time point. This study followed a two-stage minimax design. Results Eighteen patients were enrolled in the first stage. All patients had two prior lines of treatment. Patients received a median of two treatment cycles (range: 1–6) on study. There were no anti-tumor responses; stable disease was observed in 5 patients (27.8 %). Median PFS was 1.5 months, 3m-PFS rate 11.1 %, and median overall survival 4.7 months. The most common grade 3/4 toxicities were thrombocytopenia and fatigue. Two patients who had baseline taxane-related grade 1 peripheral neuropathy developed grade 3 neuropathy. The study was closed at its first interim analysis for lack of efficacy. Conclusions Bortezomib and vorinostat displayed minimal anti-tumor activity as third-line therapy in NSCLC. We do not recommend this regimen for further investigation in unselected patients. PMID:23728919

  6. Transcription factor FOXA2-centered transcriptional regulation network in non-small cell lung cancer.

    Science.gov (United States)

    Jang, Sang-Min; An, Joo-Hee; Kim, Chul-Hong; Kim, Jung-Woong; Choi, Kyung-Hee

    2015-08-01

    Lung cancer is the leading cause of cancer-mediated death. Although various therapeutic approaches are used for lung cancer treatment, these mainly target the tumor suppressor p53 transcription factor, which is involved in apoptosis and cell cycle arrest. However, p53-targeted therapies have limited application in lung cancer, since p53 is found to be mutated in more than half of lung cancers. In this study, we propose tumor suppressor FOXA2 as an alternative target protein for therapies against lung cancer and reveal a possible FOXA2-centered transcriptional regulation network by identifying new target genes and binding partners of FOXA2 by using various screening techniques. The genes encoding Glu/Asp-rich carboxy-terminal domain 2 (CITED2), nuclear receptor subfamily 0, group B, member 2 (NR0B2), cell adhesion molecule 1 (CADM1) and BCL2-associated X protein (BAX) were identified as putative target genes of FOXA2. Additionally, the proteins including highly similar to heat shock protein HSP 90-beta (HSP90A), heat shock 70 kDa protein 1A variant (HSPA1A), histone deacetylase 1 (HDAC1) and HDAC3 were identified as novel interacting partners of FOXA2. Moreover, we showed that FOXA2-dependent promoter activation of BAX and p21 genes is significantly reduced via physical interactions between the identified binding partners and FOXA2. These results provide opportunities to understand the FOXA2-centered transcriptional regulation network and novel therapeutic targets to modulate this network in p53-deficient lung cancer.

  7. The redox biology network in cancer pathophysiology and therapeutics

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    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  8. Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics.

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    Gogoi, Barbi; Gogoi, Dhrubajyoti; Silla, Yumnam; Kakoti, Bibhuti Bhushan; Bhau, Brijmohan Singh

    2017-01-31

    Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs-cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-d-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.

  9. Extending pathways and processes using molecular interaction networks to analyse cancer genome data

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    Krasnogor Natalio

    2010-12-01

    Full Text Available Abstract Background Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways. Results We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes. Conclusions The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

  10. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

    Science.gov (United States)

    Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

    2015-08-01

    Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes.

  11. Prediction of key genes in ovarian cancer treated with decitabine based on network strategy.

    Science.gov (United States)

    Wang, Yu-Zhen; Qiu, Sheng-Chun

    2016-06-01

    The objective of the present study was to predict key genes in ovarian cancer before and after treatment with decitabine utilizing a network approach and to reveal the molecular mechanism. Pathogenic networks of ovarian cancer before and after treatment were identified based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) detected by Significance Analysis of Microarrays (SAM) method. A weight was assigned to each gene in the pathogenic network and then candidate genes were evaluated. Topological properties (degree, betweenness, closeness and stress) of candidate genes were analyzed to investigate more confident pathogenic genes. Pathway enrichment analysis for candidate and seed genes were conducted. Validation of candidate gene expression in ovarian cancer was performed by reverse transcriptase-polymerase chain reaction (RT-PCR) assays. There were 73 nodes and 147 interactions in the pathogenic network before treatment, while 47 nodes and 66 interactions after treatment. A total of 32 candidate genes were identified in the before treatment group of ovarian cancer, of which 16 were rightly candidate genes after treatment and the others were silenced. We obtained 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) for decitabine treatment that were validated by RT-PCR. In conclusion, we successfully identified 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) and validated them, which provides insight into the molecular mechanisms of decitabine treatment and may be potential pathogenic biomarkers for the therapy of ovarian cancer.

  12. Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

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    Fei Long

    2015-01-01

    Full Text Available Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC and non-small-cell lung cancer (NSCLC that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

  13. The meaning and validation of social support networks for close family of persons with advanced cancer

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    Sjolander Catarina

    2012-09-01

    Full Text Available Abstract Background To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study’s empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Methods Seventeen family members with a relative who 8–14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. Results The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. Conclusions The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could

  14. Key concerns about the current state of bladder cancer: a position paper from the Bladder Cancer Think Tank, the Bladder Cancer Advocacy Network, and the Society of Urologic Oncology.

    Science.gov (United States)

    Lotan, Yair; Kamat, Ashish M; Porter, Michael P; Robinson, Victoria L; Shore, Neal; Jewett, Michael; Schelhammer, Paul F; deVere White, Ralph; Quale, Diane; Lee, Cheryl T

    2009-09-15

    Bladder cancer is the fifth most common cancer in the United States and, on a per capita basis, is the most expensive cancer from diagnosis to death. Unfortunately, National Cancer Institute funding for bladder cancer is quite low when compared with other common malignancies. Limited funding has stifled research opportunities for new and established investigators, ultimately encouraging them to redirect research efforts to other organ sites. Waning interest of scientists has further fueled the cycle of modest funding for bladder cancer. One important consequence of this has been a lack of scientific advancement in the field. Patient advocates have decidedly advanced research efforts in many cancer sites. Breast, prostate, pancreatic, and ovarian cancer advocates have organized highly successful campaigns to lobby the federal government and the medical community to devote increased attention and funding to understudied malignancies and to conduct relevant studies to better understand the therapy, diagnosis, and prevention of these diseases. Bladder cancer survivors have lacked a coordinated advocacy voice until recently. A concerted effort to align bladder cancer advocates, clinicians, and urologic organizations is essential to define the greatest needs in bladder cancer and to develop related solutions. This position paper represents a collaborative discussion to define the most concerning trends and greatest needs in the field of bladder cancer as outlined by the Bladder Cancer Think Tank, the Bladder Cancer Advocacy Network, and the Society of Urologic Oncology.

  15. P53 tumor suppression network in cancer epigenetics.

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    Mishra, Alok; Brat, Daniel J; Verma, Mukesh

    2015-01-01

    The tumor suppressor p53 is one of the most complex and widely studied genes in cancer biology. In spite of the vast on literature the transcriptional regulation of p53, aspects of its especially epigenetic regulation are not completely understood. This chapter presents a concise overview of p53-related epigenetic events involved in oncogenesis and tumor suppression. We limit the scope to epigenetic modifications of the p53 promoter per se as well as its well-established downstream targets. The indirect role of p53 affecting the epigenetic machinery of cancer cells via specific proteins and transcription factors is discussed. Current concepts of p53-related cancer epigenetics offer myriad avenues for cancer therapies. Challenges in the field are also discussed.

  16. The use of gene interaction networks to improve the identification of cancer driver genes

    Directory of Open Access Journals (Sweden)

    Emilie Ramsahai

    2017-01-01

    Full Text Available Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  17. Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.

    Science.gov (United States)

    Su, Naifang; Dai, Ding; Deng, Chao; Qian, Minping; Deng, Minghua

    2014-06-01

    Discovering the regulation of cancer-related gene is of great importance in cancer biology. Transcription factors and microRNAs are two kinds of crucial regulators in gene expression, and they compose a combinatorial regulatory network with their target genes. Revealing the structure of this network could improve the authors' understanding of gene regulation, and further explore the molecular pathway in cancer. In this article, the authors propose a novel approach graphical adaptive lasso (GALASSO) to construct the regulatory network in breast cancer. GALASSO use a Gaussian graphical model with adaptive lasso penalties to integrate the sequence information as well as gene expression profiles. The simulation study and the experimental profiles verify the accuracy of the authors' approach. The authors further reveal the structure of the regulatory network, and explore the role of feedforward loops in gene regulation. In addition, the authors discuss the combinatorial regulatory effect between transcription factors and microRNAs, and select miR-155 for detailed analysis of microRNA's role in cancer. The proposed GALASSO approach is an efficient method to construct the combinatorial regulatory network. It also provides a new way to integrate different data sources and could find more applications in meta-analysis problem.

  18. A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

    CERN Document Server

    Staiger, C; Kooter, R; Dittrich, M; Mueller, T; Klau, G W; Wessels, L F A

    2011-01-01

    Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple sin...

  19. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells

    Science.gov (United States)

    Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Radulescu, Ovidiu

    2017-01-01

    The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform. PMID:28306714

  20. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  1. Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology

    OpenAIRE

    Bianconi, Fortunato; Baldelli, Elisa; Luovini, Vienna; Petricoin, Emanuel F.; Crinò, Lucio; Valigi, Paolo

    2015-01-01

    Background The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. Results We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm bas...

  2. Social ties and risk for cancer--a prospective cohort study

    DEFF Research Database (Denmark)

    Bergelt, Corinna; Prescott, Eva; Grønbaek, Morten;

    2009-01-01

    (breast, lung, prostate and colon and rectum) were conducted with the Cox proportional hazards model, with adjustment for a number of well-known risk factors for cancer. RESULTS: While we found no significant association between social ties and risk for cancer in men, women with high social network scores......BACKGROUND: Poor social support and small social networks have been associated with increased risks for conditions such as coronary heart disease as well as with overall mortality. We investigated the association between social ties and risk for cancer. MATERIAL AND METHODS: The study sample...... had an increased risk for lung cancer of borderline significance (HR, 2.16; 95% CI, 1.02-4.60). The risks for breast cancer and colorectal cancers were not significantly increased in the same group of women. DISCUSSION: The results of this study do not support the hypothesis that social network size...

  3. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  4. Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

    Science.gov (United States)

    Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

    2016-03-01

    Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016.

  5. A multilevel data integration resource for breast cancer study

    Directory of Open Access Journals (Sweden)

    Viti Federica

    2010-06-01

    Full Text Available Abstract Background Breast cancer is one of the most common cancer types. Due to the complexity of this disease, it is important to face its study with an integrated and multilevel approach, from genes, transcripts and proteins to molecular networks, cell populations and tissues. According to the systems biology perspective, the biological functions arise from complex networks: in this context, concepts like molecular pathways, protein-protein interactions (PPIs, mathematical models and ontologies play an important role for dissecting such complexity. Results In this work we present the Genes-to-Systems Breast Cancer (G2SBC Database, a resource which integrates data about genes, transcripts and proteins reported in literature as altered in breast cancer cells. Beside the data integration, we provide an ontology based query system and analysis tools related to intracellular pathways, PPIs, protein structure and systems modelling, in order to facilitate the study of breast cancer using a multilevel perspective. The resource is available at the URL http://www.itb.cnr.it/breastcancer. Conclusions The G2SBC Database represents a systems biology oriented data integration approach devoted to breast cancer. By means of the analysis capabilities provided by the web interface, it is possible to overcome the limits of reductionist resources, enabling predictions that can lead to new experiments.

  6. Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method.

    Directory of Open Access Journals (Sweden)

    Luis Tobalina

    Full Text Available Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE, evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.

  7. Comparative indicators for cancer network management in England: Availability, characteristics and presentation

    Directory of Open Access Journals (Sweden)

    Coleman Michel P

    2008-02-01

    Full Text Available Abstract Background In 2000, the national cancer plan for England created 34 cancer networks, new organisational structures to coordinate services across populations varying between a half and three million people. We investigated the availability of data sets reflecting measures of structure, process and outcome that could be used to support network management. Methods We investigated the properties of national data sets relating to four common cancers – breast, colorectal, lung and prostate. We reviewed the availability and completeness of these data sets, identified leading items within each set and put them into tables of the 34 cancer networks. We also investigated methods of presentation. Results The Acute Hospitals Portfolio and the Cancer Standards Peer Review recorded structural characteristics at hospital and cancer service level. Process measures included Hospital Episode Statistics, recording admissions, and Hospital Waiting-List data. Patient outcome measures included the National Survey of Patient Satisfaction for cancer, and cancer survival, drawn from cancer registration. Data were drawn together to provide an exemplar indicator set a single network, and methods of graphical presentation were considered. Conclusion While not as yet used together in practice, comparative indicators are available within the National Health Service in England for use in performance assessment by cancer networks.

  8. Assessment of the psychological distress difficulties in patients with cancer using the national comprehensive cancer network rapid screening measure

    Institute of Scientific and Technical Information of China (English)

    Hamid Saeedi Saedi; Mona Koochak Pour; Emad Sabahi; Soodabeh Shahidsales

    2012-01-01

    Objective: Clinical guidelines like National Comprehensive Cancer Network Disease recommend routine psychological distress screening as a common problem among patients with cancer. The purpose of this study was to assess the prevalence of clinically significant emotional distress related to demographic and clinical association by standard distress thermometer (DT) within the patients lived in different regions of Gilan state, Iran. Methods: Participants (n = 256) completed the DT, rapid screening measure for distress and identified the presence or absence of 34 problems using the standardized checklist. Results: More than 59 percent of participants had more than 4 cut-off score for distress. The scores varied significantly in case of reported emotional source of distress, physical, physiological and total number of concerns (P < 0.001).DT scores more than four were more likely to report 22 of 32 problems on the problem list. In case of the practical and family problems, the main problems were related to child care and dealing with children, respectively. Moreover worrisome and nervousness were considered the prominent emotional problems in the list. Conclusion: Our result promise that distress thermometer measurement tool compare favorably with longer measures used to screening of distress in cancerous patients. Accompaniment of a psychologist expert in lethal or chronic disease consultation with the therapeutic team and training the rest of members of the team might be able to decrease the emotional distress problems of the cancerous patients.

  9. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

    Directory of Open Access Journals (Sweden)

    Jing Tang

    Full Text Available A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced

  10. CCNA Cisco Certified Network Associate Study Guide

    CERN Document Server

    Lammle, Todd

    2011-01-01

    Learn from the Best - Cisco Networking Authority Todd LammleWritten by Cisco networking authority Todd Lammle, this comprehensive guide has been completely updated to reflect the latest CCNA 640-802 exam. Todd's straightforward style provides lively examples, hands on and written labs, easy-to-understand analogies, and real-world scenarios that will not only help you prepare for the exam, but also give you a solid foundation as a Cisco networking professional.This Study Guide teaches you how toDescribe how a network worksConfigure, verify and troubleshoot a switch with VLANs and interswitch co

  11. Network analysis of microRNAs and their regulation in human ovarian cancer

    KAUST Repository

    Schmeier, Sebastian

    2011-11-03

    Background: MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.Results: We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network\\'s behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.Conclusions: We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance

  12. Chemotherapeutic prevention studies of prostate cancer

    DEFF Research Database (Denmark)

    Djavan, Bob; Zlotta, Alexandre; Schulman, Claude

    2004-01-01

    Despite advances in the detection and management of prostate cancer, this disease remains a major cause of morbidity and mortality in men. Increasing attention has focused on the role of chemoprevention for prostate cancer, ie the administration of agents that inhibit 1 or more steps in the natural...... history of prostate carcinogenesis. We review prostate cancer chemoprevention studies in Europe....

  13. Case study of virtual private network

    Science.gov (United States)

    Hernandez, Harold; Chung, Ping-Tsai

    2001-07-01

    In this study, business benefits for this Virtual Private Network (VPN) and protocols, techniques, equipments used in this VPN are reported. In addition, our design experience for fault tolerance, security and network management and administration on this VPN are showed. Finally, the issues for future planning of this VPN is addressed.

  14. Diagnostic Classification of Normal Persons and Cancer Patients by Using Neural Network Based on Trace Metal Contents in Serum Samples

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Artificial neural network with the back-propagation(BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There fore, the method can be used as an auxiliary means of the diagnosis of cancer.

  15. Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi zadeh

    2013-03-01

    Full Text Available Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer.

  16. Social network ties and inflammation in U.S. adults with cancer.

    Science.gov (United States)

    Yang, Yang Claire; Li, Ting; Frenk, Steven M

    2014-01-01

    The growing evidence linking social connectedness and chronic diseases such as cancer calls for a better understanding of the underlying biophysiological mechanisms. This study assessed the associations between social network ties and multiple measures of inflammation in a nationally representative sample of adults with a history of cancer (N = 1,075) from the National Health and Nutrition Examination Survey III (1988-94). Individuals with lower social network index (SNI) scores showed significantly greater inflammation marked by C-reactive protein and fibrinogen, adjusting for age and sex. Compared to fully socially integrated individuals (SNI = 4), those who were more socially isolated or had a SNI score of 3 or less exhibited increasingly elevated inflammation burdens. Specifically, the age- and sex-adjusted odds ratios (95%CI) for SNIs of 3, 2, and 0-1 were 1.49 (1.08, 2.06), 1.69 (1.21, 2.36), and 2.35 (1.62, 3.40), respectively (p < .001). Adjusting for other covariates attenuated these associations. The SNI gradients in the risks of inflammation were particularly salient for the lower socioeconomic status groups and remained significant after adjusting for other social, health behavioral, and illness factors. This study provided initial insights into the immunological pathways by which social connections are related to morbidity and mortality outcomes of cancer in particular and aging-related diseases in general.

  17. Integrative modelling of the influence of MAPK network on cancer cell fate decision.

    Directory of Open Access Journals (Sweden)

    Luca Grieco

    2013-10-01

    Full Text Available The Mitogen-Activated Protein Kinase (MAPK network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3 activating mutations.

  18. German second-opinion network for testicular cancer: sealing the leaky pipe between evidence and clinical practice.

    Science.gov (United States)

    Zengerling, Friedemann; Hartmann, Michael; Heidenreich, Axel; Krege, Susanne; Albers, Peter; Karl, Alexander; Weissbach, Lothar; Wagner, Walter; Bedke, Jens; Retz, Margitta; Schmelz, Hans U; Kliesch, Sabine; Kuczyk, Markus; Winter, Eva; Pottek, Tobias; Dieckmann, Klaus-Peter; Schrader, Andres Jan; Schrader, Mark

    2014-06-01

    In 2006, the German Testicular Cancer Study Group initiated an extensive evidence-based national second-opinion network to improve the care of testicular cancer patients. The primary aims were to reflect the current state of testicular cancer treatment in Germany and to analyze the project's effect on the quality of care delivered to testicular cancer patients. A freely available internet-based platform was developed for the exchange of data between the urologists seeking advice and the 31 second-opinion givers. After providing all data relevant to the primary treatment decision, urologists received a second opinion on their therapy plan within testicular cancer patient in Germany were submitted to second-opinion centers. Second-opinion centers can help to improve the implementation of evidence into clinical practice.

  19. A COMPARATIVE STUDY IN WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Hasan Al-Refai

    2014-02-01

    Full Text Available Sensor networks consist of a large number of small, low-powered wireless nodes with limited computation, communication, and sensing abilities, in a battery-powered sensor network, energy and communication bandwidth are a precious resources. Thus, there is a need to adapt the networking process to match the application in order to minimize the resources consumed and extend the life of the network. In this paper, we introduce a comparative study in different routing algorithms that propose vital solutions to the most important issues that should be taken into account when designing wireless network which are reliability, lifetime, communication bandwidth, transmission rand, and finally the limited energy issue, so we will introduce their algorithms and discuss how did they propose to solve such of these challenges and finally we will do some evaluation to each approach.

  20. Earth Regime Network Evolution Study (ERNESt)

    Science.gov (United States)

    Menrad, Bob

    2016-01-01

    Speaker and Presenter at the Lincoln Laboratory Communications Workshop on April 5, 2016 at the Massachusetts Institute of Technology Lincoln Laboratory in Lexington, MA. A visual presentation titled Earth Regimes Network Evolution Study (ERNESt).

  1. Immunoregulatory network and cancer-associated genes: molecular links and relevance to aging

    Directory of Open Access Journals (Sweden)

    Robi Tacutu

    2011-09-01

    Full Text Available Although different aspects of cancer immunity are a subject of intensive investigation, an integrative view on the possible molecular links between immunoregulators and cancer-associated genes has not yet been fully considered. In an attempt to get more insights on the problem, we analyzed these links from a network perspective. We showed that the immunoregulators could be organized into a miRNA-regulated PPI network-the immunoregulatory network. This network has numerous links with cancer, including (i cancerassociated immunoregulators, (ii direct and indirect protein-protein interactions (through the common protein partners, and (iii common miRNAs. These links may largely determine the interactions between the host's immunity and cancer, supporting the possibility for co-expression and post-transcriptional co-regulation of immunoregulatory and cancer genes. In addition, the connection between immunoregulation and cancer may lie within the realm of cancer-predisposing conditions, such as chronic inflammation and fibroproliferative repair. A gradual, age-related deterioration of the integrity and functionality of the immunoregulaory network could contribute to impaired immunity and generation of cancer-predisposing conditions.

  2. Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

    Science.gov (United States)

    Azim, Shafquat; Zubair, Haseeb; Srivastava, Sanjeev K; Bhardwaj, Arun; Zubair, Asif; Ahmad, Aamir; Singh, Seema; Khushman, Moh'd; Singh, Ajay P

    2016-06-29

    We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p networks by in silico analysis. Further analyses placed genes in our RNA sequencing-generated dataset to several canonical signalling pathways, such as cell-cycle control, DNA-damage and -repair responses, p53 and HIF1α. Importantly, we observed downregulation of the pancreatic adenocarcinoma signaling pathway in MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer.

  3. microRNAs and ceRNAs: RNA networks in pathogenesis of cancer

    Institute of Scientific and Technical Information of China (English)

    Xiangqian Su; Jiadi Xing; Zaozao Wang; Lei Chen; Ming Cui; Beihai Jiang

    2013-01-01

    microRNAs (miRNAs) are a class of endogenous,single-stranded non-coding RNAs of 20-23 nucleotides in length,functioning as negative regulators of gene expression at the post-transcriptional level.The dysregulation of miRNAs has been demonstrated to play critical roles in tumorigenesis,either through inhibiting tumor suppressor genes or activating oncogenes inappropriately.Besides their promising clinical applications in cancer diagnosis and treatment,recent studies have uncovered that miRNAs could act as a regulatory language,through which messenger RNAs,transcribed pseudogenes,and long noncoding RNAs crosstalk with each other and form a novel regulatory network.RNA transcripts involved in this network have been termed as competing endogenous RNAs (ceRNAs),since they influence each other's level by competing for the same pool of miRNAs through miRNA response elements (MREs) on their target transcripts.The discovery of miRNA-ceRNA network not only provides the possibility of an additional level of post-transcriptional regulation,but also dictates a reassessment of the existing regulatory pathways involved in cancer initiation and progression.

  4. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  5. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chandra Prasetyo Utomo

    2014-07-01

    Full Text Available Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.

  6. Building capacity for clinical research in developing countries: the INDOX Cancer Research Network experience.

    Science.gov (United States)

    Ali, Raghib; Finlayson, Alexander; Indox Cancer Research Network

    2012-01-01

    Transnational Organisations increasingly prioritise the need to support local research capacity in low and middle income countries in order that local priorities are addressed with due consideration of contextual issues. There remains limited evidence on the best way in which this should be done or the ways in which external agencies can support this process.We present an analysis of the learning from the INDOX Research Network, established in 2005 as a partnership between the Institute of Cancer Medicine at the University of Oxford and India's top nine comprehensive cancer centres. INDOX aims to enable Indian centres to conduct clinical research to the highest international standards; to ensure that trials are developed to address the specific needs of Indian patients by involving Indian investigators from the outset; and to provide the training to enable them to design and conduct their own studies. We report on the implementation, outputs and challenges of simultaneously trying to build capacity and deliver meaningful research output.

  7. Deriving margins in prostate cancer radiotherapy treatment: comparison of neural network and fuzzy logic models.

    Science.gov (United States)

    Mzenda, Bongile; Gegov, Alexander; Brown, David J; Petrov, Nedyalko

    2012-01-01

    This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.

  8. Distribution Network Design--literature study based

    OpenAIRE

    Li, Ang

    2012-01-01

    The focus of this research is companies' outbound distribution network design in supply chain management. Within the present competitive market, it is a fundamental importance for companies to achieve high level business performance with an effective supply chain. Outbound distribution network design as an important part in supply chain management, to a large extent decides whether companies can fulfill customers' requirement or not. Therefore, such a study is important for manufacturers and ...

  9. Social network analysis of study environment

    Directory of Open Access Journals (Sweden)

    Blaženka Divjak

    2010-06-01

    Full Text Available Student working environment influences student learning and achievement level. In this respect social aspects of students’ formal and non-formal learning play special role in learning environment. The main research problem of this paper is to find out if students' academic performance influences their position in different students' social networks. Further, there is a need to identify other predictors of this position. In the process of problem solving we use the Social Network Analysis (SNA that is based on the data we collected from the students at the Faculty of Organization and Informatics, University of Zagreb. There are two data samples: in the basic sample N=27 and in the extended sample N=52. We collected data on social-demographic position, academic performance, learning and motivation styles, student status (full-time/part-time, attitudes towards individual and teamwork as well as informal cooperation. Afterwards five different networks (exchange of learning materials, teamwork, informal communication, basic and aggregated social network were constructed. These networks were analyzed with different metrics and the most important were betweenness, closeness and degree centrality. The main result is, firstly, that the position in a social network cannot be forecast only by academic success and, secondly, that part-time students tend to form separate groups that are poorly connected with full-time students. In general, position of a student in social networks in study environment can influence student learning as well as her/his future employability and therefore it is worthwhile to be investigated.

  10. Module network inference from a cancer gene expression data set identifies microRNA regulated modules.

    Directory of Open Access Journals (Sweden)

    Eric Bonnet

    Full Text Available BACKGROUND: MicroRNAs (miRNAs are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. METHODOLOGY/PRINCIPAL FINDINGS: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. CONCLUSIONS/SIGNIFICANCE: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be

  11. Understanding cancer complexome using networks, spectral graph theory and multilayer framework

    Science.gov (United States)

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-01

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  12. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  13. Epidemiological studies of cancer in aircrew.

    Science.gov (United States)

    Hammer, Gaël P; Blettner, Maria; Zeeb, Hajo

    2009-10-01

    Exposure to cosmic ionising radiation, in addition to other specific occupational risks, is of concern to aircrew members. Epidemiological studies provide an objective way to assess the health of this occupational group. We systematically reviewed the epidemiological literature on health of aircrew members since 1990, focusing on cancer as the endpoint of interest. Sixty-five relevant publications were identified and reviewed. Whereas overall cancer incidence and mortality was generally lower than in the comparison population, consistently elevated risks were reported for breast cancer incidence in female aircrew members and for melanoma in both male and female aircrew members. Brain cancer was increased in some studies among pilots. Occasionally trends of increasing cancer mortality or incidence with increasing estimated radiation dose were reported. Ionising radiation is considered to contribute little if at all to the elevated risks for cancers among aircrew, whereas excess ultraviolet radiation is a probable cause of the increased melanoma risk.

  14. Loneliness May Sabotage Breast Cancer Survival: Study

    Science.gov (United States)

    ... gov/news/fullstory_162498.html Loneliness May Sabotage Breast Cancer Survival: Study Weak social ties linked to higher risk ... 2016 (HealthDay News) -- Loneliness may impede long-term breast cancer survival, a new study suggests. In the years after ...

  15. Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

    Science.gov (United States)

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

    2013-04-09

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

  16. A social network analysis of communication about hereditary nonpolyposis colorectal cancer genetic testing and family functioning.

    Science.gov (United States)

    Koehly, Laura M; Peterson, Susan K; Watts, Beatty G; Kempf, Kari K G; Vernon, Sally W; Gritz, Ellen R

    2003-04-01

    Hereditary cancers are relational diseases. A primary focus of research in the past has been the biological relations that exist within the families and how genes are passed along family lines. However, hereditary cancers are relational in a psychosocial sense, as well. They can impact communication relationships within a family, as well as support relationships among family members. Furthermore, the familial culture can affect an individual's participation in genetic counseling and testing endeavors. Our aims are (a) to describe the composition of familial networks, (b) to characterize the patterns of family functioning within families, (c) to analyze how these patterns relate to communications about genetic counseling and testing among family members, and (d) to identify influential family members. Specifically, we asked how the relationship between mutation status, kinship ties, and family functioning constructs, e.g., communication, cohesion, affective involvement, leadership, and conflict, was associated with discussions about genetic counseling and testing. We used social network analysis and random graph techniques to examine 783 dyadic relationships in 36 members of 5 hereditary nonpolyposis colorectal cancer (HNPCC) families interviewed from 1999-2000. Results suggest that in these five HNPCC families, two family members are more likely to discuss genetic counseling and testing if either one carries the mutation, if either one is a spouse or a first-degree relative of the other, or if the relationship is defined by positive cohesion, leadership, or lack of conflict. Furthermore, the family functioning patterns suggest that mothers tend to be the most influential persons in the family network. Results of this study suggest encouraging family members who act in the mother role to take a "team approach" with the family proband when discussing HNPCC risks and management with family members.

  17. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  18. Cognitive network structure: an experimental study

    CERN Document Server

    Guazzini, Andrea; Bagnoli, Franco; Carletti, Timoteo; Grotto, Rosapia Lauro

    2012-01-01

    In this paper we present first experimental results about a small group of people exchanging private and public messages in a virtual community. Our goal is the study of the cognitive network that emerges during a chat seance. We used the Derrida coefficient and the triangle structure under the working assumption that moods and perceived mutual affinity can produce results complementary to a full semantic analysis. The most outstanding outcome is the difference between the network obtained considering publicly exchanged messages and the one considering only privately exchanged messages: in the former case, the network is very homogeneous, in the sense that each individual interacts in the same way with all the participants, whilst in the latter the interactions among different agents are very heterogeneous, and are based on "the enemy of my enemy is my friend" strategy. Finally a recent characterization of the triangular cliques has been considered in order to describe the intimate structure of the network. E...

  19. 'Connecting tracks': exploring the roles of an Aboriginal women's cancer support network.

    Science.gov (United States)

    Cuesta-Briand, Beatriz; Bessarab, Dawn; Shahid, Shaouli; Thompson, Sandra C

    2016-11-01

    Aboriginal Australians are at higher risk of developing certain types of cancer and, once diagnosed, they have poorer outcomes than their non-Aboriginal counterparts. Lower access to cancer screening programmes, deficiencies in treatment and cultural barriers contribute to poor outcomes. Additional logistical factors affecting those living in rural areas compound these barriers. Cancer support groups have positive effects on people affected by cancer; however, there is limited evidence on peer-support programmes for Aboriginal cancer patients in Australia. This paper explores the roles played by an Aboriginal women's cancer support network operating in a regional town in Western Australia. Data were collected through semi-structured interviews with 24 participants including Aboriginal and mainstream healthcare service providers, and network members and clients. Interviews were audiotaped and transcribed verbatim. Transcripts were subjected to inductive thematic analysis. Connecting and linking people and services was perceived as the main role of the network. This role had four distinct domains: (i) facilitating access to cancer services; (ii) fostering social interaction; (iii) providing a culturally safe space; and (iv) building relationships with other agencies. Other network roles included providing emotional and practical support, delivering health education and facilitating engagement in cancer screening initiatives. Despite the network's achievements, unresolved tensions around role definition negatively impacted on the working relationship between the network and mainstream service providers, and posed a threat to the network's sustainability. Different perspectives need to be acknowledged and addressed in order to build strong, effective partnerships between service providers and Aboriginal communities. Valuing and honouring the Aboriginal approaches and expertise, and adopting an intercultural approach are suggested as necessary to the way forward.

  20. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

    Science.gov (United States)

    Kuperstein, I; Bonnet, E; Nguyen, H-A; Cohen, D; Viara, E; Grieco, L; Fourquet, S; Calzone, L; Russo, C; Kondratova, M; Dutreix, M; Barillot, E; Zinovyev, A

    2015-01-01

    Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses

  1. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  2. Genome network medicine: innovation to overcome huge challenges in cancer therapy.

    Science.gov (United States)

    Roukos, Dimitrios H

    2014-01-01

    The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored.

  3. Impact of the Cancer Prevention and Control Research Network: Accelerating the Translation of Research Into Practice.

    Science.gov (United States)

    Ribisl, Kurt M; Fernandez, Maria E; Friedman, Daniela B; Hannon, Peggy A; Leeman, Jennifer; Moore, Alexis; Olson, Lindsay; Ory, Marcia; Risendal, Betsy; Sheble, Laura; Taylor, Vicky M; Williams, Rebecca S; Weiner, Bryan J

    2017-03-01

    The Cancer Prevention and Control Research Network (CPCRN) is a thematic network dedicated to accelerating the adoption of evidence-based cancer prevention and control practices in communities by advancing dissemination and implementation science. Funded by the Centers for Disease Control and Prevention and National Cancer Institute, CPCRN has operated at two levels: Each participating network center conducts research projects with primarily local partners as well as multicenter collaborative research projects with state and national partners. Through multicenter collaboration, thematic networks leverage the expertise, resources, and partnerships of participating centers to conduct research projects collectively that might not be feasible individually. Although multicenter collaboration is often advocated, it is challenging to promote and assess. Using bibliometric network analysis and other graphical methods, this paper describes CPCRN's multicenter publication progression from 2004 to 2014. Searching PubMed, Scopus, and Web of Science in 2014 identified 249 peer-reviewed CPCRN publications involving two or more centers out of 6,534 total. The research and public health impact of these multicenter collaborative projects initiated by CPCRN during that 10-year period were then examined. CPCRN established numerous workgroups around topics such as: 2-1-1, training and technical assistance, colorectal cancer control, federally qualified health centers, cancer survivorship, and human papillomavirus. This paper discusses the challenges that arise in promoting multicenter collaboration and the strategies that CPCRN uses to address those challenges. The lessons learned should broadly interest those seeking to promote multisite collaboration to address public health problems, such as cancer prevention and control.

