Gene-environment interactions involving functional variants

DEFF Research Database (Denmark)

Barrdahl, Myrto; Rudolph, Anja; Hopper, John L

2017-01-01

.36, 95% CI: 1.16-1.59, pint  = 1.9 × 10(-5) ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint  = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10...... epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER......) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP 

Quantitative Chemical-Genetic Interaction Map Connects Gene Alterations to Drug Responses | Office of Cancer Genomics

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In a recent Cancer Discovery report, CTD2 researchers at the University of California in San Francisco developed a new quantitative chemical-genetic interaction mapping approach to evaluate drug sensitivity or resistance in isogenic cell lines. Performing a high-throughput screen with isogenic cell lines allowed the researchers to explore the impact of a panel of emerging and established drugs on cells overexpressing a single cancer-associated gene in isolation.

Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

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Yang, Cheng-Hong; Chang, Hsueh-Wei

2014-01-01

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer. PMID:24895547

Gene-diet-interactions in folate-mediated one-carbon metabolism modify colon cancer risk.

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Liu, Amy Y; Scherer, Dominique; Poole, Elizabeth; Potter, John D; Curtin, Karen; Makar, Karen; Slattery, Martha L; Caan, Bette J; Ulrich, Cornelia M

2013-04-01

The importance of folate-mediated one-carbon metabolism (FOCM) in colorectal carcinogenesis is emphasized by observations that high dietary folate intake is associated with decreased risk of colon cancer (CC) and its precursors. Additionally, polymorphisms in FOCM-related genes have been repeatedly associated with risk, supporting a causal relationship between folate and colorectal carcinogenesis. We investigated ten candidate polymorphisms with defined or probable functional impact in eight FOCM-related genes (SHMT1, DHFR, DNMT1, MTHFD1, MTHFR, MTRR, TCN2, and TDG) in 1609 CC cases and 1974 controls for association with CC risk and for interaction with dietary factors. No polymorphism was statistically significantly associated with overall risk of CC. However, statistically significant interactions modifying CC risk were observed for DNMT1 I311V with dietary folate, methionine, vitamin B2 , and vitamin B12 intake and for MTRR I22M with dietary folate, a predefined one-carbon dietary pattern, and vitamin B6 intake. We observed statistically significant gene-diet interactions with five additional polymorphisms. Our results provide evidence that FOCM-related dietary intakes modify the association between CC risk and FOCM allelic variants. These findings add to observations showing that folate-related gene-nutrient interactions play an important role in modifying the risk of CC. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Case-only gene-environment interaction between ALAD tagSNPs and occupational lead exposure in prostate cancer.

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Neslund-Dudas, Christine; Levin, Albert M; Rundle, Andrew; Beebe-Dimmer, Jennifer; Bock, Cathryn H; Nock, Nora L; Jankowski, Michelle; Datta, Indrani; Krajenta, Richard; Dou, Q Ping; Mitra, Bharati; Tang, Deliang; Rybicki, Benjamin A

2014-05-01

Black men have historically had higher blood lead levels than white men in the U.S. and have the highest incidence of prostate cancer in the world. Inorganic lead has been classified as a probable human carcinogen. Lead (Pb) inhibits delta-aminolevulinic acid dehydratase (ALAD), a gene recently implicated in other genitourinary cancers. The ALAD enzyme is involved in the second step of heme biosynthesis and is an endogenous inhibitor of the 26S proteasome, a master system for protein degradation and a current target of cancer therapy. Using a case-only study design, we assessed potential gene-environment (G × E) interactions between lifetime occupational Pb exposure and 11 tagSNPs within ALAD in black (N = 260) and white (N = 343) prostate cancer cases. Two ALAD tagSNPs in high linkage disequilibrium showed significant interaction with high Pb exposure among black cases (rs818684 interaction odds ratio or IOR = 2.73, 95% CI 1.43-5.22, P = 0.002; rs818689 IOR = 2.20, 95% CI 1.15-4.21, P = 0.017) and an additional tagSNP, rs2761016, showed G × E interaction with low Pb exposure (IOR = 2.08, 95% CI 1.13-3.84, P = 0.019). Further, the variant allele of rs818684 was associated with a higher Gleason grade in those with high Pb exposure among both blacks (OR 3.96, 95% CI 1.01-15.46, P = 0.048) and whites (OR 2.95, 95% CI 1.18-7.39, P = 0.020). Genetic variation in ALAD may modify associations between Pb and prostate cancer. Additional studies of ALAD, Pb, and prostate cancer are warranted and should include black men. Prostate 74:637-646, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

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Shiao, S Pamela K; Grayson, James; Yu, Chong Ho; Wasek, Brandi; Bottiglieri, Teodoro

2018-02-16

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls ( p relation to gene-environment interactions in the prevention of CRC.

Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors

DEFF Research Database (Denmark)

Nickels, Stefan; Truong, Thérèse; Hein, Rebecca

2013-01-01

Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cance...

Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

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Naderi, Elnaz; Mostafaei, Mehdi; Pourshams, Akram

2014-01-01

Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches. PMID:24895587

Network of microRNAs-mRNAs Interactions in Pancreatic Cancer

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Elnaz Naderi

2014-01-01

Full Text Available Background. MicroRNAs are small RNA molecules that regulate the expression of certain genes through interaction with mRNA targets and are mainly involved in human cancer. This study was conducted to make the network of miRNAs-mRNAs interactions in pancreatic cancer as the fourth leading cause of cancer death. Methods. 56 miRNAs that were exclusively expressed and 1176 genes that were downregulated or silenced in pancreas cancer were extracted from beforehand investigations. MiRNA–mRNA interactions data analysis and related networks were explored using MAGIA tool and Cytoscape 3 software. Functional annotations of candidate genes in pancreatic cancer were identified by DAVID annotation tool. Results. This network is made of 217 nodes for mRNA, 15 nodes for miRNA, and 241 edges that show 241 regulations between 15 miRNAs and 217 target genes. The miR-24 was the most significantly powerful miRNA that regulated series of important genes. ACVR2B, GFRA1, and MTHFR were significant target genes were that downregulated. Conclusion. Although the collected previous data seems to be a treasure trove, there was no study simultaneous to analysis of miRNAs and mRNAs interaction. Network of miRNA-mRNA interactions will help to corroborate experimental remarks and could be used to refine miRNA target predictions for developing new therapeutic approaches.

A Partial Least Square Approach for Modeling Gene-gene and Gene-environment Interactions When Multiple Markers Are Genotyped

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Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C.

2008-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI. PMID:18615621

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

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Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C

2009-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

Prioritizing genes associated with prostate cancer development

International Nuclear Information System (INIS)

Gorlov, Ivan P; Logothetis, Christopher J; Sircar, Kanishka; Zhao, Hongya; Maity, Sankar N; Navone, Nora M; Gorlova, Olga Y; Troncoso, Patricia; Pettaway, Curtis A; Byun, Jin Young

2010-01-01

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development. A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data. Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development. By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

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Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-06

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

Gene-gene and gene-environment interactions in prostate, breast and colorectal cancer

DEFF Research Database (Denmark)

Kopp, Tine Iskov

The incidence of cancer in the western world has increased steeply during the last 50 years. For three of the most prevalent cancer types in Denmark, prostate, breast and colorectal cancer (PC, BC and CRC, respectively), only a small fraction (1-15%) of the incidences are caused by highly penetrant...... in alcohol-related BC in postmenopausal women involving a specific polymorphism in PPARG (coding the peroxisome proliferatoractivated receptor (PPARγ)) and its interaction with the aromatase (encoded by CYP19A1) was investigated (Paper V-VI). The Danish prospective “Diet, Cancer and Health” cohort study...... as having strong influence on carcinogenesis. Therefore, very frequent, low effect polymorphisms may have a greater contribution on a population level in combination with environmental factors. Indeed, several dietary and life style factors are now well-established risk factors for different cancer types...

Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors.