  4. Boolean Network Model for Cancer Pathways: Predicting Carcinogenesis and Targeted Therapy Outcomes

    Science.gov (United States)

    Fumiã, Herman F.; Martins, Marcelo L.

    2013-01-01

    A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns – attractors – dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication. PMID:23922675

  5. Characterization of the Usage of the Serine Metabolic Network in Human Cancer

    Directory of Open Access Journals (Sweden)

    Mahya Mehrmohamadi

    2014-11-01

    Full Text Available The serine, glycine, one-carbon (SGOC metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

  6. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.

    2015-01-01

    Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase......-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks....

  7. Comparison of molecular signatures in large scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate

    Directory of Open Access Journals (Sweden)

    Rajat Suvra Banik

    2016-04-01

    Full Text Available Background Cancer, the disease of intricateness, has remained beyond our complete perception so far. Network systems biology (termed NSB is one of the most recent approaches to understand the unsolved problems of cancer development. From this perspective, differential protein networks (PINs have been developed based on the expression and interaction data of brain, cervix, lung, ovary and prostate for normal and cancer conditions. Methods Differential expression database GeneHub-GEPIS and interaction database STRING were applied for primary data retrieval. Cytoscape platform and related plugins named network analyzer; MCODE and ModuLand were used for visualization of complex networks and subsequent analysis. Results Significant differences were observedamong different common network parameters between normal and cancer states. Moreover, molecular complex numbers and overlapping modularization found to be varying significantly between normal and cancerous tissues. The number of the ranked molecular complex and the nodes involved in the overlapping modules were meaningfully higher in cancer condition.We identified79 commonly up regulated and 6 down regulated proteins in all five tissues. Number of nodes, edges; multi edge node pair, and average number of neighbor are found with significant fluctuations in case of cervix and ovarian tissues.Cluster analysis showed that the association of Myc and Cdk4 proteins is very close with other proteins within the network.Cervix and ovarian tissue showed higher increment of the molecular complex number and overlapping module network during cancer in comparison to normal state. Conclusions The differential molecular signatures identified from the work can be studied further to understand the cancer signaling process, and potential therapeutic and detection approach. [Biomed Res Ther 2016; 3(4.000: 605-615

  8. Networking and Information Technology Workforce Study: Final Report

    Data.gov (United States)

    Networking and Information Technology Research and Development, Executive Office of the President — This report presents the results of a study of the global Networking and Information Technology NIT workforce undertaken for the Networking and Information...

  9. Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

    Science.gov (United States)

    Myte, Robin; Gylling, Björn; Häggström, Jenny; Schneede, Jörn; Magne Ueland, Per; Hallmans, Göran; Johansson, Ingegerd; Palmqvist, Richard; Van Guelpen, Bethany

    2017-01-01

    The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease. PMID:28233834

  10. A new mode of organizing in health care? Governmentality and managed networks in cancer services in England.

    Science.gov (United States)

    Ferlie, Ewan; McGivern, Gerry; Fitzgerald, Louise

    2012-02-01

    We explore the argument that a new mode of health care organizing is emerging which moves beyond the established professional dominance versus New Public Management (NPM) debate. We review Foucault's work on 'governmentality', as applied to health care organizations. We specify two specific Foucauldian themes (the power/knowledge nexus in Evidence Based Medicine (EBM); and the technologies of the clinical managerial self) to analyse organizing in the English cancer services field. We introduce two qualitative case studies of Managed Cancer Networks. We suggest their governance can be fruitfully seen through a 'governmentality' lens. We consider implications for developing Foucauldian analysis of health care organizations.

  11. [Pathologists and the French network of expertise on rare cancers ENT: The REFCORpath].

    Science.gov (United States)

    Badoual, Cécile; Baglin, Anne-Catherine; Wassef, Michel; Thariat, Juliette; Reyt, Emile; Janot, François; Baujat, Bertrand

    2014-02-01

    Aerodigestive tract tumors are very diverse, either in terms of location, or histologically. Also, this heterogeneity poses particular problems for the histological diagnosis but also for the establishment of the most appropriate treatment. Thus, the network REFCOR (réseau d'expertise français sur les cancers ORL rares/French expert network on rare ENT cancers) was created to better understand these issues, by proposing an epidemiological and diagnostic approach with research collaborations. This network is dedicated to all primary malignant tumors of the salivary glands, ear, nasal cavity and sinuses and all head and neck malignancies other than conventional squamous cell carcinoma. The REFCORpath network consists of expert pathologists and offers, through a network of scanned images, a second opinion or even a third.

  12. Social Networks and Youngspeak in Study Abroad

    Science.gov (United States)

    Fernandez, Julieta

    2013-01-01

    Interactions with experienced L2 speakers can have a positive effect on study abroad (SA) students' language acquisition (e.g., development in informal vocabulary use, Schauer, 2009). Many SA students, however, experience difficulties in establishing social networks in Latin America (e.g., Isabelli-Garcia, 2006). SA experience, therefore, cannot…

  13. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

    Directory of Open Access Journals (Sweden)

    Taosheng Xu

    Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

  14. Tea and cancer prevention: epidemiological studies.

    Science.gov (United States)

    Yuan, Jian-Min; Sun, Canlan; Butler, Lesley M

    2011-08-01

    Experimental studies have consistently shown the inhibitory activities of tea extracts on tumorigenesis in multiple model systems. Epidemiological studies, however, have produced inconclusive results in humans. A comprehensive review was conducted to assess the current knowledge on tea consumption and risk of cancers in humans. In general, consumption of black tea was not associated with lower risk of cancer. High intake of green tea was consistently associated with reduced risk of upper gastrointestinal tract cancers after sufficient control for confounders. Limited data support a protective effect of green tea on lung and hepatocellular carcinogenesis. Although observational studies do not support a beneficial role of tea intake on prostate cancer risk, phase II clinical trials have demonstrated an inhibitory effect of green tea extract against the progression of prostate pre-malignant lesions. Green tea may exert beneficial effects against mammary carcinogenesis in premenopausal women and recurrence of breast cancer. There is no sufficient evidence that supports a protective role of tea intake on the development of cancers of the colorectum, pancreas, urinary tract, glioma, lymphoma, and leukemia. Future prospective observational studies with biomarkers of exposure and phase III clinical trials are required to provide definitive evidence for the hypothesized beneficial effect of tea consumption on cancer formation in humans.

  15. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  16. Utilizing Data from Cancer Patient & Survivor Studies

    Science.gov (United States)

    Utilizing Data from Cancer Patient & Survivor Studies and Understanding the Current State of Knowledge and Developing Future Research Priorities, a 2011 workshop sponsored by the Epidemiology and Genomics Research Program.

  17. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis.

    Science.gov (United States)

    Clarke, Colin; Madden, Stephen F; Doolan, Padraig; Aherne, Sinead T; Joyce, Helena; O'Driscoll, Lorraine; Gallagher, William M; Hennessy, Bryan T; Moriarty, Michael; Crown, John; Kennedy, Susan; Clynes, Martin

    2013-10-01

    Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

  18. Haploinsufficiency networks identify targetable patterns of allelic deficiency in low mutation ovarian cancer

    Science.gov (United States)

    Delaney, Joe Ryan; Patel, Chandni B.; Willis, Katelyn McCabe; Haghighiabyaneh, Mina; Axelrod, Joshua; Tancioni, Isabelle; Lu, Dan; Bapat, Jaidev; Young, Shanique; Cadassou, Octavia; Bartakova, Alena; Sheth, Parthiv; Haft, Carley; Hui, Sandra; Saenz, Cheryl; Schlaepfer, David D.; Harismendy, Olivier; Stupack, Dwayne G.

    2017-01-01

    Identification of specific oncogenic gene changes has enabled the modern generation of targeted cancer therapeutics. In high-grade serous ovarian cancer (OV), the bulk of genetic changes is not somatic point mutations, but rather somatic copy-number alterations (SCNAs). The impact of SCNAs on tumour biology remains poorly understood. Here we build haploinsufficiency network analyses to identify which SCNA patterns are most disruptive in OV. Of all KEGG pathways (N=187), autophagy is the most significantly disrupted by coincident gene deletions. Compared with 20 other cancer types, OV is most severely disrupted in autophagy and in compensatory proteostasis pathways. Network analysis prioritizes MAP1LC3B (LC3) and BECN1 as most impactful. Knockdown of LC3 and BECN1 expression confers sensitivity to cells undergoing autophagic stress independent of platinum resistance status. The results support the use of pathway network tools to evaluate how the copy-number landscape of a tumour may guide therapy. PMID:28198375

  19. A network medicine approach to build a comprehensive atlas for the prognosis of human cancer.

    Science.gov (United States)

    Zhang, Fan; Ren, Chunyan; Lau, Kwun Kit; Zheng, Zihan; Lu, Geming; Yi, Zhengzi; Zhao, Yongzhong; Su, Fei; Zhang, Shaojun; Zhang, Bin; Sobie, Eric A; Zhang, Weijia; Walsh, Martin J

    2016-11-01

    The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified.

  20. Social networks and cooperation: a bibliometric study

    Directory of Open Access Journals (Sweden)

    Ana Paula Lopes

    2013-05-01

    Full Text Available The social network analysis involves social and behavioral science. The decentralization of productive activities, such as the formation of "network organizations" as a result of downsizing of large corporate structures of the past, marked by outsoucing and formation of alliances, shows the importance of this theme. The main objective of this paper is to analyze the theory of cooperation and social networks over a period of 24 years. For this, was performed a bibliometric study with content analysis. The database chosen for the initial sample search was ISI Web of Science. The search topics were “social network” and “cooperation”. Were analyzed 97 articles and their references, through networks of citations. The main identified research groups dealing with issues related to trust, strategic alliances, natural cooperation, game theory, social capital, intensity of interaction, reciprocity and innovation. It was found that the publications occurred in a large number of journals, which indicates that the theme is multidisciplinary, and only five journals published at least three articles. Although the first publication has occurred in 1987, was from 2006 that the publications effectively increased. The areas most related to the theme of the research were performance, evolution, management, graphics, model and game theory.

  1. Radiotherapy waiting times for women with breast cancer: a population-based cohort study

    Directory of Open Access Journals (Sweden)

    Sainsbury Richard

    2007-05-01

    Full Text Available Abstract Background Waiting times for cancer patients are a national priority in the UK. Previous studies have shown variation between cancer networks in the time between diagnosis and start of radiotherapy for all cancer patients. Studies of the relationship between delay in receiving treatment and survival of breast cancer patients have been inconsistent. This study aimed to examine factors associated with waiting times for radiotherapy for breast cancer patients. Methods 35,354 women resident in South East England and diagnosed with breast cancer between 1992 and 2001 who received radiotherapy within six months of diagnosis were identified from the Thames Cancer Registry. Time to radiotherapy was measured from either the date of diagnosis or the start of the previous treatment, whichever was shorter. Unadjusted and adjusted logistic regression models were fitted to examine whether patients received radiotherapy within 60 days of their diagnosis or previous treatment. Results The adjusted proportions of patients receiving radiotherapy within 60 days varied significantly between different cancer networks (range: 43% to 81%, and decreased from 68% in 1992 to 33% in 2001. After adjustment there was no association between deprivation of area of residence, age or stage and radiotherapy wait. Median time waited to radiotherapy increased over the study period whether measured from the start of chemotherapy, hormone therapy, surgery or the date of diagnosis. Conclusion This study covered a period of time before the investment following the Cancer Plan of 2000. Results are consistent with other findings suggesting variation between cancer networks and increasing waits over time. Further studies should examine different methods of measuring waiting time, the causes and consequences of waits for radiotherapy and the effect of current initiatives and investments.

  2. System-scale network modeling of cancer using EPoC.

    Science.gov (United States)

    Abenius, Tobias; Jörnsten, Rebecka; Kling, Teresia; Schmidt, Linnéa; Sánchez, José; Nelander, Sven

    2012-01-01

    One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.

  3. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    Science.gov (United States)

    Creixell, Pau; Schoof, Erwin M.; Simpson, Craig D.; Longden, James; Miller, Chad J.; Lou, Hua Jane; Perryman, Lara; Cox, Thomas R.; Zivanovic, Nevena; Palmeri, Antonio; Wesolowska-Andersen, Agata; Helmer-Citterich, Manuela; Ferkinghoff-Borg, Jesper; Itamochi, Hiroaki; Bodenmiller, Bernd; Erler, Janine T.; Turk, Benjamin E.; Linding, Rune

    2015-01-01

    Summary Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks. PMID:26388441

  4. Neural networks improve brain cancer detection with Raman spectroscopy in the presence of light artifacts

    Science.gov (United States)

    Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

    2016-03-01

    It is often difficult to identify cancer tissue during brain cancer (glioma) surgery. Gliomas invade into areas of normal brain, and this cancer invasion is frequently not detected using standard preoperative magnetic resonance imaging (MRI). This results in enduring invasive cancer following surgery and leads to recurrence. A hand-held Raman spectroscopy is able to rapidly detect cancer invasion in patients with grade 2-4 gliomas. However, ambient light sources can produce spectral artifacts which inhibit the ability to distinguish between cancer and normal tissue using the spectral information available. To address this issue, we have demonstrated that artificial neural networks (ANN) can accurately classify invasive cancer versus normal brain tissue, even when including measurements with significant spectral artifacts from external light sources. The non-parametric and adaptive model used by ANN makes it suitable for detecting complex non-linear spectral characteristics associated with different tissues and the confounding presence of light artifacts. The use of ANN for brain cancer detection with Raman spectroscopy, in the presence of light artifacts, improves the robustness and clinical translation potential for intraoperative use. Integration with the neurosurgical workflow is facilitated by accounting for the effect of light artifacts which may occur, due to operating room lights, neuronavigation systems, windows, or other light sources. The ability to rapidly detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery, and thereby improve patient survival.

  5. An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells

    Directory of Open Access Journals (Sweden)

    Nam Seungyoon

    2012-12-01

    Full Text Available Abstract Background A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an “integrative network”. We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity. Results Based on the integrative network, we extracted “substructures” (network clusters representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells compared to drug sensitive state (parental MCF7 cells. We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222. Conclusions By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In

  6. Assessing needs and assets for building a regional network infrastructure to reduce cancer related health disparities.

    Science.gov (United States)

    Wells, Kristen J; Lima, Diana S; Meade, Cathy D; Muñoz-Antonia, Teresita; Scarinci, Isabel; McGuire, Allison; Gwede, Clement K; Pledger, W Jack; Partridge, Edward; Lipscomb, Joseph; Matthews, Roland; Matta, Jaime; Flores, Idhaliz; Weiner, Roy; Turner, Timothy; Miele, Lucio; Wiese, Thomas E; Fouad, Mona; Moreno, Carlos S; Lacey, Michelle; Christie, Debra W; Price-Haywood, Eboni G; Quinn, Gwendolyn P; Coppola, Domenico; Sodeke, Stephen O; Green, B Lee; Lichtveld, Maureen Y

    2014-06-01

    Significant cancer health disparities exist in the United States and Puerto Rico. While numerous initiatives have been implemented to reduce cancer disparities, regional coordination of these efforts between institutions is often limited. To address cancer health disparities nation-wide, a series of regional transdisciplinary networks through the Geographic Management Program (GMaP) and the Minority Biospecimen/Biobanking Geographic Management Program (BMaP) were established in six regions across the country. This paper describes the development of the Region 3 GMaP/BMaP network composed of over 100 investigators from nine institutions in five Southeastern states and Puerto Rico to develop a state-of-the-art network for cancer health disparities research and training. We describe a series of partnership activities that led to the formation of the infrastructure for this network, recount the participatory processes utilized to develop and implement a needs and assets assessment and implementation plan, and describe our approach to data collection. Completion, by all nine institutions, of the needs and assets assessment resulted in several beneficial outcomes for Region 3 GMaP/BMaP. This network entails ongoing commitment from the institutions and institutional leaders, continuous participatory and engagement activities, and effective coordination and communication centered on team science goals.

  7. Curcumin AntiCancer Studies in Pancreatic Cancer

    Science.gov (United States)

    Bimonte, Sabrina; Barbieri, Antonio; Leongito, Maddalena; Piccirillo, Mauro; Giudice, Aldo; Pivonello, Claudia; de Angelis, Cristina; Granata, Vincenza; Palaia, Raffaele; Izzo, Francesco

    2016-01-01

    Pancreatic cancer (PC) is one of the deadliest cancers worldwide. Surgical resection remains the only curative therapeutic treatment for this disease, although only the minority of patients can be resected due to late diagnosis. Systemic gemcitabine-based chemotherapy plus nab-paclitaxel are used as the gold-standard therapy for patients with advanced PC; although this treatment is associated with a better overall survival compared to the old treatment, many side effects and poor results are still present. Therefore, new alternative therapies have been considered for treatment of advanced PC. Several preclinical studies have demonstrated that curcumin, a naturally occurring polyphenolic compound, has anticancer effects against different types of cancer, including PC, by modulating many molecular targets. Regarding PC, in vitro studies have shown potent cytotoxic effects of curcumin on different PC cell lines including MiaPaCa-2, Panc-1, AsPC-1, and BxPC-3. In addition, in vivo studies on PC models have shown that the anti-proliferative effects of curcumin are caused by the inhibition of oxidative stress and angiogenesis and are due to the induction of apoptosis. On the basis of these results, several researchers tested the anticancer effects of curcumin in clinical trials, trying to overcome the poor bioavailability of this agent by developing new bioavailable forms of curcumin. In this article, we review the results of pre-clinical and clinical studies on the effects of curcumin in the treatment of PC. PMID:27438851

  8. Curcumin AntiCancer Studies in Pancreatic Cancer

    Directory of Open Access Journals (Sweden)

    Sabrina Bimonte

    2016-07-01

    Full Text Available Pancreatic cancer (PC is one of the deadliest cancers worldwide. Surgical resection remains the only curative therapeutic treatment for this disease, although only the minority of patients can be resected due to late diagnosis. Systemic gemcitabine-based chemotherapy plus nab-paclitaxel are used as the gold-standard therapy for patients with advanced PC; although this treatment is associated with a better overall survival compared to the old treatment, many side effects and poor results are still present. Therefore, new alternative therapies have been considered for treatment of advanced PC. Several preclinical studies have demonstrated that curcumin, a naturally occurring polyphenolic compound, has anticancer effects against different types of cancer, including PC, by modulating many molecular targets. Regarding PC, in vitro studies have shown potent cytotoxic effects of curcumin on different PC cell lines including MiaPaCa-2, Panc-1, AsPC-1, and BxPC-3. In addition, in vivo studies on PC models have shown that the anti-proliferative effects of curcumin are caused by the inhibition of oxidative stress and angiogenesis and are due to the induction of apoptosis. On the basis of these results, several researchers tested the anticancer effects of curcumin in clinical trials, trying to overcome the poor bioavailability of this agent by developing new bioavailable forms of curcumin. In this article, we review the results of pre-clinical and clinical studies on the effects of curcumin in the treatment of PC.

  9. Delineating transcriptional networks of prognostic gene signatures refines treatment recommendations for lymph node-negative breast cancer patients.

    Science.gov (United States)

    Lanigan, Fiona; Brien, Gerard L; Fan, Yue; Madden, Stephen F; Jerman, Emilia; Maratha, Ashwini; Aloraifi, Fatima; Hokamp, Karsten; Dunne, Eiseart J; Lohan, Amanda J; Flanagan, Louise; Garbe, James C; Stampfer, Martha R; Fridberg, Marie; Jirstrom, Karin; Quinn, Cecily M; Loftus, Brendan; Gallagher, William M; Geraghty, James; Bracken, Adrian P

    2015-09-01

    The majority of women diagnosed with lymph node-negative breast cancer are unnecessarily treated with damaging chemotherapeutics after surgical resection. This highlights the importance of understanding and more accurately predicting patient prognosis. In the present study, we define the transcriptional networks regulating well-established prognostic gene expression signatures. We find that the same set of transcriptional regulators consistently lie upstream of both 'prognosis' and 'proliferation' gene signatures, suggesting that a central transcriptional network underpins a shared phenotype within these signatures. Strikingly, the master transcriptional regulators within this network predict recurrence risk for lymph node-negative breast cancer better than currently used multigene prognostic assays, particularly in estrogen receptor-positive patients. Simultaneous examination of p16(INK4A) expression, which predicts tumours that have bypassed cellular senescence, revealed that intermediate levels of p16(INK4A) correlate with an intact pRB pathway and improved survival. A combination of these master transcriptional regulators and p16(INK4A), termed the OncoMasTR score, stratifies tumours based on their proliferative and senescence capacity, facilitating a clearer delineation of lymph node-negative breast cancer patients at high risk of recurrence, and thus requiring chemotherapy. Furthermore, OncoMasTR accurately classifies over 60% of patients as 'low risk', an improvement on existing prognostic assays, which has the potential to reduce overtreatment in early-stage patients. Taken together, the present study provides new insights into the transcriptional regulation of cellular proliferation in breast cancer and provides an opportunity to enhance and streamline methods of predicting breast cancer prognosis.

  10. Network Medicine Strikes a Blow against Breast Cancer

    DEFF Research Database (Denmark)

    Erler, Janine Terra; Linding, Rune

    2012-01-01

    Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative...

  11. NIH study confirms risk factors for male breast cancer

    Science.gov (United States)

    Pooled data from studies of about 2,400 men with breast cancer and 52,000 men without breast cancer confirmed that risk factors for male breast cancer include obesity, a rare genetic condition called Klinefelter syndrome, and gynecomastia.

  12. Study of the structure and dynamics of complex biological networks

    Science.gov (United States)

    Samal, Areejit

    2008-12-01

    In this thesis, we have studied the large scale structure and system level dynamics of certain biological networks using tools from graph theory, computational biology and dynamical systems. We study the structure and dynamics of large scale metabolic networks inside three organisms, Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus. We also study the dynamics of the large scale genetic network controlling E. coli metabolism. We have tried to explain the observed system level dynamical properties of these networks in terms of their underlying structure. Our studies of the system level dynamics of these large scale biological networks provide a different perspective on their functioning compared to that obtained from purely structural studies. Our study also leads to some new insights on features such as robustness, fragility and modularity of these large scale biological networks. We also shed light on how different networks inside the cell such as metabolic networks and genetic networks are interrelated to each other.

  13. Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

    Full Text Available Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+ breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.

  14. AHNS Series - Do you know your guidelines? Principles of treatment for nasopharyngeal cancer: A review of the National Comprehensive Cancer Network guidelines.

    Science.gov (United States)

    Gooi, Zhen; Richmon, Jeremy; Agrawal, Nishant; Blair, Elizabeth; Portugal, Louis; Vokes, Everett; Seiwert, Tanguy; de Souza, Jonas; Saloura, Vassiliki; Haraf, Daniel; Goldenberg, David; Chan, Jason

    2017-02-01

    This article is a continuation of the "Do You Know Your Guidelines" series, an initiative of the American Head and Neck Society's Education Committee to increase awareness of current best practices pertaining to head and neck cancer. The National Comprehensive Cancer Network guidelines for the management of nasopharyngeal cancer are reviewed here in a systematic fashion. These guidelines outline the workup, treatment and surveillance of patients with nasopharyngeal cancer. © 2016 Wiley Periodicals, Inc. Head Neck 39: 201-205, 2017.

  15. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

    Directory of Open Access Journals (Sweden)

    Kurt W Kohn

    Full Text Available Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1; interactions at adherens junctions (CDH1, ADAP1, CAMSAP3; interactions at desmosomes (PPL, PKP3, JUP; transcription regulation of cell-cell junction complexes (GRHL1 and 2; epithelial RNA splicing regulators (ESRP1 and 2; epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B; epithelial Ca(+2 signaling (ATP2C2, S100A14, BSPRY; terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2; maintenance of apico-basal polarity (RAB25, LLGL2, EPN3. The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

  16. The Cervix Cancer Research Network (CCRN: Increasing access to cancer clinical trials in low- and middle-income countries

    Directory of Open Access Journals (Sweden)

    Gita eSuneja

    2015-02-01

    Full Text Available Introduction: The burden of cervical cancer is large and growing in developing countries, due in large part to limited access to screening services and lack of human papillomavirus (HPV vaccination. In spite of modern advances in diagnostic and therapeutic modalities, outcomes from cervical cancer have not markedly improved in recent years. Novel clinical trials are urgently needed to improve outcomes from cervical cancer worldwide. Methods: The Cervix Cancer Research Network (CCRN, a subsidiary of the Gynecologic Cancer InterGroup (GCIG, is a multi-national, multi-institutional consortium of physicians and scientists focused on improving cervical cancer outcomes worldwide by making cancer clinical trials available in low-, middle-, and high-income countries. Standard operating procedures for participation in CCRN include a pre-qualifying questionnaire to evaluate clinical activities and research infrastructure, followed by a site visit. Once a site is approved, they may choose to participate in one of four currently accruing clinical trials.Results: To date, 13 different CCRN site visits have been performed. Of these 13 sites visited, 10 have been approved as CCRN sites including Tata Memorial Hospital, India; Bangalore, India; Trivandrum, India; Ramathibodi, Thailand; Siriaj, Thailand; Pramongkutklao, Thailand; Ho Chi Minh, Vietnam; Blokhin Russian Cancer Research Center; the Hertzen Moscow Cancer Research Institute; and the Russian Scientific Center of Roentgenoradiology. The four currently accruing clinical trials are TACO, OUTBACK, INTERLACE, and SHAPE.Discussion: The CCRN has successfully enrolled 10 sites in developing countries to participate in four randomized clinical trials. The primary objectives are to provide novel therapeutics to regions with the greatest need and to improve the validity and generalizability of clinical trial results by enrolling a diverse sample of patients.

  17. Cancer

    Science.gov (United States)

    ... cancer Non-Hodgkin lymphoma Ovarian cancer Pancreatic cancer Testicular cancer Thyroid cancer Uterine cancer Symptoms Symptoms of cancer ... tumor Obesity Pancreatic cancer Prostate cancer Stomach cancer Testicular cancer Throat or larynx cancer Thyroid cancer Patient Instructions ...

  18. Studies on retrospective analysis of leading primary cancers and improvement of cancer treatment method in Korea cancer center hospital

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jong In; Lee, Kang Hyun; Choi, Soo Yong; Kim, Ki Wha; Kang, Sung Mok

    2000-12-01

    a. Retrospective studies included cancers of the stomach, breast, bladder, salivary gland, thyroid, esophagus, endometrium and ovary. (1) Study cancers were analyzed about clinical characteristics, prognostic factors influenced on survival time, survival rate, etc. (2) Among 5,305 study patients, 1,405(26.5%) were identified with death, 3,485(65.7%) were alive and 415(7.8%) were not identified. b. Prospective studies included 10 subjects such as bladder cancer, retinoblastoma, malignant patients, gastric cancer, uterine cervix cancer and ovary cancer. We are continuing registering eligible study patients. c. Results for 11 papers were published at the journal. d. We established follow-up system in order to identify the survival for study subjects through National Statistical Office, Government Provincial Office and Cancer Registration System at Korea Cancer Center Hospital. e. At present, we are establishing computerized registration system about case report form for study cancers.

  19. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    , with genomic modifications giving rise to differential protein dynamics, ultimately resulting in disease. The exact molecular signaling networks underlying specific disease phenotypes remain elusive, as the definition thereof requires extensive analysis of not only the genomic and proteomic landscapes within...

  20. Analysis of tumor heterogeneity and cancer gene networks using deep sequencing of MMTV-induced mouse mammary tumors.

    Directory of Open Access Journals (Sweden)

    Christiaan Klijn

    Full Text Available Cancer develops through a multistep process in which normal cells progress to malignant tumors via the evolution of their genomes as a result of the acquisition of mutations in cancer driver genes. The number, identity and mode of action of cancer driver genes, and how they contribute to tumor evolution is largely unknown. This study deployed the Mouse Mammary Tumor Virus (MMTV as an insertional mutagen to find both the driver genes and the networks in which they function. Using deep insertion site sequencing we identified around 31000 retroviral integration sites in 604 MMTV-induced mammary tumors from mice with mammary gland-specific deletion of Trp53, Pten heterozygous knockout mice, or wildtype strains. We identified 18 known common integration sites (CISs and 12 previously unknown CISs marking new candidate cancer genes. Members of the Wnt, Fgf, Fgfr, Rspo and Pdgfr gene families were commonly mutated in a mutually exclusive fashion. The sequence data we generated yielded also information on the clonality of insertions in individual tumors, allowing us to develop a data-driven model of MMTV-induced tumor development. Insertional mutations near Wnt and Fgf genes mark the earliest "initiating" events in MMTV induced tumorigenesis, whereas Fgfr genes are targeted later during tumor progression. Our data shows that insertional mutagenesis can be used to discover the mutational networks, the timing of mutations, and the genes that initiate and drive tumor evolution.

  1. Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients.

    Science.gov (United States)

    Chen, Jie; Pan, Qin-Shi; Hong, Wan-Dong; Pan, Jingye; Zhang, Wen-Hui; Xu, Gang; Wang, Yu-Min

    2014-01-01

    Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

  2. The intellectual property management for data sharing in a German liver cancer research network.

    Science.gov (United States)

    He, Shan; Ganzinger, Matthias; Knaup, Petra

    2012-01-01

    Sharing data in biomedical research networks has great potential benefits including efficient use of resources, avoiding duplicate experiments and promoting collaboration. However, concerns from data producers about difficulties of getting proper acknowledgement for their contributions are becoming obstacles for efficient and network wide data sharing in reality. Effective and convenient ways of intellectual property management and acknowledging contributions to the data producers are required. This paper analyzed the system requirements for intellectual property management in a German liver cancer research network and proposed solutions for facilitating acknowledgement of data contributors using informatics tools instead of pure policy level strategies.

  3. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.

  4. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  5. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.

    Science.gov (United States)

    Bashashati, Ali; Haffari, Gholamreza; Ding, Jiarui; Ha, Gavin; Lui, Kenneth; Rosner, Jamie; Huntsman, David G; Caldas, Carlos; Aparicio, Samuel A; Shah, Sohrab P

    2012-12-22

    Simultaneous interrogation of tumor genomes and transcriptomes is underway in unprecedented global efforts. Yet, despite the essential need to separate driver mutations modulating gene expression networks from transcriptionally inert passenger mutations, robust computational methods to ascertain the impact of individual mutations on transcriptional networks are underdeveloped. We introduce a novel computational framework, DriverNet, to identify likely driver mutations by virtue of their effect on mRNA expression networks. Application to four cancer datasets reveals the prevalence of rare candidate driver mutations associated with disrupted transcriptional networks and a simultaneous modulation of oncogenic and metabolic networks, induced by copy number co-modification of adjacent oncogenic and metabolic drivers. DriverNet is available on Bioconductor or at http://compbio.bccrc.ca/software/drivernet/.

  6. Regulators of genetic risk of breast cancer identified by integrative network analysis.

    Science.gov (United States)

    Castro, Mauro A A; de Santiago, Ines; Campbell, Thomas M; Vaughn, Courtney; Hickey, Theresa E; Ross, Edith; Tilley, Wayne D; Markowetz, Florian; Ponder, Bruce A J; Meyer, Kerstin B

    2016-01-01

    Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

  7. Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer.

    Science.gov (United States)

    Porter, Christopher R; Crawford, E David

    2003-10-01

    Arguably the most important step in the prognosis of prostate cancer is early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the United States, resulting in the detection of 200,000 new cases per year. Unfortunately, the urologist's ability to diagnose prostate cancer has not kept pace with therapeutic advances; currently, many men are facing the need for prostate biopsy with the likelihood that the result will be inconclusive. This paper will focus on the tools available to assist the clinician in predicting the outcome of the prostate needle biopsy. We will examine the use of "machine learning" models (artificial intelligence), in the form of artificial neural networks (ANNs), to predict prostate biopsy outcomes using prebiopsy variables. Currently, six validated predictive models are available. Of these, five are machine learning models, and one is based on logistic regression. The role of ANNs in providing valuable predictive models to be used in conjunction with TRUS appears promising. In the few studies that have compared machine learning to traditional statistical methods, ANN and logistic regression appear to function equivalently when predicting biopsy outcome. With the introduction of more complex prebiopsy variables, ANNs are in a commanding position for use in predictive models. Easy and immediate physician access to these models will be imperative if their full potential is to be realized.