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Stefan Nickels

Full Text Available Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4 × 10(-6 and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1 × 10(-4. Overall, the per-allele odds ratio (95% confidence interval for LSP1-rs3817198 was 1.08 (1.01-1.16 in nulliparous women and ranged from 1.03 (0.96-1.10 in parous women with one birth to 1.26 (1.16-1.37 in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85-0.98 in those with an alcohol intake of <20 g/day and 1.45 (1.14-1.85 in those who drank ≥ 20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3 × 10(-5, with a per-allele OR of 1.14 (1.11-1.17 in parous women and 0.98 (0.92-1.05 in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.

Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

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Carlos Roberto Arias

2012-01-01

Full Text Available Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF. The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

Glutathione S-transferase P1, gene-gene interaction, and lung cancer susceptibility in the Chinese population: An updated meta-analysis and review

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Xue-Ming Li

2015-01-01

Full Text Available Aim of Study: To assess the impact of glutathione S-transferase P1 (GSTP1 Ile105Val polymorphism on the risk of lung cancer in the Chinese population, an updated meta-analysis and review was performed. Materials and Methods: Relevant studies were identified from PubMed, Springer Link, Ovid, Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure, and Chinese Biology Medicine published through January 22, 2015. The odds ratios (ORs and 95% confidence intervals (CIs were calculated to estimate the strength of the associations. Results: A total of 13 case-control studies, including 2026 lung cancer cases and 2451 controls, were included in this meta-analysis. Overall, significantly increased lung cancer risk was associated with the variant genotypes of GSTP1 polymorphism in the Chinese population (GG vs. AA: OR = 1.36, 95% CI = 1.01-1.84. In subgroup analyses stratified by geographic area and source of controls, the significant results were found in population-based studies (GG vs. AA: OR = 1.62, 95% CI: 1.13-2.31; GG vs. AG: OR = 1.49, 95% CI: 1.03-2.16; GG vs. AA + AG: OR = 1.55, 95% CI: 1.12-2.26. A gene-gene interaction analysis showed that there was an interaction for individuals with combination of GSTM1 (or GSTT1 null genotype and GSTP1 (AG + GG mutant genotype for lung cancer risk in Chinese. Conclusion: This meta-analysis suggests that GSTP1 Ile105Val polymorphism may increase the risk of lung cancer in the Chinese population.

The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

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Frank eEmmert-Streib

2014-02-01

Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups

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S. Pamela K. Shiao

2018-02-01

Full Text Available For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene–environment interactions and predictors of colorectal cancer (CRC by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black. We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05, on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05 except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike’s information criterion and leave-one-out cross validation methods. Body mass index (BMI and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene–environment interactions in the prevention of CRC.

Meta-analysis of Cancer Gene Profiling Data.

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Roy, Janine; Winter, Christof; Schroeder, Michael

2016-01-01

The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.

Prostate Cancer Epigenetics: A Review on Gene Regulation

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Lena Diaw

2007-01-01

Full Text Available Prostate cancer is the most common cancer in men in western countries, and its incidence is increasing steadily worldwide. Molecular changes including both genetic and epigenetic events underlying the development and progression of this disease are still not well understood. Epigenetic events are involved in gene regulation and occur through different mechanisms such as DNA methylation and histone modifi cations. Both DNA methylation and histone modifi cations affect gene regulation and play important roles either independently or by interaction in tumor initiation and progression. This review will discuss the genes associated with epigenetic alterations in prostate cancer progression: their regulation and importance as possible markers for the disease.

In silico analysis of SNPs of SYK gene Involved in Oral Cancer

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Sarita Swain

2017-12-01

Full Text Available Oral cancer is the sixth most common cancer in the world. Oral cancer is the cancer of the oral cavity and pharynx, including cancer of the lip, tongue, salivary glands, gum, floor and other areas of the mouth. The aim of the study is to identify SNPs using dbSNP and predict the effect of mutation using Predict SNP. The association of genes is done by STRING. The disease and drugs associated with the genes are obtained from Webgestalt. The prediction of binding site is done by CASTp. The interaction of ligand and protein is done by using Autodock and Visualised through Discovery studio, pymol, Ligplot. From this report we found that oral cancer differs from person to person based on their genes and genetic interactions and expressions which recommend the clinicians to go for personalized medicine rather that generalized medicine for the patients with oral cancer. Seeking the importance of genetic background of oral cancer patients further studies can be done by mining of non-synonymous SNPs associated with genes for causing oral cancer.