  8. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Science.gov (United States)

    Siletz, Anaar; Schnabel, Michael; Kniazeva, Ekaterina; Schumacher, Andrew J; Shin, Seungjin; Jeruss, Jacqueline S; Shea, Lonnie D

    2013-01-01

    The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  9. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Directory of Open Access Journals (Sweden)

    Anaar Siletz

    Full Text Available The epithelial-mesenchymal transition (EMT is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  10. Understanding and Targeting Cell Growth Networks in Breast Cancer

    Science.gov (United States)

    2013-04-01

    pathology  187(1):112-­‐126.   2.   Sherr  CJ  &  Weber  JD  (2000)  The  ARF/p53  pathway.  Current...J,  Solimini  NL,  &  Elledge  SJ  (2009)  Principles  of  cancer  therapy:  oncogene  and  non-­‐oncogene   addiction ...Cancer Res 2010; 70: 4749–4758. 32 Diederichs S, Haber DA. Dual role for argonautes in microRNA processing and posttranscriptional regulation of

  11. Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks.

    Science.gov (United States)

    Gladilin, Evgeny

    2017-01-01

    Malignant transformation is known to involve substantial rearrangement of the molecular genetic landscape of the cell. A common approach to analysis of these alterations is a reductionist one and consists of finding a compact set of differentially expressed genes or associated signaling pathways. However, due to intrinsic tumor heterogeneity and tissue specificity, biomarkers defined by a small number of genes/pathways exhibit substantial variability. As an alternative to compact differential signatures, global features of genetic cell machinery are conceivable. Global network descriptors suggested in previous works are, however, known to potentially be biased by overrepresentation of interactions between frequently studied genes-proteins. Here, we construct a cellular network of 74538 directional and differential gene expression weighted protein-protein and gene regulatory interactions, and perform graph-theoretical analysis of global human interactome using a novel, degree-independent feature-the normalized total communicability (NTC). We apply this framework to assess differences in total information flow between different cancer (BRCA/COAD/GBM) and non-cancer interactomes. Our experimental results reveal that different cancer interactomes are characterized by significant enhancement of long-range NTC, which arises from circulation of information flow within robustly organized gene subnetworks. Although enhancement of NTC emerges in different cancer types from different genomic profiles, we identified a subset of 90 common genes that are related to elevated NTC in all studied tumors. Our ontological analysis shows that these genes are associated with enhanced cell division, DNA replication, stress response, and other cellular functions and processes typically upregulated in cancer. We conclude that enhancement of long-range NTC manifested in the correlated activity of genes whose tight coordination is required for survival and proliferation of all tumor cells

  12. Study: California Ethnic Groups Seeing Increased Cancer Rates

    Science.gov (United States)

    Black Issues in Higher Education, 2005

    2005-01-01

    A statewide study on cancer and ethnicity hints that cancer rates among immigrant groups may be tied to their degree of assimilation into American culture. The study, released by the University of Southern California's Norris Comprehensive Cancer Center, marks the first statewide look at cancer rates among Vietnamese and South Asians and provides…

  13. Modeling microRNA-transcription factor networks in cancer.

    Science.gov (United States)

    Aguda, Baltazar D

    2013-01-01

    An increasing number of transcription factors (TFs) and microRNAs (miRNAs) is known to form feedback loops (FBLs) of interactions where a TF positively or negatively regulates the expression of a miRNA, and the miRNA suppresses the translation of the TF messenger RNA. FBLs are potential sources of instability in a gene regulatory network. Positive FBLs can give rise to switching behaviors while negative FBLs can generate periodic oscillations. This chapter presents documented examples of FBLs and their relevance to stem cell renewal and differentiation in gliomas. Feed-forward loops (FFLs) are only discussed briefly because they do not affect network stability unless they are members of cycles. A primer on qualitative network stability analysis is given and then used to demonstrate the network destabilizing role of FBLs. Steps in model formulation and computer simulations are illustrated using the miR-17-92/Myc/E2F network as an example. This example possesses both negative and positive FBLs.

  14. Establishment of the Fox Chase Network Breast Cancer Risk Registry.

    Science.gov (United States)

    1998-10-01

    booklets, color slides and flip chart prints which describe the normal anatomy and physiology of the breast, ovary, colon and prostate glands, cancer risk...Manual Appendix F Oncology Nursing Society Abstract Appendix G Flip Chart Appendix H Procedures for Implementation 1996 Appendix A Recruitment Procedures

  15. Early Detection of Lung Cancer Using Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Prashant Naresh

    2014-08-01

    Full Text Available Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall, precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.

  16. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    carcinoma cell lines that could cause their resistance to cisplatin treatment. Part VI closes the thesis by summarizing its main points and proposing some future perspectives for the work presented here. All in all, this work establishes a new framework for the prediction of mechanisms underlying cancer...

  17. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Directory of Open Access Journals (Sweden)

    Parvin Jeffrey

    2010-12-01

    Full Text Available Abstract Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2 were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM. A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes and targeted TFs (25% of common TFs. The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to

  18. VMAT planning study in rectal cancer patients

    OpenAIRE

    Shang, Jun; Kong, Wei; Wang, Yan-Yang; Ding, Zhe; Yan, Gang; Zhe, Hong

    2014-01-01

    Background To compare the dosimetric differences among fixed field intensity-modulated radiation therapy (IMRT), single-arc volumetric-modulated arc therapy (SA-VMAT) and double-arc volumetric-modulated arc therapy (DA-VMAT) plans in rectal cancer. Method Fifteen patients with rectal cancer previously treated with IMRT in our institution were selected for this study. For each patient, three plans were generated with the planning CT scan: one using a fixed beam IMRT, and two plans using the VM...

  19. Social Network Analysis: A case study of the Islamist terrorist network

    Energy Technology Data Exchange (ETDEWEB)

    Medina, Richard M [ORNL

    2012-01-01

    Social Network Analysis is a compilation of methods used to identify and analyze patterns in social network systems. This article serves as a primer on foundational social network concepts and analyses and builds a case study on the global Islamist terrorist network to illustrate the use and usefulness of these methods. The Islamist terrorist network is a system composed of multiple terrorist organizations that are socially connected and work toward the same goals. This research utilizes traditional social network, as well as small-world, and scale-free analyses to characterize this system on individual, network and systemic levels. Leaders in the network are identified based on their positions in the social network and the network structure is categorized. Finally, two vital nodes in the network are removed and this version of the network is compared with the previous version to make implications of strengths, weaknesses and vulnerabilities. The Islamist terrorist network structure is found to be a resilient and efficient structure, even with important social nodes removed. Implications for counterterrorism are given from the results of each analysis.

  20. Vitamin D and cancer: an overview on epidemiological studies.

    Science.gov (United States)

    Ordóñez Mena, José Manuel; Brenner, Hermann

    2014-01-01

    In recent years, a rapidly increasing number of studies have investigated the relationship of vitamin D with total cancer and site-specific cancer obtaining diverse findings. In this chapter we provide an overview of epidemiological studies of vitamin D intake, 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D serum levels and vitamin D associated polymorphisms in relation to total and site-specific cancer risk. Overall, epidemiological evidence for total cancer is inconclusive. However, a large number of studies support a relationship of vitamin D with colorectal cancer and to a lesser extent with breast cancer. Findings are inconsistent for other cancers including all other gastrointestinal cancers and prostate cancer. Different vitamin D associated polymorphisms were found to be significantly associated to colorectal, breast and prostate cancer risk.

  1. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    Science.gov (United States)

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-07-25

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer.

  2. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.

    Science.gov (United States)

    Jaeger, Savina; Min, Junxia; Nigsch, Florian; Camargo, Miguel; Hutz, Janna; Cornett, Allen; Cleaver, Stephen; Buckler, Alan; Jenkins, Jeremy L

    2014-06-01

    Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

  3. NETWORK SECURITY ATTACKS. ARP POISONING CASE STUDY

    Directory of Open Access Journals (Sweden)

    Luminiţa DEFTA

    2010-12-01

    Full Text Available Arp poisoning is one of the most common attacks in a switched network. A switch is a network device that limits the ability of attackers that use a packet sniffer to gain access to information from internal network traffic. However, using ARP poisoning the traffic between two computers can be intercepted even in a network that uses switches. This method is known as man in the middle attack. With this type of attack the affected stations from a network will have invalid entries in the ARP table. Thus, it will contain only the correspondence between the IP addresses of the stations from the same network and a single MAC address (the station that initiated the attack. In this paper we present step by step the initiation of such an attack in a network with three computers. We will intercept the traffic between two stations using the third one (the attacker.

  4. Fruit and vegetables and cancer risk: a review of southern European studies.

    Science.gov (United States)

    Turati, Federica; Rossi, Marta; Pelucchi, Claudio; Levi, Fabio; La Vecchia, Carlo

    2015-04-01

    High intakes of fruit and vegetables may reduce the risk of cancer at several sites. Evidence has been derived mainly from case-control studies. We reviewed the relationship between consumption of vegetables and fruit and the risk of several common cancers in a network of Italian and Swiss case-control studies including over 10,000 cases of fourteen different cancers and about 17,000 controls. Data were suggestive of a protective role of vegetable intake on the risk of several common epithelial cancers. OR for the highest compared with the lowest levels of consumption ranged from 0.2 (larynx, oral cavity and pharynx) to 0.9 (prostate). Inverse associations were found for both raw and cooked vegetables, although for upper digestive tract cancers the former were somewhat stronger. Similar inverse associations were found for cruciferous vegetables. Frequent consumption of allium vegetables was also associated with reduced risk of several cancers. Fruit was a favourable correlate of the risk of several cancers, particularly of the upper digestive tract, with associations generally weaker than those reported for vegetables. A reduced risk of cancers of the digestive tract and larynx was found for high consumption of citrus fruit. Suggestive protections against several forms of cancer, mainly digestive tract cancers, were found for high consumption of apples and tomatoes. High intakes of fibres, flavonoids and proanthocyanidins were inversely related to various forms of cancer. In conclusion, data from our series of case-control studies suggested a favourable role of high intakes of fruit and vegetables in the risk of many common cancers, particularly of the digestive tract. This adds evidence to the indication that aspects of the Mediterranean diet may have a favourable impact not only on CVD, but also on several common (epithelial) cancers, particularly of the digestive tract.

  5. Producción científica y redes de colaboración en cáncer en el Perú 2000-2011: un estudio bibliométrico en Scopus y Science Citation Index Scientific production and cancer-related collaboration networks in Peru 2000-2011: a bibliometric study in Scopus and Science Citation Index

    Directory of Open Access Journals (Sweden)

    Percy Mayta-Tristán

    2013-03-01

    Full Text Available Se realizó un estudio bibliométrico para describir la producción científica peruana en cáncer en revistas de visibilidad internacional, y evaluar las redes de colaboración científica. Se incluyó los artículos publicados sobre cáncer hechos en Perú en el periodo 2000 a 2011 en revistas indizadas en SCOPUS o Science Citation Index Expanded. Se identificaron 358 artículos, evidenciándose un incremento en la producción de cuatro artículos en el 2000 a 57 en el 2011. Los cánceres más estudiados fueron los de cuello uterino (77 publicaciones; mama (53, y estómago (37. El Instituto Nacional de Enfermedades Neoplásicas (INEN fue la institución más productiva (121 artículos y con mayor número de colaboraciones (180 instituciones distintas. Se identificaron 52 ensayos clínicos, 29 con al menos un autor del INEN. En conclusión, la investigación en cáncer en Perú se está incrementando, el INEN es la institución más productiva, con importante participación en ensayos clínicosA bibliometric study was carried out to describe the scientific production on cancer written by peruvians and published in international health journals, as well as to assess the scientific collaboration networks. It included articles on cancer written in Peru between the years 2000 and 2011 and published in health journals indexed in SCOPUS or Science Citation Index Expanded. In the 358 articles identified, an increase in the production was seen, from 4 articles in 2000 to 57 in 2011.The most studied types were cervical cancer (77 publications; breast cancer (53, and gastric cancer (37. The National Institute of Neoplastic Diseases (INEN was the most productive institution (121 articles and had the highest number of collaborations (180 different institutions. 52 clinical trials were identified, 29 of which had at least one author from INEN. We can conclude that, cancer research is increasing in Peru, the INEN being the most productive institution, with

  6. Network-based approaches for drug response prediction and targeted therapy development in cancer.

    Science.gov (United States)

    Dorel, Mathurin; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna

    2015-08-21

    Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

  7. An Optimal Control of Bone Marrow in Cancer Chemotherapy by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Hosseinipour

    2015-09-01

    Full Text Available Although neural network models for cancer chemotherapy have been analyzed since the early seventies, less research has been done in actually formulating them as optimal control problems. In this paper an artificial neural networks-based method for optimal control of bone marrow in cell-cycle-specific chemotherapy is proposed. In this method, we use artificial neural networks for approximating the optimal control problem which maximizes both bone marrow mass and drug`s dose at the same time. The corresponding model be transfer to Hamiltonian function and using Pontryagin principle we create the boundary conditions. After defining boundary conditions, we use the approximating property of artificial networks and put the boundary conditions in error functions to satisfy the limitations..

  8. Risk of Salivary Gland Cancer After Childhood Cancer: A Report From the Childhood Cancer Survivor Study

    Energy Technology Data Exchange (ETDEWEB)

    Boukheris, Houda [Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (United States); Stovall, Marilyn [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Gilbert, Ethel S. [Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (United States); Stratton, Kayla L. [Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (United States); Smith, Susan A.; Weathers, Rita [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Hammond, Sue [Department of Pathology, Ohio State University School of Medicine, Columbus, Ohio (United States); Mertens, Ann C. [Department of Pediatrics, Emory University, Atlanta, Georgia (United States); Donaldson, Sarah S. [Department of Radiation Oncology, Stanford University Medical Center, Stanford, California (United States); Armstrong, Gregory T.; Robison, Leslie L. [Department of Epidemiology and Cancer Control, St. Jude Children' s Research Hospital, Memphis, Tennessee (United States); Neglia, Joseph P. [Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota (United States); Inskip, Peter D., E-mail: inskippe@mail.nih.gov [Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (United States)

    2013-03-01

    Purpose: To evaluate effects of radiation therapy, chemotherapy, cigarette smoking, and alcohol consumption on the risk of second primary salivary gland cancer (SGC) in the Childhood Cancer Survivor Study (CCSS). Methods and Materials: Standardized incidence ratios (SIR) and excess absolute risks (EAR) of SGC in the CCSS were calculated using incidence rates from Surveillance, Epidemiology, and End Results population-based cancer registries. Radiation dose to the salivary glands was estimated based on medical records. Poisson regression was used to assess risks with respect to radiation dose, chemotherapy, smoking, and alcohol consumption. Results: During the time period of the study, 23 cases of SGC were diagnosed among 14,135 childhood cancer survivors. The mean age at diagnosis of the first primary cancer was 8.3 years, and the mean age at SGC diagnosis was 24.8 years. The incidence of SGC was 39-fold higher in the cohort than in the general population (SIR = 39.4; 95% CI = 25.4-57.8). The EAR was 9.8 per 100,000 person-years. Risk increased linearly with radiation dose (excess relative risk = 0.36/Gy; 95% CI = 0.06-2.5) and remained elevated after 20 years. There was no significant trend of increasing risk with increasing dose of chemotherapeutic agents, pack-years of cigarette smoking, or alcohol intake. Conclusion: Although the cumulative incidence of SGC was low, childhood cancer survivors treated with radiation experienced significantly increased risk for at least 2 decades after exposure, and risk was positively associated with radiation dose. Results underscore the importance of long-term follow up of childhood cancer survivors for the development of new malignancies.

  9. National Cancer Information Service in Italy: an information points network as a new model for providing information for cancer patients.

    Science.gov (United States)

    Truccolo, Ivana; Bufalino, Rosaria; Annunziata, Maria Antonietta; Caruso, Anita; Costantini, Anna; Cognetti, Gaetana; Florita, Antonio; Pero, Dina; Pugliese, Patrizia; Tancredi, Roberta; De Lorenzo, Francesco

    2011-01-01

    The international literature data report that good information and communication are fundamental components of a therapeutic process. They contribute to improve the patient-health care professional relationship, to facilitate doctor-patient relationships, therapeutic compliance and adherence, and to the informed consent in innovative clinical trials. We report the results of a multicentric national initiative that developed a 17-information-structure network: 16 Information Points located in the major state-funded certified cancer centers and general hospitals across Italy and a national Help-line at the nonprofit organization AIMaC (the Italian oncologic patients, families and friends association), and updated the already existing services with the aim to create the National Cancer Information Service (SION). The project is the result of a series of pilot and research projects funded by the Italian Ministry of Health. The Information Service model proposed is based on some fundamental elements: 1) human interaction with experienced operators, adequately trained in communication and information, complemented with 2) virtual interaction (Help line, Internet, blog, forum and social network); 3) informative material adequate for both scientific accuracy and communicative style; 4) adequate locations for appropriate positioning and privacy (adequate visibility); 5) appropriate advertising. First results coming from these initiatives contributed to introduce issues related to "Communication and Information to patients" as a "Public Health Instrument" to the National Cancer Plan approved by the Ministry of Health for the years 2010-2012.

  10. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    Directory of Open Access Journals (Sweden)

    Neha Sharma

    2015-01-01

    Full Text Available In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  11. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer.

    Science.gov (United States)

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  12. Imaging biomarker roadmap for cancer studies

    Science.gov (United States)

    O’Connor, James P. B.; Aboagye, Eric O.; Adams, Judith E.; Aerts, Hugo J. W. L.; Barrington, Sally F.; Beer, Ambros J.; Boellaard, Ronald; Bohndiek, Sarah E.; Brady, Michael; Brown, Gina; Buckley, David L.; Chenevert, Thomas L.; Clarke, Laurence P.; Collette, Sandra; Cook, Gary J.; deSouza, Nandita M.; Dickson, John C.; Dive, Caroline; Evelhoch, Jeffrey L.; Faivre-Finn, Corinne; Gallagher, Ferdia A.; Gilbert, Fiona J.; Gillies, Robert J.; Goh, Vicky; Griffiths, John R.; Groves, Ashley M.; Halligan, Steve; Harris, Adrian L.; Hawkes, David J.; Hoekstra, Otto S.; Huang, Erich P.; Hutton, Brian F.; Jackson, Edward F.; Jayson, Gordon C.; Jones, Andrew; Koh, Dow-Mu; Lacombe, Denis; Lambin, Philippe; Lassau, Nathalie; Leach, Martin O.; Lee, Ting-Yim; Leen, Edward L.; Lewis, Jason S.; Liu, Yan; Lythgoe, Mark F.; Manoharan, Prakash; Maxwell, Ross J.; Miles, Kenneth A.; Morgan, Bruno; Morris, Steve; Ng, Tony; Padhani, Anwar R.; Parker, Geoff J. M.; Partridge, Mike; Pathak, Arvind P.; Peet, Andrew C.; Punwani, Shonit; Reynolds, Andrew R.; Robinson, Simon P.; Shankar, Lalitha K.; Sharma, Ricky A.; Soloviev, Dmitry; Stroobants, Sigrid; Sullivan, Daniel C.; Taylor, Stuart A.; Tofts, Paul S.; Tozer, Gillian M.; van Herk, Marcel; Walker-Samuel, Simon; Wason, James; Williams, Kaye J.; Workman, Paul; Yankeelov, Thomas E.; Brindle, Kevin M.; McShane, Lisa M.; Jackson, Alan; Waterton, John C.

    2017-01-01

    Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing ‘translational gaps’ through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored ‘roadmap’. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use. PMID:27725679

  13. Control of cancer-related signal transduction networks

    Science.gov (United States)

    Albert, Reka

    2013-03-01

    Intra-cellular signaling networks are crucial to the maintenance of cellular homeostasis and for cell behavior (growth, survival, apoptosis, movement). Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development and/or the promotion of cell migration and metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. We recently demonstrated the effectiveness of network-based targeted manipulations on mitigating the disease T cell large granular lymphocyte (T-LGL) leukemia. The root of this disease is the abnormal survival of T cells which, after successfully fighting an infection, should undergo programmed cell death. We synthesized the relevant network of within-T-cell interactions from the literature, integrated it with qualitative knowledge of the dysregulated (abnormal) states of several network components, and formulated a Boolean dynamic model. The model indicated that the system possesses a steady state corresponding to the normal cell death state and a T-LGL steady state corresponding to the abnormal survival state. For each node, we evaluated the restorative manipulation consisting of maintaining the node in the state that is the opposite of its T-LGL state, e.g. knocking it out if it is overexpressed in the T-LGL state. We found that such control of any of 15 nodes led to the disappearance of the T-LGL steady state, leaving cell death as the only potential outcome from any initial condition. In four additional cases the probability of reaching the T-LGL state decreased dramatically, thus these nodes are also possible control

  14. Head and Neck Cancer Stem Cells: From Identification to Tumor Immune Network.

    Science.gov (United States)

    Dionne, L K; Driver, E R; Wang, X J

    2015-11-01

    Head and neck squamous cell carcinoma (HNSCC) is the most common form of head and neck cancer. Annually, more than half a million individuals are diagnosed with this devastating disease, with increasing incidence in Europe and Southeast Asia. The diagnosis of HNSCC often occurs in late stages of the disease and is characterized by manifestation of a high-grade primary tumor and/or lymph node metastasis, precluding timely management of this deadly cancer. Recently, HNSCC cancer stem cells have emerged as an important factor for cancer initiation and maintenance of tumor bulk. Like normal stem cells, cancer stem cells can undergo self-renewal and differentiation. This unique trait allows for maintenance of the cancer stem cell pool and facilitates differentiation into heterogeneous neoplastic progeny when necessary. Recent studies have suggested coexistence of different cancer stem cell populations within a tumor mass, where the tumor initiation and metastasis properties of these cancer stem cells can be uncoupled. Cancer stem cells also possess resistant phenotypes that evade standard chemotherapy and radiotherapy, resulting in tumor relapse. Therefore, understanding distinctive pathways relating to cancer stem cells will provide insight into early diagnosis and treatment of HNSCC. In this review, we highlight current advances in identifying cancer stem cells, detail the interactions of these cells with the immune system within the tumor niche, and discuss the potential use of immunotherapy in managing HNSCC.

  15. Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data.

    Science.gov (United States)

    Milenkovic, Tijana; Memisevic, Vesna; Ganesan, Anand K; Przulj, Natasa

    2010-03-06

    Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein-protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and analysing their structural properties could provide insights into biological function and disease. We have already demonstrated, by using a sensitive graph theoretic method for comparing topologies of node neighbourhoods called 'graphlet degree signatures', that proteins with similar surroundings in PPI networks tend to perform the same functions. Here, we explore whether the involvement of genes in cancer suggests the similarity of their topological 'signatures' as well. By applying a series of clustering methods to proteins' topological signature similarities, we demonstrate that the obtained clusters are significantly enriched with cancer genes. We apply this methodology to identify novel cancer gene candidates, validating 80 per cent of our predictions in the literature. We also validate predictions biologically by identifying cancer-related negative regulators of melanogenesis identified in our siRNA screen. This is encouraging, since we have done this solely from PPI network topology. We provide clear evidence that PPI network structure around cancer genes is different from the structure around non-cancer genes. Understanding the underlying principles of this phenomenon is an open question, with a potential for increasing our understanding of complex diseases.

  16. Assessment of Breast Cancer Risk in an Iranian Female Population Using Bayesian Networks with Varying Node Number

    Science.gov (United States)

    Rezaianzadeh, Abbas; Sepandi, Mojtaba; Rahimikazerooni, Salar

    2016-11-01

    Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

  17. Evolution of Terrorist Network using Clustered approach: A Case study

    DEFF Research Database (Denmark)

    2011-01-01

    In the paper we present a cluster based approach for terrorist network evolution. We have applied hierarchical agglomerative clustering approach to 9/11 case study. We show that, how individual actors who are initially isolated from each other are converted in small clusters and result in a fully...... evolved network. This method of network evolution can help intelligence security analysts to understand the structure of the network....

  18. Construction and analysis of regulatory genetic networks in cervical cancer based on involved microRNAs, target genes, transcription factors and host genes.

    Science.gov (United States)

    Wang, Ning; Xu, Zhiwen; Wang, Kunhao; Zhu, Minghui; Li, Yang

    2014-04-01

    Over recent years, genes and microRNA (miRNA/miR) have been considered as key biological factors in human carcinogenesis. During cancer development, genes may act as multiple identities, including target genes of miRNA, transcription factors and host genes. The present study concentrated on the regulatory networks consisting of the biological factors involved in cervical cancer in order to investigate their features and affect on this specific pathology. Numerous raw data was collected and organized into purposeful structures, and adaptive procedures were defined for application to the prepared data. The networks were therefore built with the factors as basic components according to their interacting associations. The networks were constructed at three levels of interdependency, including a differentially-expressed network, a related network and a global network. Comparisons and analyses were made at a systematic level rather than from an isolated gene or miRNA. Critical hubs were extracted in the core networks and notable features were discussed, including self-adaption feedback regulation. The present study expounds the pathogenesis from a novel point of view and is proposed to provide inspiration for further investigation and therapy.

  19. Barrett's Esophagus Translational Research Network (BETRNet) | Division of Cancer Prevention

    Science.gov (United States)

    The goal of BETRNet is to reduce the incidence, morbidity, and mortality of esophageal adenocarcinoma by answering key questions related to the progression of the disease, especially in the premalignant stage. In partnership with NCI’s Division of Cancer Biology, multidisciplinary translational research centers collaborate to better understand the biology of Barrett's esophagus and esophageal adenocarcinoma to improve risk stratification and develop prevention strategies.  | Multi-disciplinary, multi-institutional collaboration to enhance understanding of Barrett's esophagus and to prevent esophageal adenocarcinoma.

  20. Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

    Science.gov (United States)

    Sebestyén, Endre; Singh, Babita; Miñana, Belén; Pagès, Amadís; Mateo, Francesca; Pujana, Miguel Angel; Valcárcel, Juan; Eyras, Eduardo

    2016-01-01

    Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression. PMID:27197215

  1. An analysis of Social Work Oncology Network Listserv Postings on the Commission of Cancer's distress screening guidelines.

    Science.gov (United States)

    Burg, Mary Ann; Adorno, Gail; Hidalgo, Jorge

    2012-01-01

    This is a qualitative study of listserv postings by members of the Social Work Oncology Network (SWON) in response to the Commission on Cancer's 2011 guidelines for distress screening of cancer patients. Archived listserv postings for the period of December 2010 to November 2011 were deidentified and a sample was derived by a list of keywords for the analysis. Aims of the study included describing the general categories and themes of the postings devoted to the new distress screening standard and examining the process of facilitation of mutual support and information exchange by oncology social workers in response to the new screening standards. During the 12-month timeframe there were 242 unique listserv postings sampled for the analysis. Oncology social worker (OSW) discussion of the distress screening guidelines remained a constant topic over the 12 months, and major themes that emerged from the data included processes of implementation of distress screening in cancer centers, screening policies and protocols, screening tool choice, and oncology social worker professional identity. The SWON listserv members used the listserv as a mechanism to post their requests for information on screening, to share their experiences in the beginning stages of implementing the guidelines, and to build support for legitimizing oncology social workers as the lead profession in the implementation of the guidelines in member cancer centers.

  2. Integrated miRNA-risk gene-pathway pair network analysis provides prognostic biomarkers for gastric cancer

    Directory of Open Access Journals (Sweden)

    Cai H

    2016-05-01

    Full Text Available Hui Cai,1 Jiping Xu,2 Yifang Han,3 Zhengmao Lu,1 Ting Han,1 Yibo Ding,4 Liye Ma1 1Department of General Surgery, Changhai Hospital, Second Military Medical University, Shanghai, 2Department of Medical Administration, Changhai Hospital, Second Military Medical University, Shanghai, 3Department of Epidemiology, Research Institute for Medicine of Nanjing Command, Nanjing, 4Department of Epidemiology, Changhai Hospital, Second Military Medical University, Shanghai, People’s Republic of China Purpose: This study aimed to identify molecular prognostic biomarkers for gastric cancer. Methods: mRNA and miRNA expression profiles of eligible gastric cancer and control samples were downloaded from Gene Expression Omnibus to screen the differentially expressed genes (DEGs and differentially expressed miRNAs (DEmiRs, using MetaDE and limma packages, respectively. Target genes of the DEmiRs were also collected from both predictive and experimentally validated target databases of miRNAs. The overlapping genes between selected targets and DEGs were identified as risk genes, followed by functional enrichment analysis. Human pathways and their corresponding genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database for the expression analysis of each pathway in gastric cancer samples. Next, co-pathway pairs were selected according to the Pearson correlation coefficients. Finally, the co-pathway pairs, miRNA–target pairs, and risk gene–pathway pairs were merged into a complex interaction network, the most important nodes (miRNAs/target genes/co-pathway pairs of which were selected by calculating their degrees.Results: Totally, 1,260 DEGs and 144 DEmiRs were identified. There were 336 risk genes found in the 9,572 miRNA–target pairs. Judging from the pathway expression files, 45 co-pathway pairs were screened out. There were 1,389 interactive pairs and 480 nodes in the integrated network. Among all nodes in the network, focal

  3. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used...... to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences...

  4. Neural Network Analysis of Breast Cancer from Mammographic Evaluation

    Directory of Open Access Journals (Sweden)

    P. Abdolmaleki

    2006-06-01

    Full Text Available Background/Objective: Mammographic differentiation of benign lesions from malignancies is a difficult task. We developed an artificial neural network (ANN as a diagnostic aid in mammography using radiographic features as input. Materials & Methods: A three-layered ANN was used to differentiate malignant from benign findings in a group of patients with proven breast lesions on the basis of morphological data extracted from conventional mammograms. Our database included 122 patient records on 14qualitative variables. The database was randomly divided into training and validation samples including 82 and 40 patient records, respectively, to construct the ANN and validate its performance. Sensitivity, specificity, accuracy and receiver operating characteristic curve (ROC analysis for this method and the radiologist were compared. Results: Our results showed that the neural network model was able to correctly classify 30 out of 40 cases presented in the validation sample. Comparing the output with that of the radiologist, showed a reasonable diagnostic accuracy (75%, a moderate specificity (64% and a relatively high sensitivity (89%. Conclusion: A diagnostic aid was developed that accurately differentiates malignant from benign pattern using radiological features extracted from mammograms.

  5. Artificial neural networks and prostate cancer--tools for diagnosis and management.

    Science.gov (United States)

    Hu, Xinhai; Cammann, Henning; Meyer, Hellmuth-A; Miller, Kurt; Jung, Klaus; Stephan, Carsten

    2013-03-01

    Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

  6. Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Nor A.M.   Isa

    2007-01-01

    Full Text Available Thirteen cytology of fine needle aspiration image (i.e. cellularity, background information, cohesiveness, significant stromal component, clump thickness, nuclear membrane, bare nuclei, normal nuclei, mitosis, nucleus stain, uniformity of cell, fragility and number of cells in cluster are evaluated their possibility to be used as input data for artificial neural network in order to classify the breast pre-cancerous cases into four stages, namely malignant, fibroadenoma, fibrocystic disease, and other benign diseases. A total of 1300 reported breast pre-cancerous cases which was collected from Penang General Hospital and Hospital Universiti Sains Malaysia, Kelantan, Malaysia was used to train and test the artificial neural networks. The diagnosis system which was developed using the Hybrid Multilayered Perceptron and trained using Modified Recursive Prediction Error produced excellent diagnosis performance with 100% accuracy, 100% sensitivity and 100% specificity.

  7. The Heritability of Breast Cancer among women in the Nordic Twin Study of Cancer

    DEFF Research Database (Denmark)

    Möller, Sören; Mucci, Lorelei A; Harris, Jennifer R;

    2016-01-01

    and heritability of breast cancer among 21,054 monozygotic and 30,939 dizygotic female twin pairs from the Nordic Twin Study of Cancer, the largest twin study of cancer in the world. We accounted for left-censoring, right-censoring, as well as the competing risk of death. Results From 1943 through 2010, 3...

  8. The network of pluripotency, epithelial–mesenchymal transition, and prognosis of breast cancer

    Directory of Open Access Journals (Sweden)

    Voutsadakis IA

    2015-09-01

    Full Text Available Ioannis A Voutsadakis1,2 1Division of Medical Oncology, Department of Internal Medicine, Sault Area Hospital, Sault Ste Marie, ON, Canada; 2Division of Clinical Sciences, Northern Ontario School of Medicine, Sudbury, ON, Canada Abstract: Breast cancer is the leading female cancer in terms of prevalence. Progress in molecular biology has brought forward a better understanding of its pathogenesis that has led to better prognostication and treatment. Subtypes of breast cancer have been identified at the genomic level and guide therapeutic decisions based on their biology and the expected benefit from various interventions. Despite this progress, a significant percentage of patients die from their disease and further improvements are needed. The cancer stem cell theory and the epithelial–mesenchymal transition are two comparatively novel concepts that have been introduced in the area of cancer research and are actively investigated. Both processes have their physiologic roots in normal development and common mediators have begun to surface. This review discusses the associations of these networks as a prognostic framework in breast cancer. Keywords: stem cells, epithelial-to-mesenchymal transition, mesenchymal-to-epithelial transition

  9. A simple model for studying interacting networks

    Science.gov (United States)

    Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.