A Classification Framework Applied to Cancer Gene Expression Profiles

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Hussein Hijazi

2013-01-01

Full Text Available Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM, bagging, and random forest on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression increase the prediction accuracy as compared to using gene expression alone.

Protein-Protein Interaction Reagents | Office of Cancer Genomics

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The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

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The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

Insights into significant pathways and gene interaction networks underlying breast cancer cell line MCF-7 treated with 17β-estradiol (E2).

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Huan, Jinliang; Wang, Lishan; Xing, Li; Qin, Xianju; Feng, Lingbin; Pan, Xiaofeng; Zhu, Ling

2014-01-01

Estrogens are known to regulate the proliferation of breast cancer cells and to alter their cytoarchitectural and phenotypic properties, but the gene networks and pathways by which estrogenic hormones regulate these events are only partially understood. We used global gene expression profiling by Affymetrix GeneChip microarray analysis, with KEGG pathway enrichment, PPI network construction, module analysis and text mining methods to identify patterns and time courses of genes that are either stimulated or inhibited by estradiol (E2) in estrogen receptor (ER)-positive MCF-7 human breast cancer cells. Of the genes queried on the Affymetrix Human Genome U133 plus 2.0 microarray, we identified 628 (12h), 852 (24h) and 880 (48 h) differentially expressed genes (DEGs) that showed a robust pattern of regulation by E2. From pathway enrichment analysis, we found out the changes of metabolic pathways of E2 treated samples at each time point. At 12h time point, the changes of metabolic pathways were mainly focused on pathways in cancer, focal adhesion, and chemokine signaling pathway. At 24h time point, the changes were mainly enriched in neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction and calcium signaling pathway. At 48 h time point, the significant pathways were pathways in cancer, regulation of actin cytoskeleton, cell adhesion molecules (CAMs), axon guidance and ErbB signaling pathway. Of interest, our PPI network analysis and module analysis found that E2 treatment induced enhancement of PRSS23 at the three time points and PRSS23 was in the central position of each module. Text mining results showed that the important genes of DEGs have relationship with signal pathways, such as ERbB pathway (AREG), Wnt pathway (NDP), MAPK pathway (NTRK3, TH), IP3 pathway (TRA@) and some transcript factors (TCF4, MAF). Our studies highlight the diverse gene networks and metabolic and cell regulatory pathways through which E2 operates to achieve its

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk

DEFF Research Database (Denmark)

Usset, Joseph L; Raghavan, Rama; Tyrer, Jonathan P

2016-01-01

and non-obese women. METHODS: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy...... Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC...

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

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Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Emory University: High-Throughput Protein-Protein Interaction Dataset for Lung Cancer-Associated Genes | Office of Cancer Genomics

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To discover novel PPI signaling hubs for lung cancer, CTD2 Center at Emory utilized large-scale genomics datasets and literature to compile a set of lung cancer-associated genes. A library of expression vectors were generated for these genes and utilized for detecting pairwise PPIs with cell lysate-based TR-FRET assays in high-throughput screening format. Read the abstract.

Text mining in cancer gene and pathway prioritization.

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Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

CCDB: a curated database of genes involved in cervix cancer.

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Agarwal, Subhash M; Raghav, Dhwani; Singh, Harinder; Raghava, G P S

2011-01-01

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon-intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.

Behavioral science and the study of gene-nutrition and gene-physical activity interactions in obesity research.

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Faith, Myles S

2008-12-01

This report summarizes emerging opportunities for behavioral science to help advance the field of gene-environment and gene-behavior interactions, based on presentations at The National Cancer Institute (NCI) Workshop, "Gene-Nutrition and Gene-Physical Activity Interactions in the Etiology of Obesity." Three opportunities are highlighted: (i) designing potent behavioral "challenges" in experiments, (ii) determining viable behavioral phenotypes for genetics studies, and (iii) identifying specific measures of the environment or environmental exposures. Additional points are underscored, including the need to incorporate novel findings from neuroimaging studies regarding motivation and drive for eating and physical activity. Advances in behavioral science theory and methods can play an important role in advancing understanding of gene-brain-behavior relationships in obesity onset.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

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Mary Qu Yang

Full Text Available Clear cell renal cell carcinoma (ccRCC is the most common and most aggressive form of renal cell cancer (RCC. The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways. Keywords: ccRCC, Causative mutation, Pathways, Protein-protein interaction, Gene module, eQTL

Human synthetic lethal inference as potential anti-cancer target gene detection

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Solé Ricard V

2009-12-01

Full Text Available Abstract Background Two genes are called synthetic lethal (SL if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. Results In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases as well as on existent approved drugs (DrugBank database supports our selection of cancer-therapy candidates. Conclusions Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

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Jia, Peilin; Zhao, Zhongming

2014-02-01

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.

Distinct gene expression profiles in ovarian cancer linked to Lynch syndrome

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Jönsson, Jenny-Maria; Bartuma, Katarina; Dominguez-Valentin, Mev

2014-01-01

Ovarian cancer linked to Lynch syndrome represents a rare subset that typically presents at young age as early-stage tumors with an overrepresentation of endometrioid and clear cell histologies. We investigated the molecular profiles of Lynch syndrome-associated and sporadic ovarian cancer...... with the aim to identify key discriminators and central tumorigenic mechanisms in hereditary ovarian cancer. Global gene expression profiling using whole-genome c-DNA-mediated Annealing, Selection, extension, and Ligation was applied to 48 histopathologically matched Lynch syndrome-associated and sporadic...... ovarian cancers. Lynch syndrome-associated and sporadic ovarian cancers differed by 349 significantly deregulated genes, including PTPRH, BIRC3, SHH and TNFRSF6B. The genes involved were predominantly linked to cell growth, proliferation, and cell-to-cell signaling and interaction. When stratified...

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

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Sungyoung Lee

2012-12-01

Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Network graph analysis of gene-gene interactions in genome-wide association study data.

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Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

2012-12-01

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

An epistatic interaction between the PAX8 and STK17B genes in papillary thyroid cancer susceptibility.

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Iñigo Landa

Full Text Available Papillary Thyroid Cancer (PTC is a heterogeneous and complex disease; susceptibility to PTC is influenced by the joint effects of multiple common, low-penetrance genes, although relatively few have been identified to date. Here we applied a rigorous combined approach to assess both the individual and epistatic contributions of genetic factors to PTC susceptibility, based on one of the largest series of thyroid cancer cases described to date. In addition to identifying the involvement of TSHR variation in classic PTC, our pioneer study of epistasis revealed a significant interaction between variants in STK17B and PAX8. The interaction was detected by MD-MBR (p = 0.00010 and confirmed by other methods, and then replicated in a second independent series of patients (MD-MBR p = 0.017. Furthermore, we demonstrated an inverse correlation between expression of PAX8 and STK17B in a set of cell lines derived from human thyroid carcinomas. Overall, our work sheds additional light on the genetic basis of thyroid cancer susceptibility, and suggests a new direction for the exploration of the inherited genetic contribution to disease using association studies.

P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

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Webb-Robertson, Bobbie-Jo M; Bramer, Lisa M; Jensen, Jeffrey L; Kobold, Markus A; Stratton, Kelly G; White, Amanda M; Rodland, Karin D

2017-11-01

P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR . ©2017 American Association for Cancer Research.

Oxidative balance and colon and rectal cancer: interaction of lifestyle factors and genes.