    2011-03-01

    Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.

  10. The structural network of Interleukin-10 and its implications in inflammation and cancer

    OpenAIRE

    Acuner-Özbabacan, Ece Saliha; Engin, Billur Hatice; Güven-Maiorov, Emine; Kuzu, Güray; Muratçıoğlu, Serena; Başpınar, Alper; Gürsoy, Attila; Chen, Zhong; Van Waes, Carter; Nussinov, Ruth

    2014-01-01

    RESEARCH Open Access The structural network of Interleukin-10 and its implications in inflammation and cancer Ece Saliha Acuner-Ozbabacan1, Billur Hatice Engin1, Emine Guven-Maiorov1, Guray Kuzu1, Serena Muratcioglu1, Alper Baspinar1, Zhong Chen3, Carter Van Waes3, Attila Gursoy1, Ozlem Keskin1, Ruth Nussinov2,4* From SNP-SIG 2013: Identification and annotation of genetic variants in the context of structure, function, and disease Berlin, Germany. 19 July 2013 Abstract...

  11. A Bayesian network approach for modeling local failure in lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam, E-mail: elnaqa@wustl.edu [Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110 (United States)

    2011-03-21

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

  12. MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease

    Indian Academy of Sciences (India)

    Jin Wang; Subrata Sen

    2011-08-01

    MicroRNAs (miRs), the 17- to 25-nucleotide-long non-coding RNAs, regulate expression of approximately 30% of the protein-coding genes at the post-transcriptional level and have emerged as critical components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis. Abnormal expression levels of miRs, regulating critical cancerassociated pathways, have been implicated to play important roles in the oncogenic processes, functioning both as oncogenes and as tumour suppressor genes. Elucidation of the genetic networks regulated by the abnormally expressing miRs in cancer cells is proving to be extremely significant in understanding the role of these miRs in the induction of malignant-transformation-associated phenotypic changes. As a result, the miRs involved in the oncogenic transformation process are being investigated as novel biomarkers of disease detection and prognosis as well as potential therapeutic targets for human cancers. In this \\article, we review the existing literature in the field documenting the significance of aberrantly expressed miRs in human pancreatic cancer and discuss how the oncogenic miRs may be involved in the genetic networks regulating functional pathways deregulated in this malignancy.

  13. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  14. Involvement of Different networks in mammary gland involution after the pregnancy/lactation cycle: Implications in breast cancer.

    Science.gov (United States)

    Zaragozá, Rosa; García-Trevijano, Elena R; Lluch, Ana; Ribas, Gloria; Viña, Juan R

    2015-04-01

    Early pregnancy is associated with a reduction in a woman's lifetime risk for breast cancer. However, different studies have demonstrated an increase in breast cancer risk in the years immediately following pregnancy. Early and long-term risk is even higher if the mother age is above 35 years at the time of first parity. The proinflammatory microenvironment within the mammary gland after pregnancy renders an "ideal niche" for oncogenic events. Signaling pathways involved in programmed cell death and tissue remodeling during involution are also activated in breast cancer. Herein, the major signaling pathways involved in mammary gland involution, signal transducer and activator of transcription (STAT3), nuclear factor-kappa B (NF-κB), transforming growth factor beta (TGFβ), and retinoid acid receptors (RARs)/retinoid X receptors (RXRs), are reviewed as part of the complex network of signaling pathways that crosstalk in a contextual-dependent manner. These factors, also involved in breast cancer development, are important regulatory nodes for signaling amplification after weaning. Indeed, during involution, p65/p300 target genes such as MMP9, Capn1, and Capn2 are upregulated. Elevated expression and activities of these proteases in breast cancer have been extensively documented. The role of these proteases during mammary gland involution is further discussed. MMPs, calpains, and cathepsins exert their effect by modification of the extracellular matrix and intracellular proteins. Calpains, activated in the mammary gland during involution, cleave several proteins located in cell membrane, lysosomes, mitochondria, and nuclei favoring cell death. Besides, during this period, Capn1 is most probably involved in the modulation of preadipocyte differentiation through chromatin remodeling. Calpains can be implicated in cell anchoring loss, providing a proper microenvironment for tumor growth. A better understanding of the role of any of these proteases in tumorigenesis may

  15. The Applicability of Social Network Analysis to the Study of Networked Learning

    Science.gov (United States)

    Toikkanen, Tarmo; Lipponen, Lasse

    2011-01-01

    Studying networked learning (NL) by applying social network analysis (SNA) has gained popularity in recent years. However, it appears that in the context of NL the choice of SNA indices is very often dictated by using easily achievable SNA tools. Most studies in this field only involve a single group of students and utilise simple indices, such as…

  16. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  17. Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes

    Directory of Open Access Journals (Sweden)

    Valérie Bourdès

    2010-01-01

    Full Text Available The aim of this study was to compare multilayer perceptron neural networks (NNs with standard logistic regression (LR to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS, and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.

  18. Recording of hormone therapy and breast density in breast screening programs: summary and recommendations of the International Cancer Screening Network.

    NARCIS (Netherlands)

    Cox, B.; Ballard-Barbash, R.; Broeders, M.J.M.; Dowling, E.; Malila, N.; Shumak, R.; Taplin, S.; Buist, D.; Miglioretti, D.

    2010-01-01

    Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast densit

  19. Variation in detection of ductal carcinoma in situ during screening mammography: a survey within the International Cancer Screening Network

    NARCIS (Netherlands)

    Lynge, E.; Ponti, A.; James, T.; Majek, O.; Euler-Chelpin, M. von; Anttila, A.; Fitzpatrick, P.; Frigerio, A.; Kawai, M.; Scharpantgen, A.; Broeders, M.J.; Hofvind, S.; Vidal, C.; Ederra, M.; Salas, D.; Bulliard, J.L.; Tomatis, M.; Kerlikowske, K.; Taplin, S.

    2014-01-01

    BACKGROUND: There is concern about detection of ductal carcinoma in situ (DCIS) in screening mammography. DCIS accounts for a substantial proportion of screen-detected lesions but its effect on breast cancer mortality is debated. The International Cancer Screening Network conducted a comparative ana

  20. Deep Space Network information system architecture study

    Science.gov (United States)

    Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

    1992-01-01

    The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

  1. Creative networks in Guanxi Land: a study of social networking related to Shanghai Expo 2010

    OpenAIRE

    Lee, Y-H

    2007-01-01

    Entering the twentieth-first century, post-Mao China continues its considerable transformation. The central theme of this research lies in the examination of social networking through a case study of Shanghai Expo 2010. It is an analysis of the forms of networking in the formation of Shanghai as a global city. The overall question is: To what extent will China be able to enter the global network economy whilst maintaining its emphasis on hierarchical decision-making and central control? This ...

  2. Molecular network analysis of human microRNA targetome: from cancers to Alzheimer’s disease

    Directory of Open Access Journals (Sweden)

    Satoh Jun-ichi

    2012-10-01

    Full Text Available Abstract MicroRNAs (miRNAs, a class of endogenous small noncoding RNAs, mediate posttranscriptional regulation of protein-coding genes by binding chiefly to the 3’ untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation. A single miRNA concurrently downregulates hundreds of target mRNAs designated “targetome”, and thereby fine-tunes gene expression involved in diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. Recently, we characterized the molecular network of the whole human miRNA targetome by using bioinformatics tools for analyzing molecular interactions on the comprehensive knowledgebase. We found that the miRNA targetome regulated by an individual miRNA generally constitutes the biological network of functionally-associated molecules in human cells, closely linked to pathological events involved in cancers and neurodegenerative diseases. We also identified a collaborative regulation of gene expression by transcription factors and miRNAs in cancer-associated miRNA targetome networks. This review focuses on the workflow of molecular network analysis of miRNA targetome in silico. We applied the workflow to two representative datasets, composed of miRNA expression profiling of adult T cell leukemia (ATL and Alzheimer’s disease (AD, retrieved from Gene Expression Omnibus (GEO repository. The results supported the view that miRNAs act as a central regulator of both oncogenesis and neurodegeneration.

  3. Study of IMT-advanced heterogeneous network

    Institute of Scientific and Technical Information of China (English)

    Qin Fei; Peng Ying; Sun Shaohui; Wang Yingmin

    2011-01-01

    Referring to research on the Heterogeneous Network (Het-Net) application scenario and technique characters in IMT-Advaneed (The 4th Generation Mobile Communications) cellular system, this paper provides further analysis on main technique aspects of Heterogeneous Network, discussion on interference issue due to multi-layer building by access points and their corresponding solutions from standardization and engineering implementation. The proposed solution can effectively solve the interference problem in IMT-advanced Het-Net, and also improves the networking performance dramaticaUy for future mobile communication systems.

  4. [Application of cohort study in cancer prevention and control].

    Science.gov (United States)

    Dai, Min; Bai, Yana; Pu, Hongquan; Cheng, Ning; Li, Haiyan; He, Jie

    2016-03-01

    Cancer control is a long-term work. Cancer research and intervention really need the support of cohort study. In the recent years, more and more cohort studies on cancer control were conducted in China along with the increased ability of scientific research in China. Since 2010, Cancer Hospital, Chinese Academy of Medical Sciences, collaborated with Lanzhou University and the Worker' s Hospital of Jinchuan Group Company Limited, have carried out a large-scale cohort study on cancer, which covered a population of more than 50 000 called " Jinchang cohort". Since 2012, a National Key Public Health Project, "cancer screening in urban China" , has been conducted in Jinchang, which strengthened the Jinchang cohort study. Based on the Jinchang cohort study, historical cohort study, cross-sectional study and prospective cohort study have been conducted, which would provide a lot of evidence for the cancer control in China.

  5. Global characterization of signalling networks associated with tamoxifen resistance in breast cancer

    DEFF Research Database (Denmark)

    Browne, Brigid C.; Hochgräfe, Falko; Wu, Jianmin;

    2013-01-01

    Acquired resistance to the anti‐estrogen tamoxifen remains a significant challenge in breast cancer management. In this study, we used an integrative approach to characterize global protein expression and tyrosine phosphorylation events in tamoxifen‐resistant MCF7 breast cancer cells (TamR) compa...

  6. Social network analysis and network connectedness analysis for industrial symbiotic systems: model development and case study

    Institute of Scientific and Technical Information of China (English)

    Yan ZHANG; Hongmei ZHENG; Bin CHEN; Naijin YANG

    2013-01-01

    An important and practical pattern of industrial symbiosis is rapidly developing:eco-industrial parks.In this study,we used social network analysis to study the network connectedness (i.e.,the proportion of the theoretical number of connections that had been achieved) and related attributes of these hybrid ecological and industrial symbiotic systems.This approach provided insights into details of the network's interior and analyzed the overall degree of connectedness and the relationships among the nodes within the network.We then characterized the structural attributes of the network and subnetwork nodes at two levels (core and periphery),thereby providing insights into the operational problems within each eco-industrial park.We chose ten typical ecoindustrial parks in China and around the world and compared the degree of network connectedness of these systems that resulted from exchanges of products,byproducts,and wastes.By analyzing the density and nodal degree,we determined the relative power and status of the nodes in these networks,as well as other structural attributes such as the core-periphery structure and the degree of sub-network connectedness.The results reveal the operational problems created by the structure of the industrial networks and provide a basis for improving the degree of completeness,thereby increasing their potential for sustainable development and enriching the methods available for the study of industrial symbiosis.

  7. Earthquake Correlations and Networks- A Comparative Study

    CERN Document Server

    G., T R Krishna Mohan P

    2010-01-01

    We quantify the correlation between earthquakes and use the same to distinguish between relevant causally connected earthquakes. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski (2004). A network of earthquakes is constructed, which is time ordered and with links between the more correlated ones. Recurrences to earthquakes are identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions, viz. California, Japan and Himalayas, are comparatively analyzed using such a network model. The distribution of recurrence lengths and recurrence times are two of the key features analyzed to draw conclusions about the universal aspects of such a network model. We find that the unimodal feature of recurrence length distribution, which helps to associate typical rupture lengths with different magnitude earthquakes, is robust across the different seismic regions. The out-degree of the networks shows a hub ...

  8. Impact of dimensionality and network disruption on microrheology of cancer cells in 3D environments.

    Directory of Open Access Journals (Sweden)

    Michael Mak

    2014-11-01

    Full Text Available Dimensionality is a fundamental component that can have profound implications on the characteristics of physical systems. In cell biology, however, the majority of studies on cell physical properties, from rheology to force generation to migration, have been performed on 2D substrates, and it is not clear how a more realistic 3D environment influences cell properties. Here, we develop an integrated approach and demonstrate the combination of mitochondria-tracking microrheology, microfluidics, and Brownian dynamics simulations to explore the impact of dimensionality on intracellular mechanics and on the effects of intracellular disruption. Additionally, we consider both passive thermal and active motor-driven processes within the cell and demonstrate through modeling how active internal fluctuations are modulated via dimensionality. Our results demonstrate that metastatic breast cancer cells (MDA-MB-231 exhibit more solid-like internal motions in 3D compared to 2D, and actin network disruption via Cytochalasin D has a more pronounced effect on internal cell fluctuations in 2D. Our computational results and modeling show that motor-induced active stress fluctuations are enhanced in 2D, leading to increased local intracellular particle fluctuations and apparent fluid-like behavior.

  9. Dairy consumption and ovarian cancer risk in the Netherlands Cohort Study on diet and cancer

    NARCIS (Netherlands)

    Mommers, M.; Schouten, L.J.; Goldbohm, R.A.; Brandt, P.A. van den

    2006-01-01

    Ovary cancer risk in relation to consumption of dairy products was investigated using a self-administered questionnaire on dietary habits and other risk factors for cancer, which was completed in 1986 by 62 573 postmenopausal women participating in the Netherlands Cohort Study. Follow-up for cancer

  10. Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

    Science.gov (United States)

    Zhang, XiaoTian; Ni, ZhaoHui; Duan, ZiPeng; Xin, ZhuoYuan; Wang, HuaiDong; Tan, JiaYi; Wang, GuoQing; Li, Fan

    2015-01-01

    Gene expression is regulated at the transcription and translation levels; thus, both transcription factors (TFs) and microRNAs (miRNA) play roles in regulation of gene expression. This study profiled differentially expressed mRNAs and miRNAs in gastric cancer tissues to construct a TF and miRNA co-regulatory network in order to identify altered genes in gastric cancer progression. A total of 70 cases gastric cancer and paired adjacent normal tissues were subjected to cDNA and miRNA microarray analyses. We obtained 887 up-regulated and 93 down-regulated genes and 41 down-regulated and 4 up-regulated miRNAs in gastric cancer tissues. Using the Transcriptional Regulatory Element Database, we obtained 105 genes that are regulated by the E2F family of genes and using Targetscan, miRanda, miRDB and miRWalk tools, we predicted potential targeting genes of these 45 miRNAs. We then built up the E2F-related TF and miRNA co-regulatory gene network and identified 9 hub-genes. Furthermore, we found that levels of E2F1, 2, 3, 4, 5, and 7 mRNAs associated with gastric cancer cell invasion capacity, and has associated with tumor differentiation. These data showed Overexpression of E2F mRNAs associated with gastric cancer progression.

  11. Proteomic Study of the Brassinosteroid Signalling Network

    Institute of Scientific and Technical Information of China (English)

    Zhiyong Wang

    2012-01-01

    Plant growth is controlled by multiple environmental signals and endogenous hormones.In particular,brassinosteroid (BR) regulates a wide range of developmental processes throughout the life cycle of plants.BR acts through a receptor kinase signalling pathway,and BR signalling crosstalk with many other signalling pathways including light and gibberellin pathways as well as other receptor kinase pathways.My lab uses a combination of genetic,proteomic,and genomic approaches to elucidate not only the BR signaling pathway but also the global organization of the signaling network.We have successfully used proteomics to identify new components of the BR signalling pathway and to elucidated the mechanisms of signal transduction from the BRI1 receptor kinase to the BZR1 transcription factor.We have further uncovered mechanisms of crosstalk between different receptor kinase pathways,and we are dissecting the molecular mechanisms underlying signalling crosstalk and specificity.Our recent proteomic analysis of BR-regulated nuclear proteins has identified a potential link for BR regulation of flowering through RNA splicing and epigenetic mechanisms.I will discuss strategies and potential pitfalls in using proteomics to study signal transduction in plants.

  12. Toponome imaging system: in situ protein network mapping in normal and cancerous colon from the same patient reveals more than five-thousand cancer specific protein clusters and their subcellular annotation by using a three symbol code.

    Science.gov (United States)

    Bhattacharya, Sayantan; Mathew, George; Ruban, Ernie; Epstein, David B A; Krusche, Andreas; Hillert, Reyk; Schubert, Walter; Khan, Michael

    2010-12-03

    In a proof of principle study, we have applied an automated fluorescence toponome imaging system (TIS) to examine whether TIS can find protein network structures, distinguishing cancerous from normal colon tissue present in a surgical sample from the same patient. By using a three symbol code and a power of combinatorial molecular discrimination (PCMD) of 2(21) per subcellular data point in one single tissue section, we demonstrate an in situ protein network structure, visualized as a mosaic of 6813 protein clusters (combinatorial molecular phenotype or CMPs), in the cancerous part of the colon. By contrast, in the histologically normal colon, TIS identifies nearly 5 times the number of protein clusters as compared to the cancerous part (32 009). By subcellular visualization procedures, we found that many cell surface membrane molecules were closely associated with the cell cytoskeleton as unique CMPs in the normal part of the colon, while the same molecules were disassembled in the cancerous part, suggesting the presence of dysfunctional cytoskeleton-membrane complexes. As expected, glandular and stromal cell signatures were found, but interestingly also found were potentially TIS signatures identifying a very restricted subset of cells expressing several putative stem cell markers, all restricted to the cancerous tissue. The detection of these signatures is based on the extreme searching depth, high degree of dimensionality, and subcellular resolution capacity of TIS. These findings provide the technological rationale for the feasibility of a complete colon cancer toponome to be established by massive parallel high throughput/high content TIS mapping.

  13. A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer

    Science.gov (United States)

    Blau, C. Anthony; Ramirez, Arturo B.; Blau, Sibel; Pritchard, Colin C.; Dorschner, Michael O.; Schmechel, Stephen C.; Martins, Timothy J.; Mahen, Elisabeth M.; Burton, Kimberly A.; Komashko, Vitalina M.; Radenbaugh, Amie J.; Dougherty, Katy; Thomas, Anju; Miller, Christopher P.; Annis, James; Fromm, Jonathan R.; Song, Chaozhong; Chang, Elizabeth; Howard, Kellie; Austin, Sharon; Schmidt, Rodney A.; Linenberger, Michael L.; Becker, Pamela S.; Senecal, Francis M.; Mecham, Brigham H.; Lee, Su-In; Madan, Anup; Ronen, Roy; Dutkowski, Janusz; Heimfeld, Shelly; Wood, Brent L.; Stilwell, Jackie L.; Kaldjian, Eric P.; Haussler, David; Zhu, Jingchun

    2016-01-01

    Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient’s evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs. PMID:26733551

  14. A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer.

    Science.gov (United States)

    Blau, C Anthony; Ramirez, Arturo B; Blau, Sibel; Pritchard, Colin C; Dorschner, Michael O; Schmechel, Stephen C; Martins, Timothy J; Mahen, Elisabeth M; Burton, Kimberly A; Komashko, Vitalina M; Radenbaugh, Amie J; Dougherty, Katy; Thomas, Anju; Miller, Christopher P; Annis, James; Fromm, Jonathan R; Song, Chaozhong; Chang, Elizabeth; Howard, Kellie; Austin, Sharon; Schmidt, Rodney A; Linenberger, Michael L; Becker, Pamela S; Senecal, Francis M; Mecham, Brigham H; Lee, Su-In; Madan, Anup; Ronen, Roy; Dutkowski, Janusz; Heimfeld, Shelly; Wood, Brent L; Stilwell, Jackie L; Kaldjian, Eric P; Haussler, David; Zhu, Jingchun

    2016-01-01

    Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

  15. A COMPARATIVE STUDY OF CAREGIVER BURDEN IN CANCER CERVIX AND CANCER BREAST ILLNESSES

    Directory of Open Access Journals (Sweden)

    Srinivasagopalan, Nappinnai, Solayappan

    2015-07-01

    Full Text Available Background: Caregivers of individuals suffering from cancer illnesses are at risk of having subjected to mental health consequences. There is a paucity of data comparing the caregiver burden of cancer breast and cancer cervix patients. Aim: The aim of the present study is to compare the caregiver burden of cancer breast and cancer cervix patients. To study the association of caregiver burden with demographic factors like age, gender, duration of caregiving etc. Materials & Methods: This Cross sectional study is performed on the key relatives of patients of 31 cancer cervix and 31 cancer breast patients. Burden assessment schedule was used. Results: Our findings suggest burden is more in male caregivers of breast cancer patients. It is not so in caregivers of cancer cervix patients. Whenever the caregiver is closely related to the patients the burden is high in both groups. Whenever the burden scores were high the depression scores were also high. Treatment modalities as a whole correlates with burden scores in caregivers of breast cancer patients but not in cancer cervix patients. Conclusion: Caregivers with breast and cervical cancer patients are vulnerable if the caregiver is male, from low socioeconomical background, more closely related and when the patients received poor treatment modalities.

  16. Integrated analysis of the miRNA, gene and pathway regulatory network in gastric cancer.

    Science.gov (United States)

    Zhang, Haiyang; Qu, Yanjun; Duan, Jingjing; Deng, Ting; Liu, Rui; Zhang, Le; Bai, Ming; Li, Jialu; Zhou, Likun; Ning, Tao; Li, Hongli; Ge, Shaohua; Li, Hua; Ying, Guoguang; Huang, Dingzhi; Ba, Yi

    2016-02-01

    Gastric cancer is one of the most common malignant tumors worldwide; however, the efficacy of clinical treatment is limited. MicroRNAs (miRNAs) are a class of small non-coding RNAs that have been reported to play a key role in the development of cancer. They also provide novel candidates for targeted therapy. To date, in-depth studies on the molecular mechanisms of gastric cancer involving miRNAs are still absent. We previously reported that 5 miRNAs were identified as being significantly increased in gastric cancer, and the role of these miRNAs was investigated in the present study. By using bioinformatics tools, we found that more than 4,000 unique genes are potential downstream targets of gastric cancer miRNAs, and these targets belong to the protein class of nucleic acid binding, transcription factor, enzyme modulator, transferase and receptor. Pathway mapping showed that the targets of gastric cancer miRNAs are involved in the MAPK signaling pathway, pathways in cancer, the PI3K-Akt signaling pathway, the HTLV-1 signaling pathway and Ras signaling pathway, thus regulating cell growth, differentiation, apoptosis and metastasis. Analysis of the pathways related to miRNAs may provides potential drug targets for future therapy of gastric cancer.

  17. Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform.

    Directory of Open Access Journals (Sweden)

    Ramesh Ummanni

    Full Text Available Prostate cancer (PCa is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24 compared to benign (n = 21 prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.

  18. Aurora-A controls cancer cell radio- and chemoresistance via ATM/Chk2-mediated DNA repair networks.

    Science.gov (United States)

    Sun, Huizhen; Wang, Yan; Wang, Ziliang; Meng, Jiao; Qi, Zihao; Yang, Gong

    2014-05-01

    High expression of Aurora kinase A (Aurora-A) has been found to confer cancer cell radio- and chemoresistance, however, the underlying mechanism is unclear. In this study, by using Aurora-A cDNA/shRNA or the specific inhibitor VX680, we show that Aurora-A upregulates cell proliferation, cell cycle progression, and anchorage-independent growth to enhance cell resistance to cisplatin and X-ray irradiation through dysregulation of DNA damage repair networks. Mechanistic studies showed that Aurora-A promoted the expression of ATM/Chk2, but suppressed the expression of BRCA1/2, ATR/Chk1, p53, pp53 (Ser15), H2AX, γH2AX (Ser319), and RAD51. Aurora-A inhibited the focus formation of γH2AX in response to ionizing irradiation. Treatment of cells overexpressing Aurora-A and ATM/Chk2 with the ATM specific inhibitor KU-55933 increased the cell sensitivity to cisplatin and irradiation through increasing the phosphorylation of p53 at Ser15 and inhibiting the expression of Chk2, γH2AX (Ser319), and RAD51. Further study revealed that BRCA1/2 counteracted the function of Aurora-A to suppress the expression of ATM/Chk2, but to activate the expression of ATR/Chk1, pp53, γH2AX, and RAD51, leading to the enhanced cell sensitivity to irradiation and cisplatin, which was also supported by the results from animal assays. Thus, our data provide strong evidences that Aurora-A and BRCA1/2 inversely control the sensitivity of cancer cells to radio- and chemotherapy through the ATM/Chk2-mediated DNA repair networks, indicating that the DNA repair molecules including ATM/Chk2 may be considered for the targeted therapy against cancers with overexpression of Aurora-A.

  19. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

  20. Study protocol: rehabilitation including social and physical activity and education in children and teenagers with cancer (RESPECT)

    OpenAIRE

    Thorsteinsson, Troels; Helms, Anne Sofie; Adamsen, Lis; Andersen, Lars Bo; Andersen, Karen Vitting; Christensen, Karl Bang; Halse, Henrik; Heilmann, Carsten; Hejgaard, Nete; Johansen, Christoffer; Madsen, Marianne; Madsen, Svend Aage; Simovska, Venka; Strange, Birgit; Thing, Lone Friis

    2013-01-01

    Background During cancer treatment children have reduced contact with their social network of friends, and have limited participation in education, sports, and leisure activities. During and following cancer treatment, children describe school related problems, reduced physical fitness, and problems related to interaction with peers. Methods/design The RESPECT study is a nationwide population-based prospective, controlled, mixed-methods intervention study looking at children aged 6-18 years n...

  1. P30 Cancer Center Support Grant Administrative Supplements to NCI-designated Cancer Centers not affiliated with the Experimental Therapeutics Clinical Trials Network (ETCTN) to support participation in the ETCTN

    Science.gov (United States)

    P30 Cancer Center Support Grant Administrative Supplements to NCI-designated Cancer Centers not affiliated with the Experimental Therapeutics Clinical Trials Network (ETCTN) to support participation in the ETCTN

  2. Empirical study on clique-degree distribution of networks.

    Science.gov (United States)

    Xiao, Wei-Ke; Ren, Jie; Qi, Feng; Song, Zhi-Wei; Zhu, Meng-Xiao; Yang, Hong-Feng; Jin, Hui-Yu; Wang, Bing-Hong; Zhou, Tao

    2007-09-01

    The community structure and motif-modular-network hierarchy are of great importance for understanding the relationship between structures and functions. We investigate the distribution of clique degrees, which are an extension of degree and can be used to measure the density of cliques in networks. Empirical studies indicate the extensive existence of power-law clique-degree distributions in various real networks, and the power-law exponent decreases with an increase of clique size.

  3. Cancer genetic association studies in the genome-wide age

    OpenAIRE

    Savage, Sharon A

    2008-01-01

    Genome-wide association studies of hundreds of thousands of SNPs have led to a deluge of studies of genetic variation in cancer and other common diseases. Large case–control and cohort studies have identified novel SNPs as markers of cancer risk. Genome-wide association study SNP data have also advanced understanding of population-specific genetic variation. While studies of risk profiles, combinations of SNPs that may increase cancer risk, are not yet clinically applicable, future, large-sca...

  4. Complex regulation of autophagy in cancer - integrated approaches to discover the networks that hold a double-edged sword.

    Science.gov (United States)

    Kubisch, János; Türei, Dénes; Földvári-Nagy, László; Dunai, Zsuzsanna A; Zsákai, Lilian; Varga, Máté; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

    2013-08-01

    Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention

  5. German second-opinion network for testicular cancer: Sealing the leaky pipe between evidence and clinical practice

    Science.gov (United States)

    ZENGERLING, FRIEDEMANN; HARTMANN, MICHAEL; HEIDENREICH, AXEL; KREGE, SUSANNE; ALBERS, PETER; KARL, ALEXANDER; WEISSBACH, LOTHAR; WAGNER, WALTER; BEDKE, JENS; RETZ, MARGITTA; SCHMELZ, HANS U.; KLIESCH, SABINE; KUCZYK, MARKUS; WINTER, EVA; POTTEK, TOBIAS; DIECKMANN, KLAUS-PETER; SCHRADER, ANDRES JAN; SCHRADER, MARK

    2014-01-01

    In 2006, the German Testicular Cancer Study Group initiated an extensive evidence-based national second-opinion network to improve the care of testicular cancer patients. The primary aims were to reflect the current state of testicular cancer treatment in Germany and to analyze the project’s effect on the quality of care delivered to testicular cancer patients. A freely available internet-based platform was developed for the exchange of data between the urologists seeking advice and the 31 second-opinion givers. After providing all data relevant to the primary treatment decision, urologists received a second opinion on their therapy plan within <48 h. Endpoints were congruence between the first and second opinion, conformity of applied therapy with the corresponding recommendation and progression-free survival rate of the introduced patients. Significance was determined by two-sided Pearson’s χ2 test. A total of 1,284 second-opinion requests were submitted from November 2006 to October 2011, and 926 of these cases were eligible for further analysis. A discrepancy was found between first and second opinion in 39.5% of the cases. Discrepant second opinions led to less extensive treatment in 28.1% and to more extensive treatment in 15.6%. Patients treated within the framework of the second-opinion project had an overall 2-year progression-free survival rate of 90.4%. Approximately every 6th second opinion led to a relevant change in therapy. Despite the lack of financial incentives, data from every 8th testicular cancer patient in Germany were submitted to second-opinion centers. Second-opinion centers can help to improve the implementation of evidence into clinical practice. PMID:24788853

  6. Innovative and community-driven communication practices of the South Carolina cancer prevention and control research network.

    Science.gov (United States)

    Friedman, Daniela B; Brandt, Heather M; Freedman, Darcy A; Adams, Swann Arp; Young, Vicki M; Ureda, John R; McCracken, James Lyndon; Hébert, James R

    2014-07-24

    The South Carolina Cancer Prevention and Control Research Network (SC-CPCRN) is 1 of 10 networks funded by the Centers for Disease Control and Prevention and the National Cancer Institute (NCI) that works to reduce cancer-related health disparities. In partnership with federally qualified health centers and community stakeholders, the SC-CPCRN uses evidence-based approaches (eg, NCI Research-tested Intervention Programs) to disseminate and implement cancer prevention and control messages, programs, and interventions. We describe the innovative stakeholder- and community-driven communication efforts conducted by the SC-CPCRN to improve overall health and reduce cancer-related health disparities among high-risk and disparate populations in South Carolina. We describe how our communication efforts are aligned with 5 core values recommended for dissemination and implementation science: 1) rigor and relevance, 2) efficiency and speed, 3) collaboration, 4) improved capacity, and 5) cumulative knowledge.

  7. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Science.gov (United States)

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    Background Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

  8. Study of General Incomplete Star Interconnection Networks

    Institute of Scientific and Technical Information of China (English)

    史云涛; 侯紫峰; 宋建平

    2002-01-01

    The star networks, which were originally proposed by Akers and Harel, have suffered from a rigorous restriction on the number of nodes. The general incomplete star networks (GISN) are proposed in this paper to relieve this restriction. An efficient labeling scheme for GISN is given, and routing and broadcasting algorithms are also presented for GISN. The communication diameter of GISN is shown to be bounded by 4n - 7. The proposed single node broadcasting algorithm is optimal with respect to time complexity O(n log2 n).

  9. Plant sterol intakes and colorectal cancer risk in the Netherlands : cohort study on diet and cancer

    NARCIS (Netherlands)

    Normén, A.L.; Brants, H.A.M.; Voorrips, L.E.; Andersson, H.A.; Brandt, P.A. van den

    2001-01-01

    Background: Plant sterols in vegetable foods might prevent colorectal cancer. Objective: The objective was to study plant sterol intakes in relation to colorectal cancer risk in an epidemiologic study. Design: The study was performed within the framework of the Netherlands Cohort Study on Diet and C

  10. Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis*1

    Science.gov (United States)

    Hernández-Caraballo, Edwin A.; Marcó-Parra, Lué M.

    2003-12-01

    Iron, copper, zinc and selenium were determined directly in serum samples from healthy individuals ( n=33) and cancer patients ( n=27) by total reflection X-ray fluorescence spectrometry using the Compton peak as internal standard [L.M. Marcó P. et al., Spectrochim. Acta Part B 54 (1999) 1469-1480]. The standardized concentrations of these elements were used as input data for two-layer artificial neural networks trained with the generalized delta rule in order to classify such individuals according to their health status. Various artificial neural networks, comprising a linear function in the input layer, a hyperbolic tangent function in the hidden layer and a sigmoid function in the output layer, were evaluated for such a purpose. Of the networks studied, the (4:4:1) gave the highest estimation (98%) and prediction rates (94%). The latter demonstrates the potential of the total reflection X-ray fluorescence spectrometry/artificial neural network approach in clinical chemistry.

  11. Pathway analysis of bladder cancer genome-wide association study identifies novel pathways involved in bladder cancer development

    Science.gov (United States)

    Chen, Meng; Rothman, Nathaniel; Ye, Yuanqing; Gu, Jian; Scheet, Paul A.; Huang, Maosheng; Chang, David W.; Dinney, Colin P.; Silverman, Debra T.; Figueroa, Jonine D.; Chanock, Stephen J.; Wu, Xifeng

    2016-01-01

    Genome-wide association studies (GWAS) are designed to identify individual regions associated with cancer risk, but only explain a small fraction of the inherited variability. Alternative approach analyzing genetic variants within biological pathways has been proposed to discover networks of susceptibility genes with additional effects. The gene set enrichment analysis (GSEA) may complement and expand traditional GWAS analysis to identify novel genes and pathways associated with bladder cancer risk. We selected three GSEA methods: Gen-Gen, Aligator, and the SNP Ratio Test to evaluate cellular signaling pathways involved in bladder cancer susceptibility in a Texas GWAS population. The candidate genetic polymorphisms from the significant pathway selected by GSEA were validated in an independent NCI GWAS. We identified 18 novel pathways (P < 0.05) significantly associated with bladder cancer risk. Five of the most promising pathways (P ≤ 0.001 in any of the three GSEA methods) among the 18 pathways included two cell cycle pathways and neural cell adhesion molecule (NCAM), platelet-derived growth factor (PDGF), and unfolded protein response pathways. We validated the candidate polymorphisms in the NCI GWAS and found variants of RAPGEF1, SKP1, HERPUD1, CACNB2, CACNA1C, CACNA1S, COL4A2, SRC, and CACNA1C were associated with bladder cancer risk. Two CCNE1 variants, rs8102137 and rs997669, from cell cycle pathways showed the strongest associations; the CCNE1 signal at 19q12 has already been reported in previous GWAS. These findings offer additional etiologic insights highlighting the specific genes and pathways associated with bladder cancer development. GSEA may be a complementary tool to GWAS to identify additional loci of cancer susceptibility.

  12. Multimedia Networking Improvements of an Embedded System: a Case Study

    Institute of Scientific and Technical Information of China (English)

    LIU Zhi-qing

    2005-01-01

    More and more embedded systems now support multimedia networking on the Ethernet or using the Wireless LAN (WLAN) technologies. An embedded system, typically designed with a low-performance microprocessor in order to reduce both power usage and cost, often shows poor performance on multimedia networking. This paper describes a case study of improving the TCP/IP networking performance of a real-world embedded uClinux multimedia system, which is configured with both a fast Ethernet and a Wi-Fi connection. This paper analyzes networking overhead of the embedded system, and provides specific methods to improve its networking performance based upon the analysis. Our benchmark results indicate that these methods can improve the multimedia networking throughput on the embedded system by about 15%.

  13. A Study On OFDM In Mobile Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Malik Nasereldin Ahmed

    2012-06-01

    Full Text Available Orthogonal Frequency Division Multiplexing (OFDM is the physical layer in emerging wireless local area networks that are also being targeted for ad hoc networking. OFDM can be also exploited in ad hoc networks to improve the energy performance of mobile devices. It is important in wireless networks because it can be used adaptively in a dynamically changing channel. This study gives a detailed view about OFDM and how it is useful to increase the bandwidth. This paper also gives an idea about how OFDM can be a promising technology for high capacity wireless communication.

  14. The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

    Directory of Open Access Journals (Sweden)

    Amir Rostami

    Full Text Available The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks.

  15. The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

    Science.gov (United States)

    Rostami, Amir; Mondani, Hernan

    2015-01-01

    The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks.

  16. Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks

    Science.gov (United States)

    Zhang, Xiaohang; Cui, Huiyuan; Zhu, Ji; Du, Yu; Wang, Qi; Shi, Wenhua

    2017-02-01

    In recent years, multiplex networks are becoming a research focus in the domain of complex networks. Discovering significant correlations between layers in multiplex networks can provide an insight to their structures. In this study, we propose some methods to measure the dissimilarities of different layers in directed and weighted multiplex networks. The dissimilarity is defined on two levels: node level and layer level. The node dissimilarity is computed based on the distance of the probability distribution of its link weights vectors in different layers; and the layer-level dissimilarity is the weighted sum of the nodes' dissimilarities. Furthermore, the dissimilarity is disintegrated into the connection-based dissimilarity and the weight-based dissimilarity, which represent the topological structure changes and the link weight changes, respectively. The proposed methods are applied to international trade networks.

  17. Earth Regimes Network Evolution Study (ERNESt): Introducing the Space Mobile Network

    Science.gov (United States)

    Menrad, Bob

    2016-01-01

    Speaker and Presenter at the Lincoln Laboratory Communications Workshop on April 5, 2016 at the Massachusetts Institute of Technology Lincoln Laboratory in Lexington, MA. A visual presentation titled Earth Regimes Network Evolution Study (ERNESt).

  18. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.

    Science.gov (United States)

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

  19. Alcohol intake and cigarette smoking and risk of a contralateral breast cancer: The Women's Environmental Cancer and Radiation Epidemiology Study

    DEFF Research Database (Denmark)

    Knight, J.A.; Bernstein, L.; Largent, J.

    2009-01-01

    Women with primary breast cancer are at increased risk of developing second primary breast cancer. Few studies have evaluated risk factors for the development of asynchronous contralateral breast cancer in women with breast cancer. In the Women's Environmental Cancer and Radiation Epidemiology St...

  20. Biomechanical cell regulatory networks as complex adaptive systems in relation to cancer.

    Science.gov (United States)

    Feller, Liviu; Khammissa, Razia Abdool Gafaar; Lemmer, Johan

    2017-01-01

    Physiological structure and function of cells are maintained by ongoing complex dynamic adaptive processes in the intracellular molecular pathways controlling the overall profile of gene expression, and by genes in cellular gene regulatory circuits. Cytogenetic mutations and non-genetic factors such as chronic inflammation or repetitive trauma, intrinsic mechanical stresses within extracellular matrix may induce redirection of gene regulatory circuits with abnormal reactivation of embryonic developmental programmes which can now drive cell transformation and cancer initiation, and later cancer progression and metastasis. Some of the non-genetic factors that may also favour cancerization are dysregulation in epithelial-mesenchymal interactions, in cell-to-cell communication, in extracellular matrix turnover, in extracellular matrix-to-cell interactions and in mechanotransduction pathways. Persistent increase in extracellular matrix stiffness, for whatever reason, has been shown to play an important role in cell transformation, and later in cancer cell invasion. In this article we review certain cell regulatory networks driving carcinogenesis, focussing on the role of mechanical stresses modulating structure and function of cells and their extracellular matrices.

  1. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors.

    Science.gov (United States)

    Sasaki, Masao S; Tachibana, Akira; Takeda, Shunichi

    2014-05-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the 'integrate-and-fire' algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to (239)Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation-environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking.

  2. Identification of calgranulin B interacting proteins and network analysis in gastrointestinal cancer cells

    Science.gov (United States)

    Yoo, Byong Chul

    2017-01-01

    Calgranulin B is known to be involved in tumor development, but the underlying molecular mechanism is not clear. To gain insight into possible roles of calgranulin B, we screened for calgranulin B-interacting molecules in the SNU-484 gastric cancer and the SNU-81 colon cancer cells. Calgranulin B-interacting partners were identified by yeast two-hybrid and functional information was obtained by computational analysis. Most of the calgranulin B-interacting partners were involved in metabolic and cellular processes, and found to have molecular function of binding and catalytic activities. Interestingly, 46 molecules in the network of the calgranulin B-interacting proteins are known to be associated with cancer and FKBP2 was found to interact with calgranulin B in both SNU-484 and SNU-81 cells. Polyubiquitin-C encoded by UBC, which exhibited an interaction with calgranulin B, has been associated with various molecules of the extracellular space and plasma membrane identified in our screening, including Na-K-Cl cotransporter 1 and dystonin in SNU-484 cells, and ATPase subunit beta-1 in SNU-81 cells. Our data provide novel insight into the roles of calgranulin B of gastrointestinal cancer cells, and offer new clues suggesting calgranulin B acts as an effector molecule through which the cell can communicate with the tumor microenvironment via polyubiquitin-C. PMID:28152021

  3. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    Science.gov (United States)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  4. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2008-06-01

    Full Text Available Abstract Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.

  5. Network model explains why cancer cells use inefficient pathway to produce energy

    Science.gov (United States)

    Lee, Joo Sang; Marko, John; Motter, Adilson

    2012-02-01

    The Warburg effect---the use of the (energetically inefficient) fermentative pathway as opposed to (energetically efficient) respiration even in the presence of oxygen---is a common property of cancer metabolism. Here, we propose that the Warburg effect is in fact a consequence of a trade-off between the benefit of rapid growth and the cost for protein synthesis. Using genome-scale metabolic networks, we have modeled the cellular resources for protein synthesis as a growth defect that increases with enzyme concentration. Based on our model, we demonstrate that the cost of protein production during rapid growth drives the cell to rely on fermentation to produce ATP. We also identify an intimate link between extensive fermentation and rapid biosynthesis. Our findings emphasize the importance of protein synthesis as a limiting factor on cell proliferation and provide a novel mathematical framework to analyze cancer metabolism.

  6. Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer.

    Science.gov (United States)

    Liu, Rong; Lv, Qiao-Li; Yu, Jing; Hu, Lei; Zhang, Li-Hua; Cheng, Yu; Zhou, Hong-Hao

    2015-06-01

    We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines.

  7. Occupational asbestos exposure and risk of pleural mesothelioma, lung cancer, and laryngeal cancer in the prospective netherlands cohort study

    NARCIS (Netherlands)

    Offermans, N.S.M.; Vermeulen, R.; Burdorf, A.; Goldbohm, R.A.; Kauppinen, T.; Kromhout, H.; Brandt, P.A. van den

    2014-01-01

    OBJECTIVE:: To study the association between occupational asbestos exposure and pleural mesothelioma, lung cancer, and laryngeal cancer, specifically addressing risk associated with the lower end of the exposure distribution, risk of cancer subtypes, and the interaction between asbestos and smoking.

  8. Antigua/Barbuda Cancer Mortality Study

    Directory of Open Access Journals (Sweden)

    GS Daniel

    2014-10-01

    Full Text Available Objective: To determine the cancer mortality rates in Antigua and Barbuda in an effort to enhance the profile of the country’s cancer burden. Method: Available data for 2001 to 2005 were analysed to obtain cancer mortality rates. Analysis was also made of the mortality/incidence ratios. Results: There were 354 cancer deaths – 208 males (age standardized rates (ASR 111.9 and 146 females (ASR 66.3. The main causes were prostate (ASR 53 and breast (ASR 22. The mortality rates for cancers of the lung (ASR 5.09 males, 2.49 females and brain/nervous system (ASR 0.45 males, 1.7 females were significantly lower than those in the Caribbean. Conclusion: Mortality rates were highest for sex-specific cancers, accounting for more than 50% of cancer deaths.

  9. Cost effective Internet access and video conferencing for a community cancer network.

    Science.gov (United States)

    London, J W; Morton, D E; Marinucci, D; Catalano, R; Comis, R L

    1995-01-01

    Utilizing the ubiquitous personal computer as a platform, and Integrated Services Digital Network (ISDN) communications, cost effective medical information access and consultation can be provided for physicians at geographically remote sites. Two modes of access are provided: information retrieval via the Internet, and medical consultation video conferencing. Internet access provides general medical information such as current treatment options, literature citations, and active clinical trials. During video consultations, radiographic and pathology images, and medical text reports (e.g., history and physical, pathology, radiology, clinical laboratory reports), may be viewed and simultaneously annotated by either video conference participant. Both information access modes have been employed by physicians at community hospitals which are members of the Jefferson Cancer Network, and oncologists at Thomas Jefferson University Hospital. This project has demonstrated the potential cost effectiveness and benefits of this technology.

  10. Discovering Study-Specific Gene Regulatory Networks

    OpenAIRE

    2014-01-01

    This article has been made available through the Brunel Open Access Publishing Fund. This article has been made available through the Brunel Open Access Publishing Fund. Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus appro...

  11. Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems.

    Directory of Open Access Journals (Sweden)

    Ryan Tasseff

    Full Text Available Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK

  12. Error Propagation in Geodetic Networks Studied by FEMLAB

    DEFF Research Database (Denmark)

    Borre, Kai

    2009-01-01

    thousand points. This leads to so large matrix problems that one starts thinking of using continous network models. They result in one or more differential equations with corresponding boundary conditions. The Green’s function works like the covariance matrix in the discrete case. If we can find the Green......’s function we also can study error propagation through large networks. Exactly this idea is exploited for error propagation studies in large geodetic networks. To solve the boundary value problems we have used the FEMLAB software. It is a powerful tool for this type of problems. The M-file was created...... and estimate the solution by using the principle of least squares. Contemporary networks often contain several thousand points. This leads to so large matrix problems that one starts thinking of using continous network models. They result in one or more differential equations with corresponding boundary...

  13. Microfabricated platforms for the study of neuronal and cellular networks

    Energy Technology Data Exchange (ETDEWEB)

    Berdondini, L; Generelli, S; Kraus, T; Guenat, O T; Koster, S; Linder, V; Koudelka-Hep, M; Rooij, N F de [SAMLAB, Institute of Microtechnology, University of Neuchatel (Switzerland)

    2006-04-01

    In this contribution we present the development of three microfabricated devices for the study of neuronal and cellular networks. Together, these devices form an attractive toolbox, which is useful to stimulate and record signals of both electrical and chemical nature. One approach consist of microelectrode arrays for the study of neuronal networks, and allow for the electrical stimulation of individual cells in the network, while the other electrodes of the array record the electrical activity of the remaining cells of the network. We also present the use of micropipettes that can measure the extra- and intracellular concentrations of ions in cells cultures. A third approach exploits the laminar flows in a microfluidic device, to deliver minute amounts of drug to some cells in a cellular network. These three illustrations show that microfabricated platforms are appealing analytical tools in the context of cell biology.

  14. Privacy Practices of Health Social Networking Sites: Implications for Privacy and Data Security in Online Cancer Communities.

    Science.gov (United States)

    Charbonneau, Deborah H

    2016-08-01

    While online communities for social support continue to grow, little is known about the state of privacy practices of health social networking sites. This article reports on a structured content analysis of privacy policies and disclosure practices for 25 online ovarian cancer communities. All of the health social networking sites in the study sample provided privacy statements to users, yet privacy practices varied considerably across the sites. The majority of sites informed users that personal information was collected about participants and shared with third parties (96%, n = 24). Furthermore, more than half of the sites (56%, n = 14) stated that cookies technology was used to track user behaviors. Despite these disclosures, only 36% (n = 9) offered opt-out choices for sharing data with third parties. In addition, very few of the sites (28%, n = 7) allowed individuals to delete their personal information. Discussions about specific security measures used to protect personal information were largely missing. Implications for privacy, confidentiality, consumer choice, and data safety in online environments are discussed. Overall, nurses and other health professionals can utilize these findings to encourage individuals seeking online support and participating in social networking sites to build awareness of privacy risks to better protect their personal health information in the digital age.

  15. Efficacy and Safety of HER2-Targeted Agents for Breast Cancer with HER2-Overexpression: A Network Meta-Analysis.

    Directory of Open Access Journals (Sweden)

    Qiuyan Yu

    Full Text Available Clinical trials of human epidermal growth factor receptor 2 (HER2-targeted agents added to standard treatment have been efficacious for HER2-positive (HER2+ advanced breast cancer. To our knowledge, no meta-analysis has evaluated HER2-targeted therapy including trastuzumab emtansine (T-DM1 and pertuzumab for HER2-positive breast caner and ranked the targeted treatments. We performed a network meta-analysis of both direct and indirect comparisons to evaluate the effect of adding HER2-targeted agents to standard treatment and examined side effects.We performed a Bayesian-framework network meta-analysis of randomized controlled trials to compare 6 HER2-targeted treatment regimens and 1 naïve standard treatment (NST, without any-targeted drugs in targeted treatment of HER2+ breast cancer in adults. These treatment regimens were T-DM1, LC (lapatinib, HC (trastuzumab, PEC (pertuzumab, LHC (lapatinib and trastuzumab, and PEHC (pertuzumab and trastuzumab. The main outcomes were overall survival and response rates. We also examined side effects of rash, LVEF (left ventricular ejection fraction, fatigue, and gastrointestinal disorders, and performed subgroup analysis for the different treatment regimens in metastatic or advanced breast cancer.We identified 25 articles of 21 trials, with data for 11,276 participants. T-DM1 and PEHC were more efficient drug regimens with regard to overall survival as compared with LHC, LC, HC and PEC. The incidence of treatment-related rash occurs more frequently in the patients who received LC treatment regimen than PEHC and T-DM1 and HC. In subgroup analysis, T-DM1 was associated with increased overall survival as compared with LC and HC. PEHC was associated with increased overall response as compared with LC, HC, and NST.Overall, the regimen of T-DM1 as well as pertuzumab in combination with trastuzumab and docetaxel is efficacious with fewer side effects as compared with other regimens, especially for advanced HER2

  16. Preface: Recent progress from networked studies based around MST radar

    Science.gov (United States)

    Hocking, Wayne K.; Lehmann, Volker; Singer, Werner; Yamamoto, Masayuki

    2014-10-01

    Studies of the mesosphere, stratosphere, and troposphere by radar, application of networks, and multi-instrument studies have grown significantly in recent years, and have covered a wide range of areas in technology, fundamental research, and application. This special issue of the Journal of Atmospheric and Solar-Terrestrial Physics on "Recent progress from networked studies based around MST radar" focuses primarily on selected papers presented at the 13th International Workshops on Scientific and Technical Aspects of MST Radar (MST13).

  17. Screening study on new tumor marker periplakin for lung cancer

    Institute of Scientific and Technical Information of China (English)

    Shuqin Dai; Wei Li; Mian Kong; Yuzhen Zheng; Shuying Chen; Junye Wang; Linquan Zang

    2013-01-01

    Objective: The aim of this study was to use lung cancer targeting binding polypeptide ZS-9 to screen cDNA library of human lung cancer and obtain ZS-9 specific ligand to confirm tumor marker of non small-cell lung cancer. Methods: Artificially synthesize biotin labeled peptide ZS-9, anchored ZS-9 in the enzyme label plate coupled by avidin, used ZS-9 as probe to screen cDNA library of human lung cancer, after screening, obtained bacteriophage clone specifically binding with anchored polypeptide ZS-9. Extracted plasmid of bacteriophage and performed sequencing after amplified by PCR. Results: It was demonstrated by bioinformatic analysis on the sequence of ligand binded by lung cancer specific peptide ZS-9 that the ligand was the cytoskeletal protein periplakin on the surface of lung cancer cells, suggesting that periplakin might be a new marker for non-small-cell lung cancer in lung cancer. Conclusion: Use specific lung cancer binding peptide to screen new tumor marker periplakin in lung cancer and further studies on its biologic functions in genesis and development of lung cancer are still needed.

  18. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients.

    Science.gov (United States)

    Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin

    2013-09-01

    The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

  19. Awareness regarding female breast cancer in Kashmiri males - A study

    Directory of Open Access Journals (Sweden)

    Sajad Ahmad Salati

    2010-04-01

    Full Text Available Breast cancer is a major killer disease in females globally and in developing regions, where the early cancer detection facilities are unavailable, prognosis is even worse. Awareness about this disease can lead to early detection and thereby decrease the morbidity and mortality. A self designed questionnaire was used to study the level of awareness regarding breast cancer among males. The questionnaire had 15 questions and on the basis on score attained, the subjects were classified as having poor, average or good breast cancer awareness. Out of 624 participants, 555(89% had poor breast cancer awareness and 47(7.5% had average awareness. Only 22 (3.5% had good awareness about breast cancer. The level of awareness regarding female breast cancer in Kashmiri males is very low. Measures need to be taken to spread awareness about this disease in males so that they can play a vital role in early detection of this disease.

  20. Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

    Directory of Open Access Journals (Sweden)

    Sharma Animesh

    2007-01-01

    Full Text Available Abstract Background The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable

  1. The Heritability of Prostate Cancer in the Nordic Twin Study of Cancer

    DEFF Research Database (Denmark)

    Hjelmborg, Jacob B; Scheike, Thomas; Holst, Klaus;

    2014-01-01

    Background: Prostate cancer is thought to be the most heritable cancer, although little is known about how this genetic contribution varies across age. Methods: To address this question, we undertook the world's largest prospective study in the Nordic Twin Study of Cancer cohort, including 18...... risk and liability. Results: The cumulative risk of prostate cancer was similar to that of the background population. The cumulative risk for twins whose co-twin was diagnosed with prostate cancer was greater for MZ than for DZ twins across all ages. Among concordantly affected pairs, the time between...... diagnoses was significantly shorter for MZ than DZ pairs (median 3.8 versus 6.5 years, respectively). Genetic differences contributed substantially to variation in both the risk and the liability (heritability=58% (95% CI 52%-63%) of developing prostate cancer. The relative contribution of genetic factors...

  2. Study protocol: The Intensive Care Outcome Network ('ICON' study

    Directory of Open Access Journals (Sweden)

    Barber Vicki S

    2008-06-01

    Full Text Available Abstract Background Extended follow-up of survivors of ICU treatment has shown many patients suffer long-term physical and psychological consequences that affect their health-related quality of life. The current lack of rigorous longitudinal studies means that the true prevalence of these physical and psychological problems remains undetermined. Methods/Design The ICON (Intensive Care Outcome Network study is a multi-centre, longitudinal study of survivors of critical illness. Patients will be recruited prior to hospital discharge from 20–30 ICUs in the UK and will be assessed at 3, 6, and 12 months following ICU discharge for health-related quality of life as measured by the Short Form-36 (SF-36 and the EuroQoL (EQ-5D; anxiety and depression as measured by the Hospital Anxiety and Depression Scale (HADS; and post traumatic stress disorder (PTSD symptoms as measured by the PTSD Civilian Checklist (PCL-C. Postal questionnaires will be used. Discussion The ICON study will create a valuable UK database detailing the prevalence of physical and psychological morbidity experienced by patients as they recover from critical illness. Knowledge of the prevalence of physical and psychological morbidity in ICU survivors is important because research to generate models of causality, prognosis and treatment effects is dependent on accurate determination of prevalence. The results will also inform economic modelling of the long-term burden of critical illness. Trial Registration ISRCTN69112866

  3. Breast cancer screening case-control study design: impact on breast cancer mortality

    NARCIS (Netherlands)

    Paap, E.; Verbeek, A.L.M.; Puliti, D.; Paci, E.; Broeders, M.J.M.

    2011-01-01

    BACKGROUND: Recent case-control studies on the effectiveness of population-based breast cancer screening show differences in the magnitude of breast cancer mortality reduction. We investigated the role played by aspects of the case-control study design on these differences, e.g. the definition of ca

  4. The Lymphedema and Gynecologic Cancer (LEG) Study: Incidence, Risk Factors, and | Division of Cancer Prevention

    Science.gov (United States)

    DESCRIPTION (provided by applicant): The proposed study, "Lymphedema and Gynecologic cancer (LEG): Incidence, Risk Factors and Impact", will innovatively utilize the cooperative group setting of the GOG (Gynecologic Oncology Group) to prospectively study 1300 women newly diagnosed with cervical, endometrial, or vulvar cancer to determine the incidence and impact of lower extremity lymphedema following surgical treatment of these diseases. |

  5. Etiology and Early Marker Studies (EEMS) | Division of Cancer Prevention

    Science.gov (United States)

    The Etiology and Early Marker Studies (EEMS) is a component of the PLCO Trial. By collecting biologic materials and risk factor information from trial participants before the diagnosis of disease, PLCO EEMS adds substantial value to the trial, providing a resource for cancer research, focused, in particular, on cancer etiology and early markers. Etiologic studies investigate the environmental, biochemical and genetic risk factors for cancer. Early detection studies aim to develop reproducible, diagnostics-ready biomarkers of early disease. | Risk factor data and biospecimens collected before the diagnosis of disease from participants in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

  6. Small-world networks of residue interactions in the Abl kinase complexes with cancer drugs: topology of allosteric communication pathways can determine drug resistance effects.

    Science.gov (United States)

    Tse, A; Verkhivker, G M

    2015-07-01

    The human protein kinases play a fundamental regulatory role in orchestrating functional processes in complex cellular networks. Understanding how conformational equilibrium between functional kinase states can be modulated by ligand binding or mutations is critical for quantifying molecular basis of allosteric regulation and drug resistance. In this work, molecular dynamics simulations of the Abl kinase complexes with cancer drugs (Imatinib and Dasatinib) were combined with structure-based network modeling to characterize dynamics of the residue interaction networks in these systems. The results have demonstrated that structural architecture of kinase complexes can produce a small-world topology of the interaction networks. Our data have indicated that specific Imatinib binding to a small number of highly connected residues could lead to network-bridging effects and allow for efficient allosteric communication, which is mediated by a dominant pathway sensitive to the unphosphorylated Abl state. In contrast, Dasatinib binding to the active kinase form may activate a broader ensemble of allosteric pathways that are less dependent on the phosphorylation status of Abl and provide a better balance between the efficiency and resilience of signaling routes. Our results have unveiled how differences in the residue interaction networks and allosteric communications of the Abl kinase complexes can be directly related to drug resistance effects. This study offers a plausible perspective on how efficiency and robustness of the residue interaction networks and allosteric pathways in kinase structures may be associated with protein responses to drug binding.

  7. Inflammation and fatigue dimensions in advanced cancer patients and cancer survivors: An explorative study

    OpenAIRE

    2012-01-01

    textabstractBACKGROUND: Inflammation may underlie cancer-related fatigue; however, there are no studies that assess the relation between fatigue and cytokines in patients with advanced disease versus patients without disease activity. Furthermore, the relation between cytokines and the separate dimensions of fatigue is unknown. Here, association of plasma levels of inflammatory markers with physical fatigue and mental fatigue was explored in advanced cancer patients and cancer survivors. METH...

  8. Brain network dynamics underlying visuospatial judgment: an FMRI connectivity study.

    Science.gov (United States)

    de Graaf, Tom A; Roebroeck, Alard; Goebel, Rainer; Sack, Alexander T

    2010-09-01

    Previous functional imaging research has consistently indicated involvement of bilateral fronto-parietal networks during the execution of visuospatial tasks. Studies with TMS have suggested that the right hemispheric network, but not the left, is functionally relevant for visuospatial judgments. However, very little is still known about the interactions within these fronto-parietal networks underlying visuospatial processing. In the current study, we investigated task modulation of functional connectivity (instantaneous correlations of regional time courses), and task-specific effective connectivity (direction of influences), within the right fronto-parietal network activated during visuospatial judgments. Ten healthy volunteers performed a behaviorally controlled visuospatial judgment task (ANGLE) or a control task (COLOR) in an fMRI experiment. Visuospatial task-specific activations were found in posterior parietal cortex (PPC) and middle/inferior frontal gyrus (MFG). Functional connectivity within this network was task-modulated, with significantly higher connectivity between PPC and MFG during ANGLE than during COLOR. Effective connectivity analysis for directed influence revealed that visuospatial task-specific projections within this network were predominantly in a frontal-to-parietal direction. Moreover, ANGLE-specific influences from thalamic nuclei to PPC were identified. Exploratory effective connectivity analysis revealed that closely neighboring clusters, within visuospatial regions, were differentially involved in the network. These neighboring clusters had opposite effective connectivity patterns to other nodes of the fronto-parietal network. Our data thus reveal that visuospatial judgments are supported by massive fronto-parietal backprojections, thalamo-parietal influence, and multiple stages, or loops, of information flow within the visuospatial network. We speculate on possible functional contributions of the various network nodes and

  9. Language networks in children: Evidence from functional MRI studies

    OpenAIRE

    2009-01-01

    We review functional MRI and other neuroimaging studies of language skills in children from infancy to adulthood. These studies show developmental changes in the networks of brain regions supporting language, which can be affected by brain injuries or neurological disorders. Particular aspects of language rely on networks that lateralize to the dominant hemisphere; others rely on bilateral or non-dominant mechanisms. Multiple fMRI tasks for pediatric patients characterize functional brain reo...

  10. Evaluation of exposure to pioglitazone and risk of prostate cancer: a nested case–control study

    Science.gov (United States)

    Boxall, Naomi; Bennett, Dimitri; Hunger, Matthias; Dolin, Paul; Thompson, Paula L

    2016-01-01

    Objectives Investigate potential association between pioglitazone exposure and risk of prostate cancer. Research design and methods Nested, matched case–control study. UK primary care data (Clinical Practice Research Datalink (CPRD) GOLD) linked to inpatient (Hospital Episode Statistics (HES)) and cancer registry (National Cancer Information Network (NCIN)) data. English men aged ≥40 years diagnosed with type 2 diabetes mellitus, January 1, 2001 to January 5, 2015. Cases, with prostate cancer diagnosis, matched with up to 4 controls by age, cohort entry date and region. ORs for association of exposure to pioglitazone to incident prostate cancer, adjusted for potential confounders. Results From a cohort of 47 772 men with 243 923 person-years follow-up, 756 definite cases of prostate cancer were identified. Incidence was 309.9/100 000 person-years (95% CI 288.6 to 332.8). Pioglitazone use was not associated with prostate cancer risk; adjusted OR 0.759, 95% CI 0.502 to 1.148. Analyses showed no difference when possible cases, prostate cancer in CPRD GOLD only, included (adjusted OR 0.726, 95% CI 0.510 to 1.034). No association when adjusted for channeling bias (OR 0.778, 95% CI 0.511 to 1.184) or limited to an index date prior to July 1, 2011 (adjusted OR 0.508, 95% CI 0.294 to 0.879), despite prostate-specific antigen screening occurring more frequently among cases than controls (81.6% of 756 definite cases cf. 24.2% of 2942 controls (ppioglitazone use, increasing pioglitazone dose or increasing time since initiation. Conclusions In this real-world, nested matched case–control study, exposure to pioglitazone was not associated with increased risk of prostate cancer. PMID:28074141

  11. [Studies on trace elements in cancerous stomach tissue of the patients with stomach cancer].

    Science.gov (United States)

    Kobayashi, M

    1990-05-01

    This study was performed to find out whether copper, zinc, manganese, selenium and iron concentrations in the cancerous and normal stomach tissues of the patients with stomach cancer vary within the malignant stages and Borrmann classification or not, and to investigate the interaction of copper, zinc, manganese, selenium and iron concentrations in blood of these patients. Copper concentration in cancerous tissues was not statistically significant as compared with normal tissues. Plasma and whole blood copper concentration of Stage IV showed a significant higher level than that of stage I. Zinc concentration in cancerous tissues was not statistically significant as compared with normal tissues. Selenium concentration in cancerous tissues showed a statistically significant high level as compared with that in normal tissues. Plasma selenium concentration of Stage III showed a significant lower level than that of stage I. Iron concentration in cancerous tissues showed a significantly lower level than that in normal tissues at stage IV. Whole blood iron concentration was low levels in proportion to the progress of stomach cancer. The correlation of selenium concentration between in cancerous tissues and in whole blood of these patients was significant with the correlation coefficient of 0.340. The correlation of iron concentration between in cancerous tissues and in whole blood of these patients was significant with the correlation coefficient of 0.423. The correlation between iron concentration in cancerous tissues and hemoglobin concentration in whole blood of these patients was significant with the correlation coefficient of 0.361.

  12. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

    Science.gov (United States)

    Poornima, Paramasivan; Kumar, Jothi Dinesh; Zhao, Qiaoli; Blunder, Martina; Efferth, Thomas

    2016-09-01

    Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

  13. Soyfood intake and breast cancer survival: a followup of the Shanghai Breast Cancer Study.

    Science.gov (United States)

    Boyapati, Sonia M; Shu, Xiao-ou; Ruan, Zhi Xian; Dai, Qi; Cai, Qiuyin; Gao, Yu-tang; Zheng, Wei

    2005-07-01

    Soy and its constituents have been shown in many in vivo and in vitro studies and in some epidemiological studies to have anti-cancer effects. Some soy constituents, however, also stimulate cell proliferation, which has raised concerns in promoting soy intake among breast cancer survivors. To investigate whether soy intake may be associated with breast cancer survival, we evaluated data from a cohort of 1459 breast cancer patients who participated in the Shanghai Breast Cancer Study between 1996 and 1998. Usual soy food intake was assessed using a validated food frequency questionnaire at baseline. The median follow-up time for this cohort of women was 5.2 years. We found that soy intake prior to cancer diagnosis was unrelated to disease-free breast cancer survival (adjusted hazard ratio [HR]=0.99, 95% confidence interval [CI], 0.73-1.33 for the highest tertile compared to the lowest tertile). The association between soy protein intake and breast cancer survival did not differ according to ER/PR status, tumor stage, age at diagnosis, body mass index (BMI), waist to hip ratio (WHR), or menopausal status. Additionally, the soy-survival association did not appear to vary according to XbaI or PvuII polymorphisms in ER-alpha, or C(14206)T, G(25652)A, or A(50766)G polymorphisms in ER-beta. These data suggest that soyfoods do not have an adverse effect on breast cancer survival.