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Slattery, Martha L; Lundgreen, Abbie; Welbourn, Bill; Wolff, Roger K; Corcoran, Christopher

2012-06-01

Pro-oxidant and anti-oxidant genetic and lifestyle factors can contribute to an individual's level of oxidative stress. We hypothesize that diet, lifestyle and genetic factors work together to influence colon and rectal cancer through an oxidative balance mechanism. We evaluated nine markers for eosinophil peroxidase (EPX), two for myeloperoxidase (MPO), four for hypoxia-inducible factor-1A (HIFIA), and 16 for inducible nitric oxide synthase (NOS2A) in conjunction with dietary antioxidants, aspirin/NSAID use, and cigarette smoking. We used data from population-based case-control studies (colon cancer n=1555 cases, 1956 controls; rectal cancer n=754 cases, 959 controls). Only NOS2A rs2297518 was associated with colon cancer (OR 0.86 95% CI 0.74, 0.99) and EPX rs2302313 and MPO rs2243828 were associated with rectal cancer (OR 0.75 95% CI 0.59, 0.96; OR 0.81 95% CI 0.67, 0.99 respectively) for main effects. However, after adjustment for multiple comparisons we observed the following significant interactions for colon cancer: NOS2A and lutein, EPX and aspirin/NSAID use, and NOS2A (4 SNPs) and cigarette smoking. For rectal cancer we observed the following interactions after adjustment for multiple comparisons: HIF1A and vitamin E, NOS2A (3SNPs) with calcium; MPO with lutein; HIF1A with lycopene; NOS2A with selenium; EPX and NOS2A with aspirin/NSAID use; HIF1A, MPO, and NOS2A (3 SNPs) with cigarette smoking. We observed significant interaction between a composite oxidative balance score and a polygenic model for both colon (p interaction 0.0008) and rectal cancer (p=0.0018). These results suggest the need to comprehensively evaluate interactions to assess the contribution of risk from both environmental and genetic factors. Copyright © 2012 Elsevier B.V. All rights reserved.

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

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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

Case-only study of interactions between metabolic enzymes and smoking in colorectal cancer

International Nuclear Information System (INIS)

Fan, Chunhong; Jin, Mingjuan; Chen, Kun; Zhang, Yongjing; Zhang, Shuangshuang; Liu, Bing

2007-01-01

Gene-gene and gene-environment interactions involved in the metabolism of carcinogens may increase the risk of cancer. Our objective was to measure the interactions between common polymorphisms of P450 (CYP1A2, CYP1B1, CYP2E1), GSTM1 and T1, SULT1A1 and cigarette smoking in colorectal cancer (CRC). A case-only design was conducted in a Chinese population including 207 patients with sporadic CRC. Unconditional logistic regression analysis was performed adjusting for age, gender, alcohol consumption, and cigarette smoking. The interaction odds ratio (COR) for the gene-gene interaction between CYP1B1 1294G and SULT1A1 638A allele was 2.68 (95% CI: 1.16–6.26). The results of the gene-environment analyses revealed that an interaction existed between cigarette smoking and the CYP1B1 1294G allele for CRC (COR = 2.62, 95%CI: 1.01–6.72), the COR for the interaction of CYP1B1 1294G and smoking history > 35 pack-years was 3.47 (95%CI: 1.12–10.80). No other significant gene-gene and gene-environment interactions were observed. Our results showed that the interaction between polymorphisms in CYP1B1 1294G and SULT1A1*2 may play a significant role on CRC in the Chinese population. Also, it is suggested that the association between cigarette smoking and CRC could be differentiated by the CYP1B1 1294G allele

Identification of human disease genes from interactome network using graphlet interaction.

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Xiao-Dong Wang

Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

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Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.

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Hutter, Carolyn M; Mechanic, Leah E; Chatterjee, Nilanjan; Kraft, Peter; Gillanders, Elizabeth M

2013-11-01

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors. © 2013 WILEY PERIODICALS, INC.