  14. Personal and Network Dynamics in Performance of Knowledge Workers: A Study of Australian Breast Radiologists.

    Directory of Open Access Journals (Sweden)

    Seyedamir Tavakoli Taba

    Full Text Available In this paper, we propose a theoretical model based upon previous studies about personal and social network dynamics of job performance. We provide empirical support for this model using real-world data within the context of the Australian radiology profession. An examination of radiologists' professional network topology through structural-positional and relational dimensions and radiologists' personal characteristics in terms of knowledge, experience and self-esteem is provided. Thirty one breast imaging radiologists completed a purpose designed questionnaire regarding their network characteristics and personal attributes. These radiologists also independently read a test set of 60 mammographic cases: 20 cases with cancer and 40 normal cases. A Jackknife free response operating characteristic (JAFROC method was used to measure the performance of the radiologists' in detecting breast cancers.Correlational analyses showed that reader performance was positively correlated with the social network variables of degree centrality and effective size, but negatively correlated with constraint and hierarchy. For personal characteristics, the number of mammograms read per year and self-esteem (self-evaluation positively correlated with reader performance. Hierarchical multiple regression analysis indicated that the combination of number of mammograms read per year and network's effective size, hierarchy and tie strength was the best fitting model, explaining 63.4% of the variance in reader performance. The results from this study indicate the positive relationship between reading high volumes of cases by radiologists and expertise development, but also strongly emphasise the association between effective social/professional interactions and informal knowledge sharing with high performance.

  15. Bayesian networks: a powerful tool for systems biology study

    Institute of Scientific and Technical Information of China (English)

    Xiu-Jie WANG

    2010-01-01

    @@ Higher Education Press and Springer-Verlag Berlin Heidelberg 2010The wide application of omics research approaches caused a burst of biological data in the past decade, and also promoted the growth of systems biology, a research field that studies biological questions from a genome-wide point of view. One feature of systems biology study is to integrate and identify. Not only experiments are carried out at whole-genome scales, but also data from various resources, such as genomics, transcriptomics, proteomics,and metabolics data, need to be integrated to identify correlations among targeted entities. Therefore, plenty amounts of experimental data, robust statistical methods, and reliable network construction models are indispensable for systems biology study. Among the available network construction models, Bayesian network is considered as one of the most effective methods available so far for biological network predictions (Pe'er, 2005).

  16. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Lv Jie

    2011-10-01

    Full Text Available Abstract Background As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. Results We constructed a weighted human PPI network (WHPN using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching Pub

  17. Identification of key genes associated with colorectal cancer based on the transcriptional network.

    Science.gov (United States)

    Chen, Guoting; Li, Hengping; Niu, Xianping; Li, Guofeng; Han, Ning; Li, Xin; Li, Guang; Liu, Yangzhou; Sun, Guixin; Wang, Yong; Li, Zengchun; Li, Qinchuan

    2015-07-01

    Colorectal cancer (CRC) is among the most lethal human cancers, but the mechanism of the cancer is still unclear enough. We aimed to explore the key genes in CRC progression. The gene expression profile (GSE4183) of CRC was obtained from Gene Expression Omnibus database which included 8 normal samples, 15 adenoma samples, 15 CRC samples and 15 inflammatory bowel disease (IBD) samples. Thereinto, 8 normal, 15 adenoma, and 15 CRC samples were chosen for our research. The differentially expressed genes (DEGs) in normal vs. adenoma, normal vs. CRC, and adenoma vs. CRC, were identified using the Wilcoxon test method in R respectively. The interactive network of DEGs was constructed to select the significant modules using the Pearson's correlation. Meanwhile, transcriptional network of DEGs was also constructed using the g: Profiler. Totally, 2,741 DEGs in normal vs. adenoma, 1,484 DEGs in normal vs. CRC, and 396 DEGs in adenoma vs. CRC were identified. Moreover, function analysis of DEGs in each group showed FcR-mediated phagocytosis pathway in module 1, cardiac muscle contraction pathway in module 6, and Jak-STAT signaling pathway in module 19 were also enriched. Furthermore, MZF1 and AP2 were the transcription factor in module 6, with the target SP1, while SP1 was also a transcription in module 20. DEGs like NCF1, AKT, SP1, AP2, MZF1, and TPM might be used as specific biomarkers in CRC development. Therapy targeting on the functions of these key genes might provide novel perspective for CRC treatment.

  18. Chemotherapeutic prevention studies of prostate cancer

    DEFF Research Database (Denmark)

    Djavan, Bob; Zlotta, Alexandre; Schulman, Claude;

    2004-01-01

    Despite advances in the detection and management of prostate cancer, this disease remains a major cause of morbidity and mortality in men. Increasing attention has focused on the role of chemoprevention for prostate cancer, ie the administration of agents that inhibit 1 or more steps in the natur...

  19. Primary cultures of human colon cancer as a model to study cancer stem cells.

    Science.gov (United States)

    Koshkin, Sergey; Danilova, Anna; Raskin, Grigory; Petrov, Nikolai; Bajenova, Olga; O'Brien, Stephen J; Tomilin, Alexey; Tolkunova, Elena

    2016-09-01

    The principal cause of death in cancer involves tumor progression and metastasis. Since only a small proportion of the primary tumor cells, cancer stem cells (CSCs), which are the most aggressive, have the capacity to metastasize and display properties of stem cells, it is imperative to characterize the gene expression of diagnostic markers and to evaluate the drug sensitivity in the CSCs themselves. Here, we have examined the key genes that are involved in the progression of colorectal cancer and are expressed in cancer stem cells. Primary cultures of colorectal cancer cells from a patient's tumors were studied using the flow cytometry and cytological methods. We have evaluated the clinical and stem cell marker expression in these cells, their resistance to 5-fluorouracil and irinotecan, and the ability of cells to form tumors in mice. The data shows the role of stem cell marker Oct4 in the resistance of primary colorectal cancer tumor cells to 5-fluorouracil.

  20. Risk of cancer after blood transfusion from donors with subclinical cancer: a retrospective cohort study

    DEFF Research Database (Denmark)

    Edgren, Gustaf; Hjalgrim, Henrik; Reilly, Marie

    2007-01-01

    BACKGROUND: Although mechanisms for detection of short-term complications after blood transfusions are well developed, complications with delayed onset, notably transmission of chronic diseases such as cancer, have been difficult to assess. Our aim was to investigate the possible risk of cancer...... transmission from blood donors to recipients through blood transfusion. METHODS: We did a register-based retrospective cohort study of cancer incidence among patients who received blood from donors deemed to have a subclinical cancer at the time of donation. These precancerous donors were diagnosed...... with a cancer within 5 years of the donation. Data from all computerised blood bank registers in Sweden and Denmark gathered between 1968 and 2002 were merged into a common database. Demographic and medical data, including mortality and cancer incidence, were ascertained through linkages with nationwide...

  1. Graphic Narratives and Cancer Prevention: A Case Study of an American Cancer Society Comic Book.

    Science.gov (United States)

    Krakow, Melinda

    2017-05-01

    As the interest in graphic medicine grows, health communicators have started engaging readers with compelling visual and textual accounts of health and illness, including via comic books. One context where comics have shown promise is cancer communication. This brief report presents an early example of graphic medicine developed by the American Cancer Society. "Ladies … Wouldn't It Be Better to Know?" is a comic book produced in the 1960s to provide the public with lay information about the Pap test for cervical cancer prevention and detection. An analysis of a key narrative attribute, plot development, illustrates the central role that perceived barriers played in this midcentury public health message, a component that remains a consideration of cancer communication design today. This case study of an early graphic narrative identifies promising cancer message features that can be used to address and refute barriers to cervical cancer screening and connects contemporary research with historical efforts in public health communication.

  2. Using Boolean Logic Modeling of Gene Regulatory Networks to Exploit the Links Between Cancer and Metabolism for Therapeutic Purposes.

    Science.gov (United States)

    Arshad, Osama A; Venkatasubramani, Priyadharshini S; Datta, Aniruddha; Venkatraj, Jijayanagaram

    2016-01-01

    The uncontrolled cell proliferation that is characteristically associated with cancer is usually accompanied by alterations in the genome and cell metabolism. Indeed, the phenomenon of cancer cells metabolizing glucose using a less efficient anaerobic process even in the presence of normal oxygen levels, termed the Warburg effect, is currently considered to be one of the hallmarks of cancer. Diabetes, much like cancer, is defined by significant metabolic changes. Recent epidemiological studies have shown that diabetes patients treated with the antidiabetic drug Metformin have significantly lowered risk of cancer as compared to patients treated with other antidiabetic drugs. We utilize a Boolean logic model of the pathways commonly mutated in cancer to not only investigate the efficacy of Metformin for cancer therapeutic purposes but also demonstrate how Metformin in concert with other cancer drugs could provide better and less toxic clinical outcomes as compared to using cancer drugs alone.

  3. Cancer Mortality in People Treated with Antidepressants before Cancer Diagnosis: A Population Based Cohort Study.

    Directory of Open Access Journals (Sweden)

    Yuelian Sun

    Full Text Available Depression is common after a cancer diagnosis and is associated with an increased mortality, but it is unclear whether depression occurring before the cancer diagnosis affects cancer mortality. We aimed to study cancer mortality of people treated with antidepressants before cancer diagnosis.We conducted a population based cohort study of all adults diagnosed with cancer between January 2003 and December 2010 in Denmark (N = 201,662. We obtained information on cancer from the Danish Cancer Registry, on the day of death from the Danish Civil Registry, and on redeemed antidepressants from the Danish National Prescription Registry. Current users of antidepressants were defined as those who redeemed the latest prescription of antidepressant 0-4 months before cancer diagnosis (irrespective of earlier prescriptions, and former users as those who redeemed the latest prescription five or more months before cancer diagnosis. We estimated an all-cause one-year mortality rate ratio (MRR and a conditional five-year MRR for patients who survived the first year after cancer diagnosis and confidence interval (CI using a Cox proportional hazards regression model. Overall, 33,111 (16.4% patients redeemed at least one antidepressant prescription in the three years before cancer diagnosis of whom 21,851 (10.8% were current users at the time of cancer diagnosis. Current antidepressant users had a 32% higher one-year mortality (MRR = 1.32, 95% CI: 1.29-1.35 and a 22% higher conditional five-year mortality (MRR = 1.22, 95% CI: 1.17-1.26 if patients survived the first year after the cancer diagnosis than patients not redeeming antidepressants. The one-year mortality was particularly high for patients who initiated antidepressant treatment within four months before cancer diagnosis (MRR = 1.54, 95% CI: 1.47-1.61. Former users had no increased cancer mortality.Initiation of antidepressive treatment prior to cancer diagnosis is common and is associated with an increased

  4. A study of reactor monitoring method with neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2001-03-01

    The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

  5. [Management of psychiatric inpatients with advanced cancer: a pilot study].

    Science.gov (United States)

    Rhondali, Wadih; Ledoux, Mathilde; Sahraoui, Fatma; Marotta, Juliette; Sanchez, Vincent; Filbet, Marilène

    2013-09-01

    The prevalence of cancer is not well established and probably underestimated in long-stay psychiatric inpatients. Psychiatric patients do not have the same access for cancer screening and care. Therapeutic decision-making is a real ethical problem. In this context, access to medical care should be provided by the establishment of guidelines and/or recommendations for this specific population. The aim of our study was to assess how cancer was managed among long term psychiatric inpatients. For this pilot study, we used a mixed methodology: a quantitative part with a retrospective chart review of cancer patients in a psychiatric institution and a qualitative part based on semi-structured interviews with psychiatrists with discourse analysis. Delay in cancer diagnosis can be explained by communication and behavior disorders, inadequate screening, and additional tests often refused by patients. Compliance and ethical issues (i.e. obtaining informed consent) are many pitfalls to optimal cancer care that should be explored in further research.

  6. Study examines outcomes from surgery to prevent ovarian cancer

    Science.gov (United States)

    A new study looked at women at high risk of ovarian cancer who had no clinical signs of the disease and who underwent risk-reducing salpingo-oophorectomy (RRSO). The study results showed cancer in the removed tissues of 2.6 percent (25 of 966) of the par

  7. Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer.

    Science.gov (United States)

    Huang, Ying; Ma, Jing; Porter, Alan L; Kwon, Seokbeom; Zhu, Donghua

    2015-01-01

    The rapid development of new and emerging science & technologies (NESTs) brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area - nano-enabled drug delivery (NEDD). NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1) international cooperation is increasing, but networking characteristics change over time; (2) highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3) research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

  8. Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD for brain cancer

    Directory of Open Access Journals (Sweden)

    Ying Huang

    2015-07-01

    Full Text Available The rapid development of new and emerging science & technologies (NESTs brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD. NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1 international cooperation is increasing, but networking characteristics change over time; (2 highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3 research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

  9. The hamster cheek pouch model for field cancerization studies.

    Science.gov (United States)

    Monti-Hughes, Andrea; Aromando, Romina F; Pérez, Miguel A; Schwint, Amanda E; Itoiz, Maria E

    2015-02-01

    External carcinogens, such as tobacco and alcohol, induce molecular changes in large areas of oral mucosa, which increase the risk of malignant transformation. This condition, known as 'field cancerization', can be detected in biopsy specimens using histochemical techniques, even before histological alterations are identified. The efficacy of these histochemical techniques as biomarkers of early cancerization must be demonstrated in appropriate models. The hamster cheek pouch oral cancer model, universally employed in biological studies and in studies for the prevention and treatment of oral cancer, is also an excellent model of field cancerization. The carcinogen is applied in solution to the surface of the mucosa and induces alterations that recapitulate the stages of cancerization in human oral mucosa. We have demonstrated that the following can be used for the early detection of cancerized tissue: silver staining of nucleolar organizer regions; the Feulgen reaction to stain DNA followed by ploidy analysis; immunohistochemical analysis of fibroblast growth factor-2, immunohistochemical labeling of proliferating cells to demonstrate an increase of epithelial cell proliferation in the absence of inflammation; and changes in markers of angiogenesis (i.e. those indicating vascular endothelial growth factor activity, endothelial cell proliferation and vascular density). The hamster cheek pouch model of oral cancer was also proposed and validated by our group for boron neutron capture therapy studies for the treatment of oral cancer. Clinical trials of this novel treatment modality have been performed and are underway for certain tumor types and localizations. Having demonstrated the efficacy of boron neutron capture therapy to control tumors in the hamster cheek pouch oral cancer model, we adapted the model for the long-term study of field cancerized tissue. We demonstrated the inhibitory effect of boron neutron capture therapy on tumor development in field

  10. VEGF, HIF-1α expression and MVD as an angiogenic network in familial breast cancer.

    Science.gov (United States)

    Saponaro, Concetta; Malfettone, Andrea; Ranieri, Girolamo; Danza, Katia; Simone, Giovanni; Paradiso, Angelo; Mangia, Anita

    2013-01-01

    Angiogenesis, which plays an important role in tumor growth and progression of breast cancer, is regulated by a balance between pro- and anti-angiogenic factors. Expression of vascular endothelial growth factor (VEGF) is up-regulated during hypoxia by hypoxia-inducible factor-1α (HIF-1α). It is known that there is an interaction between HIF-1α and BRCA1 carrier cancers, but little has been reported about angiogenesis in BRCA1-2 carrier and BRCAX breast cancers. In this study, we investigated the expression of VEGF and HIF-1α and microvessel density (MVD) in 26 BRCA1-2 carriers and 58 BRCAX compared to 77 sporadic breast cancers, by immunohistochemistry. VEGF expression in BRCA1-2 carriers was higher than in BRCAX cancer tissues (p = 0.0001). Furthermore, VEGF expression was higher in both BRCA1-2 carriers and BRCAX than the sporadic group (p<0.0001). VEGF immunoreactivity was correlated with poor tumor grade (p = 0.0074), hormone receptors negativity (p = 0.0206, p = 0.0002 respectively), and MIB-1-labeling index (p = 0.0044) in familial cancers (BRCA1-2 and BRCAX). The percentage of nuclear HIF-1α expression was higher in the BRCA1-2 carriers than in BRCAX cancers (p<0.05), and in all familial than in sporadic tumor tissues (p = 0.0045). A higher MVD was observed in BRCA1-2 carrier than in BRCAX and sporadic cancer tissues (p = 0.002, p = 0.0001 respectively), and in all familial tumors than in sporadic tumors (p = 0.01). MVD was positively related to HIF-1α expression in BRCA1-2 carriers (r = 0.521, p = 0.006), and, in particular, we observed a highly significant correlation in the familial group (r = 0.421, p<0.0001). Our findings suggest that angiogenesis plays a crucial role in BRCA1-2 carrier breast cancers. Prospective studies in larger BRCA1-2 carrier series are needed to improve the best therapeutic strategies for this subgroup of breast cancer patients.

  11. [Social network analysis and eating disorders: a study concerning blogs].

    Science.gov (United States)

    Bastianelli, Alessia; Spoto, Andrea; Vidotto, Giulio

    2011-01-01

    This study is aimed at analyzing the structure of relations among blogs referring to Eating Disorders (ED). Through the use Of Social Network Analysis (SNA) we investigated both the groups and their structure in order to study the social processes within the network. A formal analysis of the ED blogs' characteristics has been carried out. This analysis provided us with information about network Centrality and Cohesion parameters. Results allow us to highlight the most relevant blogs in the network. Even if the extremely variable nature of the blogs does not allow to have a precise picture of the blogosphere referring to ED, this first attempt to apply SNA in this field allowed us to suggest interesting remarks about EBD both from the research and from the social perspective.

  12. Studying Policy Transfer through the Lens of Social Network Analysis

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Brøgger, Katja; Steiner-Khamsi, Gita;

    Studying Policy Transfer through the Lens of Social Network Analysis The panelists present the findings of a joint empirical research project carried out at Aarhus University (DPU/Copenhagen) and at Teachers College, Columbia University (New York). The research project succeeded to identify...... discursive networks of political stakeholders and policy advisors that were considered key actors in the Danish school reform. The research team investigated how these networks interrelate, change over time, and represent different constituents (government, academe, business), at times contradicting...... or collaborating with each other, respectively. Against the backdrop of globalization studies in comparative education, the research project attempted to identify borrowers, translators, and brokers of educational reform drawing on a complementary set of expertise from social network analysis methodology (Oren...

  13. Religious networking organizations and social justice: an ethnographic case study.

    Science.gov (United States)

    Todd, Nathan R

    2012-09-01

    The current study provides an innovative examination of how and why religious networking organizations work for social justice in their local community. Similar to a coalition or community coordinating council, religious networking organizations are formal organizations comprised of individuals from multiple religious congregations who consistently meet to organize around a common goal. Based on over a year and a half of ethnographic participation in two separate religious networking organizations focused on community betterment and social justice, this study reports on the purpose and structure of these organizations, how each used networking to create social capital, and how religion was integrated into the organizations' social justice work. Findings contribute to the growing literature on social capital, empowering community settings, and the unique role of religious settings in promoting social justice. Implications for future research and practice also are discussed.

  14. Recurrent urinary tract infection and risk of bladder cancer in the Nijmegen bladder cancer study

    NARCIS (Netherlands)

    Vermeulen, S.; Hanum, N.; Grotenhuis, A.J.; Castano-Vinyals, G.; Heijden, A.G. van der; Aben, K.K.H.; Mysorekar, I.U.; Kiemeney, L.A.L.M.

    2015-01-01

    BACKGROUND: Controversy exists on whether urinary tract infection (UTI) is a risk factor for urinary bladder cancer (UBC). Here, the association is investigated using data from one of the largest bladder cancer case-control studies worldwide. METHODS: Information on (i) history and age at onset of r

  15. Constructing Bayesian networks by integrating gene expression and copy number data identifies NLGN4Y as a novel regulator of prostate cancer progression.

    Science.gov (United States)

    Gong, Yixuan; Wang, Li; Chippada-Venkata, Uma; Dai, Xudong; Oh, William K; Zhu, Jun

    2016-10-18

    To understand the heterogeneity of prostate cancer (PCa) and identify novel underlying drivers, we constructed integrative molecular Bayesian networks (IMBNs) for PCa by integrating gene expression and copy number alteration data from published datasets. After demonstrating such IMBNs with superior network accuracy, we identified multiple sub-networks within IMBNs related to biochemical recurrence (BCR) of PCa and inferred the corresponding key drivers. The key drivers regulated a set of common effectors including genes preferentially expressed in neuronal cells. NLGN4Y-a protein involved in synaptic adhesion in neurons-was ranked as the top gene closely linked to key drivers of myogenesis subnetworks. Lower expression of NLGN4Y was associated with higher grade PCa and an increased risk of BCR. We show that restoration of the protein expression of NLGN4Y in PC-3 cells leads to decreased cell proliferation, migration and inflammatory cytokine expression. Our results suggest that NLGN4Y is an important negative regulator in prostate cancer progression. More importantly, it highlights the value of IMBNs in generating biologically and clinically relevant hypotheses about prostate cancer that can be validated by independent studies.

  16. Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts

    Science.gov (United States)

    Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; Tremblay, Marie-Andrée; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

    2016-09-01

    Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. In vivo Raman spectroscopy can detect these invasive cancer cells in patients with grade 2 to 4 gliomas. The robustness of this Raman signal can be dampened by spectral artifacts generated by lights in the operating room. We found that artificial neural networks (ANNs) can overcome these spectral artifacts using nonparametric and adaptive models to detect complex nonlinear spectral characteristics. Coupling ANN with Raman spectroscopy simplifies the intraoperative use of Raman spectroscopy by limiting changes required to the standard neurosurgical workflow. The ability to detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery and improve patient survival.

  17. Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence.

    Science.gov (United States)

    Csermely, Peter; Hódsági, János; Korcsmáros, Tamás; Módos, Dezső; Perez-Lopez, Áron R; Szalay, Kristóf; Veres, Dániel V; Lenti, Katalin; Wu, Ling-Yun; Zhang, Xiang-Sun

    2015-02-01

    Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

  18. Numerical study on the perception-based network formation model

    CERN Document Server

    Jo, Hang-Hyun

    2015-01-01

    In order to understand the evolution of social networks in terms of perception-based strategic link formation, we numerically study a perception-based network formation model. Here each individual is assumed to have his/her own perception of the actual network, and use it to decide whether to create a link to other individual. An individual with the least perception accuracy can benefit from updating his/her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. The initial actual network and initial perceptions are modeled by Erd\\H{o}s-R\\'enyi random networks but with different linking probabilities. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. The effect of initial conditions on the complexity o...

  19. Targeting the insulin-like growth factor network in cancer therapy.

    Science.gov (United States)

    Heidegger, Isabel; Pircher, Andreas; Klocker, Helmut; Massoner, Petra

    2011-04-15

    During the last decades, changes in the insulin-like growth factor (IGF) signaling have been related to the pathogenesis of cancer. Therefore, IGFs became highly attractive therapeutic cancer targets. Several drugs including monoclonal antibodies (mAB), small molecule tyrosine kinase inhibitors (RTKIs), anti-sense oligonucleotids (ASOs) and IGF-binding proteins (IGFBPs) targeting the IGF axis were developed. With over 60 ongoing clinical trials, the IGF1 receptor (IGF1R) is currently one of the most studied molecular targets in the field of oncology. In this review, we provide an overview on the IGF axis, its signaling pathways and its significance in neoplasia. We critically review the preclinical and clinical studies investigating the role of IGF1R as a cancer target and discuss preliminary results and possible limitations.

  20. Epidemiologic studies of particulate matter and lung cancer

    Institute of Scientific and Technical Information of China (English)

    Yin-Ge Li; Xiang Gao

    2014-01-01

    Particulate matter (PM) plays an important role in air pollution, especially in China. European and American researchers conducted several cohort-based studies to examine the potential relationship between PM and lung cancer and found a positive association between PM and lung cancer mortality. In contrast, the results regarding PM and lung cancer risk remain inconsistent. Most of the previous studies had limitations such as misclassification of PM exposure and residual confounders, diminishing the impact of their findings. In addition, prospective studies on this topic are very limited in Chinese populations. This is an important problem because China has one of the highest concentrations of PM in the world and has had an increased mortality risk due to lung cancer. In this context, more prospective studies in Chinese populations are warranted to investigate the relationship between PM and lung cancer.

  1. Risk for unemployment of cancer survivors: A Danish cohort study

    DEFF Research Database (Denmark)

    Carlsen, Kathrine; Dalton, Susanne Oksbjerg; Diderichsen, Finn;

    2008-01-01

    AIM: To investigate whether cancer survivors are at an increased risk for unemployment after cancer. MATERIALS AND METHODS: A cohort of 65,510 patients who were part of the workforce in the year before diagnosis and a random sample of 316,925 age and gender-matched controls were followed for up...... to 20 years in a longitudinal register-based cohort study. Demographic, socioeconomic and health-related information were obtained through Danish administrative registers. RESULTS: Cancer survivors had a small but significantly increased risk for unemployment following cancer. Stratified analyses showed...

  2. Linkage of E2F1 transcriptional network and cell proliferation with respiratory chain activity in breast cancer cells.

    Science.gov (United States)

    Mori, Kazunori; Uchida, Tetsu; Fukumura, Motonori; Tamiya, Shigetoshi; Higurashi, Masato; Sakai, Hirosato; Ishikawa, Fumihiro; Shibanuma, Motoko

    2016-07-01

    Mitochondria are multifunctional organelles; they have been implicated in various aspects of tumorigenesis. In this study, we investigated a novel role of the basal electron transport chain (ETC) activity in cell proliferation by inhibiting mitochondrial replication and transcription (mtR/T) using pharmacological and genetic interventions, which depleted mitochondrial DNA/RNA, thereby inducing ETC deficiency. Interestingly, mtR/T inhibition did not decrease ATP levels despite deficiency in ETC activity in different cell types, including MDA-MB-231 breast cancer cells, but it severely impeded cell cycle progression, specifically progression during G2 and/or M phases in the cancer cells. Under these conditions, the expression of a group of cell cycle regulators was downregulated without affecting the growth signaling pathway. Further analysis suggested that the transcriptional network organized by E2F1 was significantly affected because of the downregulation of E2F1 in response to ETC deficiency, which eventually resulted in the suppression of cell proliferation. Thus, in this study, the E2F1-mediated ETC-dependent mechanism has emerged as the regulatory mechanism of cell cycle progression. In addition to E2F1, FOXM1 and BMYB were also downregulated, which contributed specifically to the defects in G2 and/or M phase progression. Thus, ETC-deficient cancer cells lost their growing ability, including their tumorigenic potential in vivo. ETC deficiency abolished the production of reactive oxygen species (ROS) from the mitochondria and a mitochondria-targeted antioxidant mimicked the deficiency, thereby suggesting that ETC activity signaled through ROS production. In conclusion, this novel coupling between ETC activity and cell cycle progression may be an important mechanism for coordinating cell proliferation and metabolism.

  3. Study of apoptosis in human liver cancers

    Institute of Scientific and Technical Information of China (English)

    Chang-Min Shan; Juan Li

    2002-01-01

    AIM: To investigate the action of apoptosis in occurrence ofliver cacinornas in vivo and the biological effect of Solanumlyratum Thumb on BEL-7404 cell line inducing apoptosis invitro.METHODS: The apoptosis in the liver carcinoma wasdetected with terminal deoxynucl neotidyl transferasemediated dUTP nick end labelling (TUNEL); the cancer cellscultured in DMED medium were treated with extract ofSolanum lyratum Thumb and observed under microscope,and their DNA was assayed by gel electrophoresis.RESULTS: In vivo apoptotic cells in the cancer adjacenttissues inceased; in vitro treatment of liver cancers withextract of Solanum lyratum Thumb could induce the cells tomanifest a typical apoptotic morphology. Their DNA wasfractured and a characteristic ladder pattem could be foundusing electrophoresis.CONCLUSION: In vivo the apoptosis of carcinomas waslower; maybe the cells divided quickly and then the cancersoccurred. In the cancer adjacent tissues, the apoptosispricked up, and in vitro Solarium lyratum Thumb couldinduce the apoptosis of BEL-7404 cells.

  4. Childhood Cancer Survivor Study: An Overview

    Science.gov (United States)

    ... Resources Conducting Clinical Trials Statistical Tools and Data Terminology Resources NCI Data Catalog Cryo-EM NCI's Role ... Contacts Other Funding Find NCI funding for small business innovation, technology transfer, and contracts Training Cancer Training ...

  5. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  6. AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Maqlin Paramanandam

    2016-11-01

    Full Text Available The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN. The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges

  7. Bayesian network approach to spatial data mining: a case study

    Science.gov (United States)

    Huang, Jiejun; Wan, Youchuan

    2006-10-01

    Spatial data mining is a process of discovering interesting, novel, and potentially useful information or knowledge hidden in spatial data sets. It involves different techniques and different methods from various areas of research. A Bayesian network is a graphical model that encodes causal probabilistic relationships among variables of interest, which has a powerful ability for representing and reasoning and provides an effective way to spatial data mining. In this paper we give an introduction to Bayesian networks, and discuss using Bayesian networks for spatial data mining. We propose a framework of spatial data mining based on Bayesian networks. Then we show a case study and use the experimental results to validate the practical viability of the proposed approach to spatial data mining. Finally, the paper gives a summary and some remarks.

  8. Study of the starting pressure gradient in branching network

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In order to increase the production of oil in low permeability reservoirs with high efficiency,it is necessary to fully understand the properties and special behaviors of the reservoirs and correctly describe the flow in the reservoirs.This paper applies the branching network mode to the study of the starting pressure gradient of nonlinear Newtonian fluid (Bingham fluid) in the reservoirs with low permeability based on the fact that the fractured network may exist in the reservoirs.The proposed model for starting pressure gradient is a function of yield stress,microstructural parameters of the network.The proposed model may have the potential in further exploiting the mechanisms of flow in porous media with fractured network.

  9. Water supply network district metering theory and case study

    CERN Document Server

    Di Nardo, Armando; Di Mauro, Anna

    2013-01-01

    The management of a water supply network can be substantially improved defining permanent sectors or districts that enhances simpler water loss detection and pressure management. However, the water network partitioning may compromise water system performance, since some pipes are usually closed to delimit districts in order not to have too many metering stations, to decrease costs and simplify water balance. This may reduce the reliability of the whole system and not guarantee the delivery of water at the different network nodes. In practical applications, the design of districts or sectors is generally based on empirical approaches or on limited field experiences. The book proposes a design support methodology, based on graph theory principles and tested on real case study. The described methodology can help water utilities, professionals and researchers to define the optimal districts or sectors of a water supply network.

  10. A STUDY OF SPAM DETECTION ALGORITHM ON SOCIAL MEDIA NETWORKS

    Directory of Open Access Journals (Sweden)

    Saini Jacob Soman

    2014-01-01

    Full Text Available In today’s world, the issue of identifying spammers has received increasing attention because of its practical relevance in the field of social network analysis. The growing popularity of social networking sites has made them prime targets for spammers. By allowing users to publicize and share their independently generated content, online social networks become susceptible to different types of malicious and opportunistic user actions. Social network community users are fed with irrelevant information while surfing, due to spammer’s activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. Therefore, this study attempts to review various spam detection frameworks which deals about the detection and elimination of spams in various sources.

  11. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  12. A NEURAL NETWORK STUDY ON GLASS TRANSITION TEMPERATURE OF POLYMERS

    Institute of Scientific and Technical Information of China (English)

    Lin-xi Zhanga; De-lu Zhao; You-xing Huang

    2002-01-01

    In this paper, an artificial neural network model is adopted to study the glass transition temperature of polymers. In our artificial neural networks, the input nodes are the characteristic ratio C∞, the average molecular weight M, between entanglement points and the molecular weight Mmon of repeating unit. The output node is the glass transition temperature Tg,and the number of the hidden layer is 6. We found that the artificial neural network simulations are accurate in predicting the outcome for polymers for which it is not trained. The maximum relative error for predicting of the glass transition temperature is 3.47%, and the overall average error is only 2.27%. Artificial neural networks may provide some new ideas to investigate other properties of the polymers.

  13. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

    Directory of Open Access Journals (Sweden)

    Jordán Ferenc

    2010-07-01

    Full Text Available Abstract Background Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. Results Based on the i2d and OMIM databases, we have constructed (i a network of proteins causing five selected diseases (DP, disease proteins plus their interacting partners (IP, non-disease proteins, the DPIP network and (ii a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1 various cancers, (2 heart diseases, (3 obesity, (4 diabetes and (5 autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. Conclusions We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand

  14. Patterns Prediction of Chemotherapy Sensitivity in Cancer Cell lines Using FTIR Spectrum, Neural Network and Principal Components Analysis.

    Science.gov (United States)

    Zendehdel, Rezvan; Masoudi-Nejad, Ali; H Shirazi, Farshad

    2012-01-01

    Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a sensitive human ovarian cell line, A2780, and its cisplatin-resistant derivative, A2780-cp. In this study FTIR method have been evaluated via the use of principal components analysis (PCA), ANN (artificial neuronal network) and LDA (linear discriminate analysis). FTIR spectroscopy on these cells in the range of 400-4000 cm(-1) showed alteration in the secondary structure of proteins and a CH stretching vibration. We have found that the ANN models correctly classified more than 95% of the cell lines, while the LDA models with the same data sets could classify 85% of cases. In the process of different ranges of spectra, the best classification of data set in the range of 1000-2000 cm(-1) was done using ANN model, while the data set between 2500-3000 cm(-1) was more correctly classified with the LDA model. PCA of the spectral data also provide a good separation for representing the variety of cell line spectra. Our work supports the promise of ANN analysis of FTIR spectrum as a supervised powerful approach and PCA as unsupervised modeling for the development of automated methods to determine the resistant phenotype of cancer classification.

  15. GalNAc-T4 putatively modulates the estrogen regulatory network through FOXA1 glycosylation in human breast cancer cells.

    Science.gov (United States)

    Niang, Bachir; Jin, Liyuan; Chen, Xixi; Guo, Xiaohan; Zhang, Hongshuo; Wu, Qiong; Padhiar, Arshad Ahmed; Xiao, Min; Fang, Deyu; Zhang, Jianing

    2016-01-01

    GALNT4 belongs to a family of N-acetylgalactosaminyltransferases, which catalyze the transfer of GalNAc to Serine or Threonine residues in the initial step of mucin-type O-linked protein glycosylation. This glycosylation type is the most complex post-translational modification of proteins, playing important roles during cellular differentiation and in pathological disorders. Most of the breast cancer subtypes are estrogen receptor positive, and hence, the estrogen pathway represents a key regulatory network. We investigated the expression of GalNAc-T4 in a panel of mammary epithelial cell lines and found its expression is associated with the estrogen status of the cells. FOXA1, a key transcription factor, functions to promote estrogen responsive gene expression by acting as a cofactor to estrogen receptor alpha (ERα), but all the aspects of this regulatory mechanism are not fully explored. This study found that knockdown of GALNT4 expression in human breast cancer cells attenuated the protein expression of ERα, FOXA1, and Cyclin D1. Further, our immunoprecipitation assays depicted the possibility of FOXA1 to undergo O-GalNAc modifications with a decrease of GalNAc residues in the GALNT4 knockdown cells and also impairment in the FOXA1-ERα association. Rescuing GALNT4 expression could restore the interaction as well as the glycosylation of FOXA1. Together, these findings suggest a key role for GalNAc-T4 in the estrogen pathway through FOXA1 glycosylation.

  16. Think Tank: Identifying and Creating the Next Generation of Community-Based Cancer Prevention Studies | Division of Cancer Prevention

    Science.gov (United States)

    In late 2015, the NCI Division of Cancer Prevention convened cancer prevention research experts and stakeholders to discuss the current state of cancer prevention research, identify key prevention research priorities for the NCI, and identify studies that could be conducted within the NCI Community Oncology Research Program. Read the Cancer Prevention Research journal article (PDF, 532KB). |

  17. Obesity and cancer: mechanistic insights from transdisciplinary studies.

    Science.gov (United States)

    Allott, Emma H; Hursting, Stephen D

    2015-12-01

    Obesity is associated with a range of health outcomes that are of clinical and public health significance, including cancer. Herein, we summarize epidemiologic and preclinical evidence for an association between obesity and increased risk of breast and prostate cancer incidence and mortality. Moreover, we describe data from observational studies of weight change in humans and from calorie-restriction studies in mouse models that support a potential role for weight loss in counteracting tumor-promoting properties of obesity in breast and prostate cancers. Given that weight loss is challenging to achieve and maintain, we also consider evidence linking treatments for obesity-associated co-morbidities, including metformin, statins and non-steroidal anti-inflammatory drugs, with reduced breast and prostate cancer incidence and mortality. Finally, we highlight several challenges that should be considered when conducting epidemiologic and preclinical research in the area of obesity and cancer, including the measurement of obesity in population-based studies, the timing of obesity and weight change in relation to tumor latency and cancer diagnosis, and the heterogeneous nature of obesity and its associated co-morbidities. Given that obesity is a complex trait, comprised of behavioral, epidemiologic and molecular/metabolic factors, we argue that a transdisciplinary approach is the key to understanding the mechanisms linking obesity and cancer. As such, this review highlights the critical need to integrate evidence from both epidemiologic and preclinical studies to gain insight into both biologic and non-biologic mechanisms contributing to the obesity-cancer link.

  18. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

    Directory of Open Access Journals (Sweden)

    Zhao Yuming

    2011-05-01

    Full Text Available Abstract Background Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. Results We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct ( Conclusions We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.

  19. One Health and cancer: A comparative study of human and canine cancers in Nairobi

    Directory of Open Access Journals (Sweden)

    Nyariaro Kelvin Momanyi

    2016-11-01

    Full Text Available Aim: Recent trends in comparative animal and human research inform us that collaborative research plays a key role in deciphering and solving cancer challenges. Globally, cancer is a devastating diagnosis with an increasing burden in both humans and dogs and ranks as the number three killer among humans in Kenya. This study aimed to provide comparative information on cancers affecting humans and dogs in Nairobi, Kenya. Materials and Methods: Dog data collection was by cancer case finding from five veterinary clinics and two diagnostic laboratories, whereas the human dataset was from the Nairobi Cancer Registry covering the period 2002-2012. The analysis was achieved using IBM SPSS Statistics® v.20 (Dog data and CanReg5 (human data. The human population was estimated from the Kenya National Census, whereas the dog population was estimated from the human using a human:dog ratio of 4.1:1. Results: A total of 15,558 human and 367 dog cancer cases were identified. In humans, females had higher cancer cases 8993 (an age-standardized rate of 179.3 per 100,000 compared to 6565 in males (122.1 per 100,000. This order was reversed in dogs where males had higher cases 198 (14.9 per 100,000 compared to 169 (17.5 per 100,000 in females. The incident cancer cases increased over the 11-year study period in both species. Common cancers affecting both humans and dogs were: Prostate (30.4, 0.8, the respiratory tract (8.3, 1.3, lymphoma (5.6, 1.4, and liver and biliary tract (6.3, 0.5, whereas, in females, they were: Breast (44.5, 3.6, lip, oral cavity, and pharynx (8.8, 0.6, liver and biliary tract (6.5, 1.2, and lymphoma (6.0, 0.6, respectively, per 100,000. Conclusion: The commonality of some of the cancers in both humans and dogs fortifies that it may be possible to use dogs as models and sentinels in studying human cancers in Kenya and Africa. We further infer that developing joint animalhuman cancer registries and integrated cancer surveillance systems may

  20. Propranolol Reduces Cancer Risk: A Population-Based Cohort Study.

    Science.gov (United States)

    Chang, Ping-Ying; Huang, Wen-Yen; Lin, Cheng-Li; Huang, Tzu-Chuan; Wu, Yi-Ying; Chen, Jia-Hong; Kao, Chia-Hung

    2015-07-01

    β-Blockers have been reported to exhibit potential anticancer effects in cancer cell lines and animal models. However, clinical studies have yielded inconsistent results regarding cancer outcomes and cancer risk when β-blockers were used. This study investigated the association between propranolol and cancer risk.Between January 1, 2000 and December 31, 2011, a patient cohort was extracted from the Longitudinal Health Insurance Database 2000, a subset of the Taiwan National Health Insurance Research Database. A propranolol cohort (propranolol usage >6 months) and nonpropranolol cohort were matched using a propensity score. Cox proportional hazard models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) of cancer associated with propranolol treatment.The study sample comprised 24,238 patients. After a 12-year follow-up period, the cumulative incidence for developing cancer was low in the propranolol cohort (HR: 0.75; 95% CI: 0.67-0.85; P propranolol treatment exhibited significantly lower risks of cancers in head and neck (HR: 0.58; 95% CI: 0.35-0.95), esophagus (HR: 0.35; 95% CI: 0.13-0.96), stomach (HR: 0.54; 95% CI: 0.30-0.98), colon (HR: 0.68; 95% CI: 0.49-0.93), and prostate cancers (HR: 0.52; 95% CI: 0.33-0.83). The protective effect of propranolol for head and neck, stomach, colon, and prostate cancers was most substantial when exposure duration exceeded 1000 days.This study supports the proposition that propranolol can reduce the risk of head and neck, esophagus, stomach, colon, and prostate cancers. Further prospective study is necessary to confirm these findings.

  1. Social network analysis in identifying influential webloggers: A preliminary study

    Science.gov (United States)

    Hasmuni, Noraini; Sulaiman, Nor Intan Saniah; Zaibidi, Nerda Zura

    2014-12-01

    In recent years, second generation of internet-based services such as weblog has become an effective communication tool to publish information on the Web. Weblogs have unique characteristics that deserve users' attention. Some of webloggers have seen weblogs as appropriate medium to initiate and expand business. These webloggers or also known as direct profit-oriented webloggers (DPOWs) communicate and share knowledge with each other through social interaction. However, survivability is the main issue among DPOW. Frequent communication with influential webloggers is one of the way to keep survive as DPOW. This paper aims to understand the network structure and identify influential webloggers within the network. Proper understanding of the network structure can assist us in knowing how the information is exchanged among members and enhance survivability among DPOW. 30 DPOW were involved in this study. Degree centrality and betweenness centrality measurement in Social Network Analysis (SNA) were used to examine the strength relation and identify influential webloggers within the network. Thus, webloggers with the highest value of these measurements are considered as the most influential webloggers in the network.

  2. Atopy and Specific Cancer Sites: a Review of Epidemiological Studies.

    Science.gov (United States)

    Cui, Yubao; Hill, Andrew W

    2016-12-01

    Mounting evidence appears to link asthma and atopy to cancer susceptibility. This review presents and discusses published epidemiological studies on the association between site-specific cancers and atopy. PubMed was searched electronically for publications between 1995 and 2015, and cited references were researched manually. Quantitative studies relating to atopy, allergy, or asthma and cancer were identified and tabulated. Despite many exposure-related limitations, patterns in the studies were observed. Asthma, specifically, has been observed to be a risk factor for lung cancer. A protective effect of atopic diseases against pancreatic cancer has been shown consistently in case-control studies but not in cohort studies. Allergy of any type appears to be protective against glioma and adult acute lymphoblastic leukemia. Most studies on atopic diseases and non-Hodgkin lymphoma or colorectal cancer reported an inverse association. The other sites identified had varying and non-significant outcomes. Further research should be dedicated to carefully defined exposure assessments of "atopy" as well as the biological plausibility in the association between atopic diseases and cancer.

  3. Tryptophan degradation in women with breast cancer: a pilot study

    Directory of Open Access Journals (Sweden)

    Schubert Christine M

    2011-05-01

    Full Text Available Abstract Background Altered tryptophan metabolism and indoleamine 2,3-dioxygenase activity are linked to cancer development and progression. In addition, these biological factors have been associated with the development and severity of neuropsychiatric syndromes, including major depressive disorder. However, this biological mechanism associated with both poor disease outcomes and adverse neuropsychiatric symptoms has received little attention in women with breast cancer. Therefore, a pilot study was undertaken to compare levels of tryptophan and other proteins involved in tryptophan degradation in women with breast cancer to women without cancer, and secondarily, to examine levels in women with breast caner over the course of chemotherapy. Findings Blood samples were collected from women with a recent diagnosis of breast cancer (n = 33 before their first cycle of chemotherapy and after their last cycle of chemotherapy. The comparison group (n = 24 provided a blood sample prior to breast biopsy. Plasma concentrations of tryptophan, kynurenine, and tyrosine were determined. The kynurenine to tryptophan ratio (KYN/TRP was used to estimate indoleamine 2,3-dioxygenase activity. On average, the women with breast cancer had lower levels of tryptophan, elevated levels of kynurenine and tyrosine and an increased KYN/TRP ratio compared to women without breast cancer. There was a statistically significant difference between the two groups in the KYN/TRP ratio (p = 0.036, which remained elevated in women with breast cancer throughout the treatment trajectory. Conclusions The findings of this pilot study suggest that increased tryptophan degradation may occur in women with early-stage breast cancer. Given the multifactorial consequences of increased tryptophan degradation in cancer outcomes and neuropsychiatric symptom manifestation, this biological mechanism deserves broader attention in women with breast cancer.

  4. Applying Bayesian networks in practical customer satisfaction studies

    NARCIS (Netherlands)

    Jaronski, W.; Bloemer, J.M.M.; Vanhoof, K.; Wets, G.

    2004-01-01

    This chapter presents an application of Bayesian network technology in an empirical customer satisfaction study. The findings of the study should provide insight to the importance of product/service dimensions in terms of the strength of their influence on overall (dis)satisfaction. To this end we a

  5. Co-evolution of conventions and networks : an experimental study

    NARCIS (Netherlands)

    Corten, R.; Buskens, V.W.

    2010-01-01

    We study the emergence of conventions in dynamic networks experimentally. Conventions are modeled in terms of coordination games in which actors can choose both their behavior and their interaction partners. We study how macro-level outcomes of the process in terms of Pareto-efficiency and heterogen

  6. A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.

    Science.gov (United States)

    Lancashire, L J; Powe, D G; Reis-Filho, J S; Rakha, E; Lemetre, C; Weigelt, B; Abdel-Fatah, T M; Green, A R; Mukta, R; Blamey, R; Paish, E C; Rees, R C; Ellis, I O; Ball, G R

    2010-02-01

    Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.

  7. Immunodynamics: a cancer immunotherapy trials network review of immune monitoring in immuno-oncology clinical trials.

    Science.gov (United States)

    Kohrt, Holbrook E; Tumeh, Paul C; Benson, Don; Bhardwaj, Nina; Brody, Joshua; Formenti, Silvia; Fox, Bernard A; Galon, Jerome; June, Carl H; Kalos, Michael; Kirsch, Ilan; Kleen, Thomas; Kroemer, Guido; Lanier, Lewis; Levy, Ron; Lyerly, H Kim; Maecker, Holden; Marabelle, Aurelien; Melenhorst, Jos; Miller, Jeffrey; Melero, Ignacio; Odunsi, Kunle; Palucka, Karolina; Peoples, George; Ribas, Antoni; Robins, Harlan; Robinson, William; Serafini, Tito; Sondel, Paul; Vivier, Eric; Weber, Jeff; Wolchok, Jedd; Zitvogel, Laurence; Disis, Mary L; Cheever, Martin A

    2016-01-01

    The efficacy of PD-1/PD-L1 targeted therapies in addition to anti-CTLA-4 solidifies immunotherapy as a modality to add to the anticancer arsenal. Despite raising the bar of clinical efficacy, immunologically targeted agents raise new challenges to conventional drug development paradigms by highlighting the limited relevance of assessing standard pharmacokinetics (PK) and pharmacodynamics (PD). Specifically, systemic and intratumoral immune effects have not consistently correlated with standard relationships between systemic dose, toxicity, and efficacy for cytotoxic therapies. Hence, PK and PD paradigms remain inadequate to guide the selection of doses and schedules, both starting and recommended Phase 2 for immunotherapies. The promise of harnessing the immune response against cancer must also be considered in light of unique and potentially serious toxicities. Refining immune endpoints to better inform clinical trial design represents a high priority challenge. The Cancer Immunotherapy Trials Network investigators review the immunodynamic effects of specific classes of immunotherapeutic agents to focus immune assessment modalities and sites, both systemic and importantly intratumoral, which are critical to the success of the rapidly growing field of immuno-oncology.

  8. A computational study of routing algorithms for realistic transportation networks

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, R.; Marathe, M.V.; Nagel, K.

    1998-12-01

    The authors carry out an experimental analysis of a number of shortest path (routing) algorithms investigated in the context of the TRANSIMS (Transportation Analysis and Simulation System) project. The main focus of the paper is to study how various heuristic and exact solutions, associated data structures affected the computational performance of the software developed especially for realistic transportation networks. For this purpose the authors have used Dallas Fort-Worth road network with very high degree of resolution. The following general results are obtained: (1) they discuss and experimentally analyze various one-one shortest path algorithms, which include classical exact algorithms studied in the literature as well as heuristic solutions that are designed to take into account the geometric structure of the input instances; (2) they describe a number of extensions to the basic shortest path algorithm. These extensions were primarily motivated by practical problems arising in TRANSIMS and ITS (Intelligent Transportation Systems) related technologies. Extensions discussed include--(i) time dependent networks, (ii) multi-modal networks, (iii) networks with public transportation and associated schedules. Computational results are provided to empirically compare the efficiency of various algorithms. The studies indicate that a modified Dijkstra`s algorithm is computationally fast and an excellent candidate for use in various transportation planning applications as well as ITS related technologies.

  9. Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

    Science.gov (United States)

    Tian, Xinyu; Wang, Xuefeng; Chen, Jun

    2014-01-01

    Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

  10. A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Xin Lai

    2013-01-01

    Full Text Available MicroRNAs (miRNAs are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

  11. Eleventh Hour Network+ Exam N10-004 Study Guide

    CERN Document Server

    Alpern, Naomi

    2009-01-01

    The 11th Hour Network+ Study Guide is keyed to the N10-004 revision of the CompTIA Network+ exam. This book is streamlined to include only core certification information and is presented for ease of last-minute studying. Main objectives of the exam are covered with key concepts highlighted. ..: ..; Fast Facts quickly review fundamentals ..; Exam Warnings highlight particularly tough sections of the exam ..; Crunch Time sidebars point out key concepts to remember ..; Did You Know? sidebars cover sometimes forgotten details ..; Top Five Toughest Questions and answers help you to prepare ..

  12. Patterns of care for radiotherapy in vulvar cancer: a Gynecologic Cancer Intergroup study

    DEFF Research Database (Denmark)

    Gaffney, David K; Du Bois, Andreas; Narayan, Kailash;

    2009-01-01

    BACKGROUND: This study aimed to describe radiotherapeutic practice in the treatment of vulvar cancer in member study groups of the Gynecologic Cancer Intergroup (GCIG). METHODS: A survey was developed and distributed to representatives of the member study groups of the GCIG, targeting the use...... of radiotherapy (RT) in vulvar cancer. RESULTS: Thirty-two surveys were returned from 12 different cooperative groups. The most common indications for neoadjuvant RT include unresectable disease or International Federation of Gynecology and Obstetrics stage >/=III. For the neoadjuvant treatment of vulvar cancer...... of a broadly accepted standard. This underscores the importance of international cooperation as in GCIG to gather more reliable data for uncommon tumors in gynecologic oncology....

  13. MicroRNA-regulated gene networks during mammary cell differentiation are associated with breast cancer.

    Science.gov (United States)

    Aydoğdu, Eylem; Katchy, Anne; Tsouko, Efrosini; Lin, Chin-Yo; Haldosén, Lars-Arne; Helguero, Luisa; Williams, Cecilia

    2012-08-01

    MicroRNAs (miRNAs) play pivotal roles in stem cell biology, differentiation and oncogenesis and are of high interest as potential breast cancer therapeutics. However, their expression and function during normal mammary differentiation and in breast cancer remain to be elucidated. In order to identify which miRNAs are involved in mammary differentiation, we thoroughly investigated miRNA expression during functional differentiation of undifferentiated, stem cell-like, murine mammary cells using two different large-scale approaches followed by qPCR. Significant changes in expression of 21 miRNAs were observed in repeated rounds of mammary cell differentiation. The majority, including the miR-200 family and known tumor suppressor miRNAs, was upregulated during differentiation. Only four miRNAs, including oncomiR miR-17, were downregulated. Pathway analysis indicated complex interactions between regulated miRNA clusters and major pathways involved in differentiation, proliferation and stem cell maintenance. Comparisons with human breast cancer tumors showed the gene profile from the undifferentiated, stem-like stage clustered with that of poor-prognosis breast cancer. A common nominator in these groups was the E2F pathway, which was overrepresented among genes targeted by the differentiation-induced miRNAs. A subset of miRNAs could further discriminate between human non-cancer and breast cancer cell lines, and miR-200a/miR-200b, miR-146b and miR-148a were specifically downregulated in triple-negative breast cancer cells. We show that miR-200a/miR-200b can inhibit epithelial-mesenchymal transition (EMT)-characteristic morphological changes in undifferentiated, non-tumorigenic mammary cells. Our studies propose EphA2 as a novel and important target gene for miR-200a. In conclusion, we present evidentiary data on how miRNAs are involved in mammary cell differentiation and indicate their related roles in breast cancer.

  14. Ensuring quality in studies linking cancer registries and biobanks.

    Science.gov (United States)

    Langseth, Hilde; Luostarinen, Tapio; Bray, Freddie; Dillner, Joakim

    2010-04-01

    The Nordic countries have a long tradition of providing comparable and high quality cancer data through the national population-based cancer registries and the capability to link the diverse large-scale biobanks currently in operation. The joining of these two infrastructural resources can provide a study base for large-scale studies of etiology, treatment and early detection of cancer. Research projects based on combined data from cancer registries and biobanks provides great opportunities, but also presents major challenges. Biorepositories have become an important resource in molecular epidemiology, and the increased interest in performing etiological, clinical and gene-environment-interaction studies, involving information from biological samples linked to population-based cancer registries, warrants a joint evaluation of the quality aspects of the two resources, as well as an assessment of whether the resources can be successfully combined into a high quality study. While the quality of biospecimen handling and analysis is commonly considered in different studies, the logistics of data handling including the linkage of the biobank with the cancer registry is an overlooked aspect of a biobank-based study. It is thus the aim of this paper to describe recommendations on data handling, in particular the linkage of biobank material to cancer registry data and the quality aspects thereof, based on the experience of Nordic collaborative projects combining data from cancer registries and biobanks. We propose a standard documentation with respect to the following topics: the quality control aspects of cancer registration, the identification of cases and controls, the identification and use of data confounders, the stability of serum components, historical storage conditions, aliquoting history, the number of freeze/thaw cycles and available volumes.

  15. ACCISS study rationale and design: activating collaborative cancer information service support for cervical cancer screening

    Directory of Open Access Journals (Sweden)

    Bullard Emily

    2009-12-01

    Full Text Available Abstract Background High-quality cancer information resources are available but underutilized by the public. Despite greater awareness of the National Cancer Institute's Cancer Information Service among low-income African Americans and Hispanics compared with Caucasians, actual Cancer Information Service usage is lower than expected, paralleling excess cancer-related morbidity and mortality for these subgroups. The proposed research examines how to connect the Cancer Information Service to low-income African-American and Hispanic women and their health care providers. The study will examine whether targeted physician mailing to women scheduled for colposcopy to follow up an abnormal Pap test can increase calls to the Cancer Information Service, enhance appropriate medical follow-up, and improve satisfaction with provider-patient communication. Methods/Design The study will be conducted in two clinics in ethnically diverse low-income communities in Chicago. During the formative phase, patients and providers will provide input regarding materials planned for use in the experimental phase of the study. The experimental phase will use a two-group prospective randomized controlled trial design. African American and Hispanic women with an abnormal Pap test will be randomized to Usual Care (routine colposcopy reminder letter or Intervention (reminder plus provider recommendation to call the Cancer Information Service and sample questions to ask. Primary outcomes will be: 1 calls to the Cancer Information Service; 2 timely medical follow-up, operationalized by whether the patient keeps her colposcopy appointment within six months of the abnormal Pap; and 3 patient satisfaction with provider-patient communication at follow-up. Discussion The study examines the effectiveness of a feasible, sustainable, and culturally sensitive strategy to increase awareness and use of the Cancer Information Service among an underserved population. The goal of linking a

  16. Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

    Directory of Open Access Journals (Sweden)

    Guang Hu

    2017-01-01

    Full Text Available The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM and Protein Contact Network (PCN are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

  17. Cancer prevention by green tea: evidence from epidemiologic studies.

    Science.gov (United States)

    Yuan, Jian-Min

    2013-12-01

    In contrast to the consistent results of an inhibitory effect of green tea extracts and tea polyphenols on the development and growth of carcinogen-induced tumors in experimental animal models, results from human studies are mixed. Both observational and intervention studies have provided evidence in support of a protective role of green tea intake in the development of oral-digestive tract cancer or an inhibitory role of oral supplementation of green tea extract on a precancerous lesion of oral cavity. Evidence in support of green tea intake against the development of liver cancer risk is limited and inconsistent. An inverse association between green tea intake and lung cancer risk has been observed among never smokers but not among smokers. Although observational studies do not support a beneficial role of tea intake against the development of prostate cancer, several phase 2 clinical trials have shown an inhibitory effect of green tea extract against the progression of prostate premalignant lesions to malignant tumors. Prospective epidemiologic studies so far have not provided evidence for a protective effect of green tea consumption on breast cancer development. Current data neither confirm nor refute a definitive cancer-preventive role of green tea intake. Large randomized intervention trials on the efficacy of green tea polyphenols or extracts are required before a recommendation for green tea consumption for cancer prevention should be made.

  18. Network analysis: a new approach to study endocrine disorders.

    Science.gov (United States)

    Stevens, A; De Leonibus, C; Hanson, D; Dowsey, A W; Whatmore, A; Meyer, S; Donn, R P; Chatelain, P; Banerjee, I; Cosgrove, K E; Clayton, P E; Dunne, M J

    2014-02-01

    Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

  19. 77 FR 50712 - Information Collection: Southern Alaska Sharing Network and Subsistence Study; Proposed...

    Science.gov (United States)

    2012-08-22

    ... local sharing networks that structure contemporary subsistence-cash economies using research methods... Bureau of Ocean Energy Management Information Collection: Southern Alaska Sharing Network and Subsistence... in Alaska, ``Southern Alaska Sharing Network and Subsistence Study.'' DATES: Submit written...

  20. Association between cancer and contact allergy: a linkage study

    DEFF Research Database (Denmark)

    Engkilde, Kaare; Thyssen, Jacob P; Menné, Torkil;

    2011-01-01

    and cancer, few have looked into the association between cancer and contact allergy, a type IV allergy. By linking two clinical databases, the authors investigate the possible association between contact allergy and cancer. Methods Record linkage of two different registers was performed: (1) a tertiary...... by logistic regression analysis. Results An inverse association between contact allergy and non-melanoma skin- and breast cancer, respectively, was identified in both sexes, and an inverse trend for brain cancer was found in women with contact allergy. Additionally, a positive association between contact......Background Contact allergy is a prevalent disorder. It is estimated that about 20% of the general population are allergic to one or more of the chemicals that constitute the European baseline patch test panel. While many studies have investigated associations between type I allergic disorders...

  1. Combinations of genetic data in a study of oral cancer

    DEFF Research Database (Denmark)

    Mellerup, Erling Thyge; Møller, Gert Lykke; Mondal, Pinaki

    2015-01-01

    for a polygenic disorder will not occur in in control persons genetically unrelated to patients, so the strategy is to analyze combinations of genetic variants present exclusively in patients. In a previous study of oral cancer and leukoplakia 325 SNPs were analyzed. This study has been supplemented...... with an analysis of combinations of two SNP genotypes from among the 325 SNPs. Two clusters of combinations containing 95 patient specific combinations were significantly associated with oral cancer or leukoplakia. Of 373 patients with oral cancer 205 patients had a number of these 95 combinations in their genome...

  2. Swarna Bhasma in cancer: A prospective clinical study.

    Science.gov (United States)

    Das, Soumen; Das, Mangal C; Paul, Retina

    2012-07-01

    Despite the advances in the treatment of cancer, mortality is still high. Complementary and alternative medicine is emerging as a potent modality in cancer treatment. 'Swarna Bhasma' (SB), containing gold particles, is an ancient Indian medicine has shown its anticancer activity. This present study was conducted to detect the effect of SB on solid malignancies. A total of 43 patients were included in this study received SB for 1 year. Seventeen patients showed response. The response was best in rectal cancer group 70% (7/10). Nearly 41.02% patients survived for 1 year after treatment but after 5 years this came down to 15.38%.

  3. Breast cancer screening: An outpatient clinic study

    Directory of Open Access Journals (Sweden)

    Mustafa Girgin

    2017-03-01

    Conclusion: A multidisciplinary cancer screening program should be maintained. With such a process, the aim is to reduce the morbidity and mortality of the disease without adversely affecting the health conditions of asymptomatic individuals based on the screening. Success is brought about by the combination of individual features. [Arch Clin Exp Surg 2017; 6(1.000: 23-27

  4. Gut Bacteria May Link Diet, Colon Cancer, Study Says

    Science.gov (United States)

    ... https://medlineplus.gov/news/fullstory_163274.html Gut Bacteria May Link Diet, Colon Cancer, Study Says High- ... link appears to be a type of intestinal bacteria, the Boston research team said. Specifically, they looked ...

  5. Tea and cancer prevention: studies in animals and humans.

    Science.gov (United States)

    Chung, Fung-Lung; Schwartz, Joel; Herzog, Christopher R; Yang, Yang-Ming

    2003-10-01

    The role of tea in protection against cancer has been supported by ample evidence from studies in cell culture and animal models. However, epidemiological studies have generated inconsistent results, some of which associated tea with reduced risk of cancer, whereas others found that tea lacks protective activity against certain human cancers. These results raise questions about the actual role of tea in human cancer that needs to be addressed. This article is intended to provide a better perspective on this controversy by summarizing the laboratory studies in animals and humans with emphasis on animal tumor bioassays on skin, lung, mammary glands and colon, and the molecular and cellular mechanisms affected by tea. Finally, a recent small pilot intervention study with green tea in smokers is presented.

  6. Study to Understand Cervical Cancer Early Endpoints and Determinants (SUCCEED)

    Science.gov (United States)

    A study to comprehensively assess biomarkers of risk for progressive cervical neoplasia, and thus develop a new set of biomarkers that can distinguish those at highest risk of cervical cancer from those with benign infection

  7. American Institute for Cancer Research

    Science.gov (United States)

    ... About Cancer By Cancer Site What Is Cancer Foods That Fight Cancer Tools You Can Use Cancer Infographics & Multimedia Studying ... About Cancer By Cancer Site What Is Cancer Foods That Fight Cancer Tools You Can Use Cancer Infographics & Multimedia Studying ...

  8. Alcohol, folate, methionine, and risk of incident breast cancer in the American Cancer Society Cancer Prevention Study II Nutrition Cohort.

    Science.gov (United States)

    Feigelson, Heather Spencer; Jonas, Carolyn R; Robertson, Andreas S; McCullough, Marjorie L; Thun, Michael J; Calle, Eugenia E

    2003-02-01

    Recent studies suggest that the increased risk of breast cancer associated with alcohol consumption may be reduced by adequate folate intake. We examined this question among 66,561 postmenopausal women in the American Cancer Society Cancer Prevention Study II Nutrition Cohort. A total of 1,303 incident cases had accrued during the first 5 years of follow-up. Cox proportional hazards models and stratified analysis were used to examine the relationship between alcohol, dietary and total folate intake, multivitamin use, dietary methionine, and breast cancer. We observed an increasing risk of breast cancer with increasing alcohol consumption (P for trend = 0.01). In the highest category of consumption (15 or more grams of ethanol/day), the risk of breast cancer was 1.26 (95% confidence interval, 1.04-1.53) compared with nonusers. We observed this association with higher alcohol consumption for in situ, localized, and regional disease. We found no association between risk of breast cancer and dietary folate, total folate, multivitamin use, or methionine intake. Furthermore, we found no evidence of an interaction between levels of dietary folate (P for interaction = 0.10) or total folate (P for interaction = 0.61) and alcohol. Nor did we find evidence of an interaction between alcohol consumption and recent or long-term multivitamin use (P for interaction = 0.27). Our results are consistent with a positive association with alcohol but do not support an association with folate or methionine intake or an interaction between folate and alcohol intake on risk of breast cancer.

  9. Baldness and testicular cancer: the EPSAM case-control study.

    Science.gov (United States)

    Moirano, G; Zugna, D; Grasso, C; Lista, P; Ciuffreda, L; Segnan, N; Merletti, F; Richiardi, L

    2016-03-01

    The etiology of testicular cancer is largely unexplained. Research has mainly focused on prenatal exposures, especially to sex hormones, while less attention has been paid to exposures that may act also postnatally. As baldness has been previously associated with testicular cancer risk we focused on baldness and body hairiness, which are both associated with androgen activity. We used data of the Postnatal Exposures and Male Health (EPSAM) study, a case-control study on testicular cancer conducted in the Province of Turin, Italy, involving cases diagnosed between 1997 and 2008. Information was collected using mailed questionnaires. Analyses included 255 cases and 459 controls. We calculated ORs and 95% CIs to estimate testicular cancer risk among those who developed baldness and among those with body hairiness. We found an inverse association between testicular cancer and baldness (OR: 0.67, 95% CI: 0.46-0.98) and body hairiness (OR: 0.78, 95% CI: 0.53-1.16), although the latter had wider CIs. The inverse association between baldness and testicular cancer is consistent with the results from previous studies. These results suggest that androgens activity may influence testicular cancer risk.

  10. High body mass index and cancer risk-a Mendelian randomisation study

    DEFF Research Database (Denmark)

    Benn, Marianne; Tybjærg-Hansen, Anne; Smith, George Davey;

    2016-01-01

    108,812 individuals from the general population, we found that observationally high BMI was associated with lower risk of lung and skin cancer overall and with higher risk of breast cancer in postmenopausal women, but not with other types of cancer. BMI increasing alleles were not associated with risk...... of follow-up (range 0-37), 8002 developed non-skin cancer, 3347 non-melanoma skin cancer, 1396 lung cancer, 637 other smoking related cancers, 1203 colon cancer, 159 kidney cancer, 1402 breast cancer, 1062 prostate cancer, and 2804 other cancers. Participants were genotyped for five genetic variants...... associated with BMI. Two Danish general population studies, the Copenhagen General Population and the Copenhagen City Heart Study. In observational analyses, overall risk of non-melanoma skin cancer was 35 % (95 % confidence interval 28-42 %) lower and risk of lung cancer 32 % (19-43 %) lower in individuals...

  11. Chapter 8. Tea and Cancer Prevention: Epidemiological Studies

    OpenAIRE

    Yuan, Jian-Min; Sun, Canlan; Butler, Lesley M

    2011-01-01

    Experimental studies have consistently shown the inhibitory activities of tea extracts on tumorigenesis in multiple model systems. Epidemiologic studies, however, have produced inconclusive results in humans. A comprehensive review was conducted to assess the current knowledge on tea consumption and risk of cancers in humans. In general, consumption of black tea was not associated with lower risk of cancer. High intake of green tea was consistently associated with reduced risk of upper gastro...

  12. The progress of study on pathogenesis in ovarian cancer

    Institute of Scientific and Technical Information of China (English)

    JI Yu-bin; LI Hai-jiao; YU Lei; LIU Guang-da; PANG Lin-lin; YANG Hai-fan

    2008-01-01

    Ovarian cancer is one of the three malignant tumors in female reproductive system, the death rate locates in the first place of gynecological cancer. Most patients are already at the advanced stage when examine their bodies, five-year survival rate are only about 20 % to 30 %. So gynecological cancer has bedome one of tumor which the most waiting to be considered. It happens refer to the incidence of chromosomal abnormalities, cancer gene change. The inactivation of tumor suppressor gene, inhibitor of apoptosis and other genetic changes, the imbalance in the regulatory network due to the interaction of multiple genes and their product. Chromosomal abnormalities play an important role in the development of ovarian cancer, the chromosomes of common characteristic and non-random changes are 1,3, 5, 6, 7, 8, 11, 12, 15, 17, 18, 20, 22 etc. Cancer gene including K-ras, c-erb-B2/HER-2, D1 (CyclinD1), AIB1 etc. K-ras coded protein p21 is activated through point mutation, cause the enzyme activity deprivation of GMP, slowed down the speed of GTP degrdn into GMP, activate target molecule persistently, make cells proliferate persistently, then leading to cancer. HER-2 gene amplification result in the over expression of HER-2 protein, made cells over proliferate,Protein over expression convey the strong signal of proliferation, over activate the early transcription factor and certain gene in the nuclear, then promote the occurrence of cancer. Cyclin D1 promote cells enter from S to Gl phase, thus contribute to the proliferation of cell division, then canceration. AIB1 gene over express, will cause tumor cells immortalized. Tumor suppressor gene, such as BRCA1, p53, p73, p16 etc. The expression depl of BRCA1 protein in ovarian Cystadenocarcinoma prompt that the reduction of BRCA1 protein synthesis, resulting in apoptosis decreased, the cell proliferation disinhibit, then disorder and proliferate, thus leading to cancer, p53 mutation happened in about 30 percents to 80 percents

  13. Colorectal cancer prevention: Perspectives of key players from social networks in a low-income rural US region

    Directory of Open Access Journals (Sweden)

    Nancy E. Schoenberg

    2016-02-01

    Full Text Available Social networks influence health behavior and health status. Within social networks, “key players” often influence those around them, particularly in traditionally underserved areas like the Appalachian region in the USA. From a total sample of 787 Appalachian residents, we identified and interviewed 10 key players in complex networks, asking them what comprises a key player, their role in their network and community, and ideas to overcome and increase colorectal cancer (CRC screening. Key players emphasized their communication skills, resourcefulness, and special occupational and educational status in the community. Barriers to CRC screening included negative perceptions of the colonoscopy screening procedure, discomfort with the medical system, and misinformed perspectives on screening. Ideas to improve screening focused on increasing awareness of women's susceptibility to CRC, providing information on different screening tests, improving access, and the key role of health-care providers and key players themselves. We provide recommendations to leverage these vital community resources.

  14. Providing supportive care to cancer patients: a study on inter-organizational relationships

    Directory of Open Access Journals (Sweden)

    Kevin Brazil

    2008-02-01

    Full Text Available Background: Supportive cancer care (SCC has historically been provided by organizations that work independently and possess limited inter-organizational coordination. Despite the recognition that SCC services must be better coordinated, little research has been done to examine inter-organizational relationships that would enable this goal. Objective: The purpose of this study was to describe relationships among programs that support those affected by cancer. Through this description the study objective was to identify the optimal approach to coordinating SCC in the community. Methods: Senior administrators in programs that provided care to persons and their families living with or affected by cancer participated in a personal interview. Setting: South-central Ontario, Canada. Study population: administrators from 43 (97% eligible programs consented to participate in the study. Results: Network analysis revealed a diffuse system where centralization was greater in operational than administrative activities. A greater number of provider cliques were present at the operational level than the administrative level. Respondents identified several priorities to improve the coordination of cancer care in the community including: improving standards of care; establishing a regional coordinating body; increasing resources; and improving communication between programs. Conclusion: Our results point to the importance of developing a better understanding on the types of relationships that exist among service programs if effective integrated models of care are to be developed.

  15. Research in Danish cancer rehabilitation

    DEFF Research Database (Denmark)

    Høybye, Mette Terp; Dalton, Susanne Oksbjerg; Christensen, Jane

    2008-01-01

    of the cancer survivors with respect to cancer site, sociodemographic variables, social network, lifestyle, self-rated health and the prevalence of cancer-related late effects. The study is part of the FOCARE research project, in which the long-term effects of the rehabilitation programme are evaluated...

  16. Recreational physical activity and epithelial ovarian cancer: a case-control study, systematic review, and meta-analysis.

    Science.gov (United States)

    Olsen, Catherine M; Bain, Christopher J; Jordan, Susan J; Nagle, Christina M; Green, Adèle C; Whiteman, David C; Webb, Penelope M

    2007-11-01

    It remains unclear whether physical activity is associated with epithelial ovarian cancer risk. We therefore examined the association between recreational physical activity and risk of ovarian cancer in a national population-based case-control study in Australia. We also systematically reviewed all the available evidence linking physical activity with ovarian cancer to provide the best summary estimate of the association. The case-control study included women ages 18 to 79 years with a new diagnosis of invasive (n=1,269) or borderline (n=311) epithelial ovarian cancer identified through a network of clinics, physicians, and state cancer registries throughout Australia. Controls (n=1,509) were randomly selected from the national electoral roll and were frequency matched to cases by age and state. For the systematic review, we identified eligible studies using Medline, the ISI Science Citation Index, and manual review of retrieved references, and included all case-control or cohort studies that permitted assessment of an association between physical activity (recreational/occupational/sedentary behavior) and histologically confirmed ovarian cancer. Meta-analysis was restricted to the subset of these studies that reported on recreational physical activity. In our case-control study, we observed weakly inverse or null associations between recreational physical activity and risk of epithelial ovarian cancer overall. There was no evidence that the effects varied by tumor behavior or histologic subtype. Twelve studies were included in the meta-analysis, which gave summary estimates of 0.79 (95% confidence interval, 0.70-0.85) for case-control studies and 0.81 (95% confidence interval, 0.57-1.17) for cohort studies for the risk of ovarian cancer associated with highest versus lowest levels of recreational physical activity. Thus, pooled results from observational studies suggest that a modest inverse association exists between level of recreational physical activity and

  17. Study on Network Security Architecture for Power Systems

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The wide application of network technology in power systems brings not only convenience and flexibility but also security threats. An architecture of network security for power system was proposed in this study,which protected data and facilities from being attacked by outside users by means of firewall, security monitor and control system. Firewall was basically the first line of defense for the intranet; the security monitoring system was a kind of IDS (Intrusion Detection System), while security control system provided authentication, authorization,data-encrypted transmission and security management. This architecture provides various security services, such as identification, authentication, authorization, data integrity and confidentiality.

  18. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...

  19. Ecological interaction and phylogeny, studying functionality on composed networks

    Science.gov (United States)

    Cruz, Claudia P. T.; Fonseca, Carlos Roberto; Corso, Gilberto

    2012-02-01

    We study a class of composed networks that are formed by two tree networks, TP and TA, whose end points touch each other through a bipartite network BPA. We explore this network using a functional approach. We are interested in how much the topology, or the structure, of TX (X=A or P) determines the links of BPA. This composed structure is a useful model in evolutionary biology, where TP and TA are the phylogenetic trees of plants and animals that interact in an ecological community. We make use of ecological networks of dispersion of fruits, which are formed by frugivorous animals and plants with fruits; the animals, usually birds, eat fruits and disperse their seeds. We analyse how the phylogeny of TX determines or is correlated with BPA using a Monte Carlo approach. We use the phylogenetic distance among elements that interact with a given species to construct an index κ that quantifies the influence of TX over BPA. The algorithm is based on the assumption that interaction matrices that follows a phylogeny of TX have a total phylogenetic distance smaller than the average distance of an ensemble of Monte Carlo realisations. We find that the effect of phylogeny of animal species is more pronounced in the ecological matrix than plant phylogeny.

  20. Better exercise adherence after treatment for cancer (BEAT Cancer) study: Rationale, design, and methods

    OpenAIRE

    Rogers, Laura Q; McAuley, Edward; Anton, Philip M.; Courneya, Kerry S.; Vicari, Sandra; Hopkins-Price, Patricia; Verhulst, Steven; Mocharnuk, Robert; Hoelzer, Karen

    2011-01-01

    Most breast cancer survivors do not engage in regular physical activity. Our physical activity behavior change intervention for breast cancer survivors significantly improved physical activity and health outcomes post-intervention during a pilot, feasibility study. Testing in additional sites with a larger sample and longer follow-up is warranted to confirm program effectiveness short and longer term. Importantly, the pilot intervention resulted in changes in physical activity and social cogn...

  1. Inter-organizational network studies - a literature review

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten; Waldstrøm, Christian

    literature review of the last 12 years' research on inter-organizational networks, with a focus on the methodological aspects. The findings of this paper is that few of the previous studies have used the full methodological (and thus theoretical) scope of the available data and that the most cited papers...

  2. A Wireless Sensor Network Field Study: Network Development, Installation, and Measurement Results

    Science.gov (United States)

    Davis, T. W.; Kuo, C.; van Hemmen, H.; Aouni, A.; Ferriss, E.; Liang, Y.; Liang, X.

    2010-12-01

    The sustainable condition of our freshwater resources partially depends on our understanding of the natural system in which it is cycled. Exploring the status and trends of soil moisture and transpiration can help improve estimates (including flux and storage components) of water budgets on a regional-scale. As a part of this effort, a multi-node wireless network measuring sap flow, soil water content and soil water potential has been deployed in a forested and hill-sloped region in western Pennsylvania. The results of this study are presented in three components. The first is comprised of the issues faced with the development of the node mesh and its evolution to a stable network through the dense vegetation and variable topography. This component includes a comparison of mote battery life, especially over network bottlenecks, and signal transmission statistics, including parenting analysis and data packet loss. The second component examines the design and installation of the sensor nodes. Due to the frequent occurrences of precipitation, water intrusion was a major concern. This is exemplified in the water-proofing techniques used in the box design which enclosed sensors and other vulnerable electronics. The final component reviews the data collected from the network and the different techniques used for processing the measurements. A power saving scheme is tested for removing low mote battery power attenuation in the transmitted data. The results for the soil moisture and sap flow measurements are compared with data collected by a local weather station.

  3. Risk of thyroid cancer in survivors of childhood cancer: results from the British Childhood Cancer Survivor Study.

    Science.gov (United States)

    Taylor, Aliki J; Croft, Adam P; Palace, Aimee M; Winter, David L; Reulen, Raoul C; Stiller, Charles A; Stevens, Michael C G; Hawkins, Mike M

    2009-11-15

    Second primary neoplasms (SPNs) are a recognised late effect of treatment for childhood cancer. Thyroid SPNs can develop after exposure to low-dose radiation, due to the radio-sensitivity of the thyroid gland. The British Childhood Cancer Survivor Study (BCCSS) was set up to directly monitor the late effects of treatment, including risk of SPNs, in childhood cancer survivors and includes 17,980 5-year survivors. We carried out a cohort analysis to determine the risk of thyroid SPNs in the BCCSS, and estimated risk using standardised incidence ratios (SIRs), relative risk (RR) using multivariate Poisson regression and cumulative incidence curves. There were 340,202 person years at risk subsequent to a 5-year survival, median follow-up 17.4 years per survivor. We identified 50 thyroid SPNs including 31 (62%) papillary carcinomas, 15 (30%) follicular carcinomas and 4 (8%) other types. 88% of thyroid SPNs developed after exposure to radiotherapy in or around the thyroid gland. SIR overall was 18.0 (95% confidence interval 13.4-23.8). Risk of thyroid cancer was highest after Hodgkin's disease: RR 3.3 (1.1-10.1) and Non Hodgkin's Lymphoma: RR 3.4 (1.1-10.7) relative to leukaemia (RR 1.0) (p Survivors treated with radiotherapy in childhood had a RR of 4.6 (1.4-15.1) relative to survivors not treated with radiotherapy (RR 1.0), p = 0003. In conclusion, the risk of thyroid cancer in childhood cancer survivors is relatively high in this cohort of childhood cancer survivors. These results will be of use in counselling survivors of childhood cancer exposed to radiation in or around the thyroid area.

  4. Sexual Dysfunction in Breast Cancer: A Case-Control Study

    Directory of Open Access Journals (Sweden)

    Mandana Ebrahimi

    2015-02-01

    Full Text Available Background: Sexual dysfunction in breast cancer patients is considered as a common and distressing problem. Considering the increasing number of breast cancer survivors living for longer periods of time with the disease and the importance of their quality of life, we conducted the present study to compare the sexual functioning in breast cancer patients with their healthy counterparts.Methods: In this case-control study, breast cancer patients who completed their treatment protocol and were followed up for at least six months were included. The controls were healthy women with normal clinical breast examinations. All subjects filled-in the Persian version of Female Sexual Function Index questionnaire.Results: A total of 165 subjects including 71 breast cancer patients and 94 healthy women were studied. The frequency of sexual dysfunction in cases and controls was 52.6% and 47.4%, respectively (P = 0.09. There were no significant differences between the two groups regarding domain scores, except for vaginal lubrication (P = 0.045. Logistic regression analysis indicated that significant determinants of sexual dysfunction in breast cancer group was patients' age (OR = 4.0, 95%CI: 1.3 – 11.5, P = 0.01 and age of the spouse (OR= 9.8, 95% CI: 1.8-51.9, P= 0.007, while in controls, only emotional relationship with the husband was the significant predictive factor (OR = 6.3, 95%CI: 1.9 – 20.5, P = 0.002.Conclusions: Our findings indicated that sexual dysfunction is prevalent in Iranian women regardless of their physical health status. The frequency of vaginal dryness in breast cancer patients was significantly higher than controls. Age of the patient and the spouse (>40 were the only significant predictors of sexual dysfunction among women with breast cancer. Preventive strategies, sexual education and access to effective treatment should be planned in supportive care of breast cancer patients.

  5. Serum calcium concentration and prostate cancer risk: a multicenter study.

    Science.gov (United States)

    Salem, Sepehr; Hosseini, Mostafa; Allameh, Farzad; Babakoohi, Shahab; Mehrsai, Abdolrasoul; Pourmand, Gholamreza

    2013-01-01

    This study sought to further evaluate the possible effects of serum calcium level on prostate cancer (PC) risk, with considering the age, body mass index (BMI), and sex steroid hormones. Using data from a prospective multicenter study, serum calcium concentration, as well as thorough demographic and medical characteristics, were determined in 194 cases with newly diagnosed, clinicopathologically confirmed PC and 317 controls, without any malignant disease, admitted to the same network of hospitals. Serum total and ionized calcium levels were categorized into tertiles. Multivariate logistic regression model was used to estimate odds ratios (OR) and corresponding 95% confidence intervals (CI) after adjustment for major potential confounders, including age, BMI, smoking, alcohol, education, occupation, marital status, family history of PC, and sex hormones level. The mean serum calcium level (±SD) in case and control groups was 9.22 (±0.46) mg/dl and 9.48 (±0.51) mg/dl, respectively (P < 0.001). After adjustment for mentioned confounders, a significant trend of decreasing risk was found for serum total calcium concentration (OR = 0.27, 95% CI = 0.12-0.59, comparing the highest with the lowest tertile) and ionized calcium (OR = 0.25, 95% CI = 0.10-0.58). An increase of 1 mg/dl in serum calcium level was associated with a significant decrease in PC risk (OR = 0.52; 95% CI = 0.34-0.76). Our findings reveal the inverse association between serum total and ionized concentrations and PC risk, which supports the hypothesis that calcium may protect against PC. Furthermore, no evidence was found regarding age, BMI, and sex steroid hormones to modify the association between serum calcium and PC risk.

  6. Study examines quality of life factors at end of life for patients with cancer | Division of Cancer Prevention

    Science.gov (United States)

    Better quality of life at the end of life for patients with advanced cancer was associated with avoiding hospitalizations and the intensive care unit, worrying less, praying or meditating, being visited by a pastor in a hospital or clinic, and having a therapeutic alliance with their physician, according to a Dana-Farber Cancer Institute report published Online First by Archives of Internal Medicine, a JAMA Network publication. |

  7. Epidemiologic study on penile cancer in Brazil

    Directory of Open Access Journals (Sweden)

    Luciano A. Favorito

    2008-10-01

    Full Text Available OBJECTIVES: To assess epidemiologic characteristics of penile cancer in Brazil. MATERIALS AND METHODS: From May 2006 to June 2007, a questionnaire was distributed to all Brazilian urologists. Their patients' clinical and epidemiological data was analyzed (age, race, place of residence, history of sexually transmitted diseases, tobacco smoking, performance of circumcision, type of hospital service, as well as the time between the appearance of the symptoms and the diagnosis, the pathological characteristics of the tumor (histological type, degree, localization and size of lesion, stage of disease, the type of treatment performed and the present state of the patient. RESULTS: 283 new cases of penile cancer in Brazil were recorded. The majority of these cases occurred in the north and northeast (53.02% and southeast (45.54% regions. The majority of patients (224, or 78.96% were more than 46 years of age while only 21 patients (7.41% were less than 35 years of age. Of the 283 patients presenting penile cancer, 171 (60.42% had phimosis with the consequent impossibility to expose the glans. A prior medical history positive for HPV infection was reported in 18 of the 283 cases (6.36%. In 101 patients (35.68% tobacco smoking was reported. The vast majority of the cases (n = 207; 73.14% presented with tumors localized in the glans and prepuce. In 48 cases (16.96% the tumor affected the glans, the prepuce and the corpus penis; in 28 cases (9.89% the tumor affected the entire penis. The majority of the patients (n = 123; 75.26% presented with T1 or T2; only 9 patients (3.18% presented with T4 disease. CONCLUSION: Penile cancer is a very frequent pathology in Brazil, predominantly affecting low income, white, uncircumcised patients, living in the north and northeast regions of the country.

  8. Genome-wide Association Studies from the Cancer Genetic Markers of Susceptibility (CGEMS) Initiative | Office of Cancer Genomics

    Science.gov (United States)

    CGEMS identifies common inherited genetic variations associated with a number of cancers, including breast and prostate. Data from these genome-wide association studies (GWAS) are available through the Division of Cancer Epidemiology & Genetics website.

  9. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

    OpenAIRE

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user...

  10. Blood Vessel Normalization in the Hamster Oral Cancer Model for Experimental Cancer Therapy Studies

    Energy Technology Data Exchange (ETDEWEB)

    Ana J. Molinari; Romina F. Aromando; Maria E. Itoiz; Marcela A. Garabalino; Andrea Monti Hughes; Elisa M. Heber; Emiliano C. C. Pozzi; David W. Nigg; Veronica A. Trivillin; Amanda E. Schwint

    2012-07-01

    Normalization of tumor blood vessels improves drug and oxygen delivery to cancer cells. The aim of this study was to develop a technique to normalize blood vessels in the hamster cheek pouch model of oral cancer. Materials and Methods: Tumor-bearing hamsters were treated with thalidomide and were compared with controls. Results: Twenty eight hours after treatment with thalidomide, the blood vessels of premalignant tissue observable in vivo became narrower and less tortuous than those of controls; Evans Blue Dye extravasation in tumor was significantly reduced (indicating a reduction in aberrant tumor vascular hyperpermeability that compromises blood flow), and tumor blood vessel morphology in histological sections, labeled for Factor VIII, revealed a significant reduction in compressive forces. These findings indicated blood vessel normalization with a window of 48 h. Conclusion: The technique developed herein has rendered the hamster oral cancer model amenable to research, with the potential benefit of vascular normalization in head and neck cancer therapy.

  11. Eurocan plus report: feasibility study for coordination of national cancer research activities.

    Science.gov (United States)

    2008-01-01

    The EUROCAN+PLUS Project, called for by the European Parliament, was launched in October 2005 as a feasibility study for coordination of national cancer research activities in Europe. Over the course of the next two years, the Project process organized over 60 large meetings and countless smaller meetings that gathered in total over a thousand people, the largest Europe-wide consultation ever conducted in the field of cancer research.Despite a strong tradition in biomedical science in Europe, fragmentation and lack of sustainability remain formidable challenges for implementing innovative cancer research and cancer care improvement. There is an enormous duplication of research effort in the Member States, which wastes time, wastes money and severely limits the total intellectual concentration on the wide cancer problem. There is a striking lack of communication between some of the biggest actors on the European scene, and there are palpable tensions between funders and those researchers seeking funds.It is essential to include the patients' voice in the establishment of priority areas in cancer research at the present time. The necessity to have dialogue between funders and scientists to establish the best mechanisms to meet the needs of the entire community is evident. A top priority should be the development of translational research (in its widest form), leading to the development of effective and innovative cancer treatments and preventive strategies. Translational research ranges from bench-to-bedside innovative cancer therapies and extends to include bringing about changes in population behaviours when a risk factor is established.The EUROCAN+PLUS Project recommends the creation of a small, permanent and independent European Cancer Initiative (ECI). This should be a model structure and was widely supported at both General Assemblies of the project. The ECI should assume responsibility for stimulating innovative cancer research and facilitating processes

  12. Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes.

    Science.gov (United States)

    Kalamohan, Kalaivani; Periasamy, Jayaprakash; Bhaskar Rao, Divya; Barnabas, Georgina D; Ponnaiyan, Srigayatri; Ganesan, Kumaresan

    2014-10-01

    Despite the advancements in the cancer therapeutics, gastric cancer ranks as the second most common cancers with high global mortality rate. Integrative functional genomic investigation is a powerful approach to understand the major dysregulations and to identify the potential targets toward the development of targeted therapeutics for various cancers. Intestinal and diffuse type gastric tumors remain the major subtypes and the molecular determinants and drivers of these distinct subtypes remain unidentified. In this investigation, by exploring the network of gene coexpression association in gastric tumors, mRNA expressions of 20,318 genes across 200 gastric tumors were categorized into 21 modules. The genes and the hub genes of the modules show gastric cancer subtype specific expression. The expression patterns of the modules were correlated with intestinal and diffuse subtypes as well as with the differentiation status of gastric tumors. Among these, G1 module has been identified as a major driving force of diffuse type gastric tumors with the features of (i) enriched mesenchymal, mesenchymal stem cell like, and mesenchymal derived multiple lineages, (ii) elevated OCT1 mediated transcription, (iii) involvement of Notch activation, and (iv) reduced polycomb mediated epigenetic repression. G13 module has been identified as key factor in intestinal type gastric tumors and found to have the characteristic features of (i) involvement of embryonic stem cell like properties, (ii) Wnt, MYC and E2F mediated transcription programs, and (iii) involvement of polycomb mediated repression. Thus the differential transcription programs, differential epigenetic regulation and varying stem cell features involved in two major subtypes of gastric cancer were delineated by exploring the gene coexpression network. The identified subtype specific dysregulations could be optimally employed in developing subtype specific therapeutic targeting strategies for gastric cancer.

  13. Isoliquiritigenin induces growth inhibition and apoptosis through downregulating arachidonic acid metabolic network and the deactivation of PI3K/Akt in human breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ying; Zhao, Haixia [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Wang, Yuzhong [Key Laboratory for Oral Biomedical Engineering of Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan 430079 (China); Zheng, Hao; Yu, Wei; Chai, Hongyan [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Zhang, Jing [Animal Experimental Center of Wuhan University, Wuhan 430071 (China); Falck, John R. [Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390,USA (United States); Guo, Austin M. [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Department of Pharmacology, New York Medical College, Valhalla, NY 10595 (United States); Yue, Jiang; Peng, Renxiu [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Yang, Jing, E-mail: yangjingliu2013@163.com [Department of Pharmacology, School of Medicine, Wuhan University, Wuhan 430071 (China); Research Center of Food and Drug Evaluation, Wuhan University, Wuhan 430071 (China)

    2013-10-01

    network and the deactivation of PI3K/Akt in human breast cancer. - Highlights: • Isoliquiritigenin induces growth inhibition and apoptosis in human breast cancer. • The proapoptotic action of isoliquiritigenin has been studied in vitro and in vivo. • Arachidonic acid metabolic network mediates isoliquiritigenin-induced apoptosis. • PI3K/Akt deactivation is asssociated with isoliquiritigenin-induced apoptosis. • Isoliquiritigenin may be a multi-target drug in the treatment of breast cancer.

  14. Texture Classification using Artificial Neural Network for Diagnosis of Skin Cancer

    Directory of Open Access Journals (Sweden)

    Dalia N. Abdul-Wadood

    2014-07-01

    Full Text Available This paper attempts to improve the efficiency of the system that proposed in [1] to determine whether a given skin lesion microscopic image is malignant or benign; in case of malignancy, the system can specify its type; whether it is squamous cell carcinoma or basal cell carcinoma (the two leading skin cancer types. The testing of this system was conducted using 80 microscopic images of skin tissues of the types normal, benign and the two types of skin cancer (squamous and basal; the images have been collected from different hospital pathology departments as part of the research work. Some of the collected samples have been used as training and others as testing materials. The proposed system consists of 3 main steps. First, extraction of a set of textural descriptors to localize the abnormal visual attributes which may appear in the tested skin tissue images. Second, selection of the best discriminating texture features. Third, identify the type of skin tissue images using artificial neural network (ANN. In the training phase, the system was trained using 50 skin tissue images, the textural features extracted from training samples were analyzed and their discrimination powers were evaluated in order to get a list of the most suitable features for auto recognition task. When ANN is trained on co-occurrence features the attained allocation accuracy rates was (%97.71 and the diagnosis accuracy rate was (%98.75. While when using ANN with combinations of different types of textural features; the allocation accuracy rate reached to (%97.90 while the diagnosis accuracy rate became (%98.75

  15. Screening and cervical cancer cure: population based cohort study

    OpenAIRE

    Andrae, B.; Andersson, T. M.-L.; Lambert, P C; Kemetli, L.; Silfverdal, L.; Strander, B.; Ryd, W.; Dillner, J.; Tornberg, S.; Sparen, P.

    2012-01-01

    Objective To determine whether detection of invasive cervical cancer by screening results in better prognosis or merely increases the lead time until death. Design Nationwide population based cohort study. Setting Sweden. Participants All 1230 women with cervical cancer diagnosed during 1999-2001 in Sweden prospectively followed up for an average of 8.5 years. Main outcome measures Cure proportions and five year relative survival ratios, stratified by screening history, mode of detection, age...

  16. [Scientific collaboration between Istituto Superiore di Sanità and Italian Association of Cancer Registries for the study of cancer incidence in Italian polluted sites].

    Science.gov (United States)

    Comba, P; Crocetti, E; Buzzoni, C; Fazzo, L; Ferretti, S; Fusco, M; Iavarone, I; Pirastu, R; Ricci, P

    2011-01-01

    The collaborative study between Istituto superiore di sanità and Associazione italiana registri tumori (ISS-AIRTUM) aims at investigating cancer incidence in polluted sites for adults and for children (0-14 years) and adolescents (15-19 years) to comment the study results in the light of a set of a priori hypotheses. On the whole, 141 out of 298 municipalities included in SENTIERI Project are served by a Cancer Register participating to the AIRTUM network. For a description of SENTIERI, refer to the 2010 Supplement of Epidemiology & Prevention devoted to SENTIERI Project. The time window of the study is the period 1996-2005. The number of expected cases in each polluted site will be estimated by applying incidence rates of the national pool of cancer registries and of the pool of the geographic macroarea in which each site is located: Northern, Central, Southern Italy and Islands. Cancer incidence in children and adolescents is one of the main priorities of international public health institutions, because of the need to protect childhood health from involuntary exposure to environmental risk factors. Standardized incidence ratios (SIRs) will be computed using expected figures derived from the national pool of cancer registries.

  17. Transcriptional network in ovarian cancer cell line SKOV3 treated with Pinellia pedatisecta Schott extract.

    Science.gov (United States)

    Zhou, Li; Xu, Teng; Zhang, Ying; Zhu, Mei; Zhu, Wen; Wang, Ziqiang; Gu, Hangzhi; Wang, Hanchu; Li, Peizhen; Ying, Jun; Yang, Lei; Ren, Ping; Li, Jinsong; Xu, Zuyuan; Ni, Liyan; Bao, Qiyu; Chen, Jindong

    2016-07-01

    Ovarian cancer is the most lethal disease among the malignant tumors of female reproductive organs. Few successful therapeutic options exist for patients with ovarian cancer. The common therapeutic methods are surgical operation, chemotherapy, radiotherapy, and combination of these treatments. In recent years, studies have indicated that Pinellia pedatisecta Schott (PPS), a traditional Chinese medicine, could inhibit tumor growth. In this study, we demonstrated that PPS extract could induce apoptosis in SKOV3 cells in a dose- and time-dependent manner. We further conducted transcriptome sequencing on PPS extract-treated SKOV3 cells along with controls, and identified 1,754 transcripts whose expression differs at least 3-fold over the controls. These differentially expressed transcripts include the apoptosis-related genes such as the caspase family members, and were significantly enriched in steroid biosynthesis in the KEGG pathway database compared with the transcriptome background. Most of the differentially expressed transcripts from this pathway were upregulated in PPS extract-treated cell line, indicating that PPS extract-induced apoptosis was accompanied by increased steroid biosynthesis (e.g. zymosterol). These results suggest that PPS extract could be a new cytostatic therapeutic agent for ovarian cancer.

  18. Research from the Early Detection Research Network on New Methods to Detect Prostate Cancer | Division of Cancer Prevention

    Science.gov (United States)

    Prostate cancer is the most frequently diagnosed non-skin cancer in men in the United States. In 2010 there were 218,000 men diagnosed with prostate cancer. The prevalence of the diagnosis makes the disease a major health burden. While the majority of the diagnosed men will survive the disease, about 15% will die from it, a rate that is affected by over-diagnosis and the consequent over-treatment. |

  19. Phase III study by the Norwegian lung cancer study group

    DEFF Research Database (Denmark)

    Grønberg, Bjørn H; Bremnes, Roy M; Fløtten, Oystein

    2009-01-01

    PURPOSE To compare pemetrexed/carboplatin with a standard regimen as first-line therapy in advanced non-small-cell lung cancer NSCLC. PATIENTS AND METHODS Patients with stage IIIB or IV NSCLC and performance status of 0 to 2 were randomly assigned to receive pemetrexed 500 mg/m(2) plus carboplatin......, and fatigue reported on the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 and the lung cancer-specific module LC13 during the first 20 weeks. Secondary end points were overall survival and toxicity. Results Four hundred thirty-six eligible patients were enrolled...

  20. Study of hydrodynamic model in sluice controlled river networks

    Science.gov (United States)

    Li, Yan; Zeng, Fantang

    2010-05-01

    Shiqi river network ,is situated in the Zhongshan city of Guangdong province in the P.R.China. The river network covers approximately 702.55km2 ,with a total river length of over 500km and extending over 34km from north to south and over 46km from east to west. The river network overlaps with the most densely populated and economically developed region in the Pear River Delta Economic Zone. In 2008 the region had a population of 1 846.9 thousands And a GDP of more than 8 2500 million RMB. All branches of the river network are encircled by the main rivers of Pear River Delta(PRD) network. With the economic and social development, all natural connections with the external rivers are controlled by the sluices, water body exchanges between the Shiqi river network and external rivers are significantly changed by human activities. The overall objective the research is to develop a tool for the local Environmental Protection Bureau to Understand and quantify the impact of the artificial construction on the hydrological cycle. The developed model can accurate representation of the water levels and flows in the study area, to allow accurate representation of the transport of pollutants. The river network topography is derived directly from the available database. Only the "major" rivers were included in the model, because cross-section data for the "minor" rivers are currently not available. In general, the 1D hydrodynamic model is provided with flow boundary conditions ("Q") at its upstream boundaries and with water level boundary conditions ("z") at its downstream boundaries. For all boundaries of Shiqi river network, there are no flow records available, all records are water level. To reflect the hydrodynamic process accurately, the author developed a new methods to set the hydrodynamic model's boundary. For each boundary, the boundary condition is "Z" when the sluice is open, and the boundary condition is "Q" while it is closed. The open or close condition is identified

  1. Active Barrett's Esophagus Translational Research Network Grants | Division of Cancer Prevention

    Science.gov (United States)

    The Division of Cancer Prevention (DCP) conducts and supports research to determine a person's risk of cancer and to find ways to reduce the risk. This knowledge is critical to making prog