PTH Gene Polymorphism and Breast Cancer Risk in Kazakhstan

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Nurgul Sikhayeva

2014-12-01

Full Text Available Introduction. Breast cancer is the most common type of cancer among women. In Kazakhstan, breast cancer holds first place among causes of women death caused by cancer in the 45-55 year age group . Many studies have shown that the risk of acquiring breast cancer may be related to the level of calcium in the blood serum. One of the important regulators of calcium metabolism in the body is the parathyroid hormone. Single nucleotide polymorphisms in the gene encoding the parathyroid hormone (PTH are associated with breast cancer development risk, and may modify the associative interaction between the levels of calcium intake and breast cancer. Experimental studies have shown that PTH gene has a carcinogenic effect. At least three studies showed a weak positive correlation between the risk of acquiring breast cancer and primary hyperparathyroidism, a state with high levels of PTH and often high levels of calcium. The aim of this investigation was to evaluate potential association between PTH gene polymorphism and breast cancer risk among Kazakhstani women.Methods. Female breast cancer patients (n = 429 and matched control women (n = 373 were recruited into a case – control study,. Genomic DNA was extracted from peripheral venous blood of study participants using Wizard® Genomic DNA Purification Kit (Promega, USA. Detection of PTH gene polymorphism (rs1459015 was done by means of the TaqMan® SNP Genotyping Assay of real-time PCR. Statistical analysis was conducted using SPSS 19.0.Results. PTH gene alleles were in Hardy–Weinberg equilibrium (p > 0.05. Distribution was 59% CC, 35% CT, 6% TT in the group with breast cancer and 50% CC, 43% CT, 6% TT in the control group. Total difference (between the group with breast cancer and the control group in allele frequencies for PTH polymorphism was not significant (p > 0.05. No association was found between rs1459015 TT and breast cancer risk (OR = 1.039; 95%, CI 0.740 - 1.297; p = 0.893.Conclusion. We

Evaluation of candidate stromal epithelial cross-talk genes identifies association between risk of serous ovarian cancer and TERT, a cancer susceptibility "hot-spot"

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Johnatty, Sharon E; Beesley, Jonathan; Chen, Xiaoqing

2010-01-01

We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes...

Identifying candidate driver genes by integrative ovarian cancer genomics data

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Lu, Xinguo; Lu, Jibo

2017-08-01

Integrative analysis of molecular mechanics underlying cancer can distinguish interactions that cannot be revealed based on one kind of data for the appropriate diagnosis and treatment of cancer patients. Tumor samples exhibit heterogeneity in omics data, such as somatic mutations, Copy Number Variations CNVs), gene expression profiles and so on. In this paper we combined gene co-expression modules and mutation modulators separately in tumor patients to obtain the candidate driver genes for resistant and sensitive tumor from the heterogeneous data. The final list of modulators identified are well known in biological processes associated with ovarian cancer, such as CCL17, CACTIN, CCL16, CCL22, APOB, KDF1, CCL11, HNF1B, LRG1, MED1 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

Evaluation of candidate stromal epithelial cross-talk genes identifies association between risk of serous ovarian cancer and TERT, a cancer susceptibility "hot-spot"

DEFF Research Database (Denmark)

Johnatty, Sharon E; Beesley, Jonathan; Chen, Xiaoqing

2010-01-01

We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes in...

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

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Hua-Sheng Chiu

2018-04-01

Full Text Available Summary: Long noncoding RNAs (lncRNAs are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor and TUG1 and WT1-AS (inferred onco-lncRNAs dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. : Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context. Keywords: lncRNA, regulation, modulation, cancer gene, pan-cancer, noncoding RNA, microRNA, RNA-binding proteins, interactome

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets.

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Tiys, Evgeny S; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2018-02-09

Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of

Genetic variation in selenoprotein genes, lifestyle, and risk of colon and rectal cancer.

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Martha L Slattery

Full Text Available BACKGROUND: Associations between selenium and cancer have directed attention to role of selenoproteins in the carcinogenic process. METHODS: We used data from two population-based case-control studies of colon (n = 1555 cases, 1956 controls and rectal (n = 754 cases, 959 controls cancer. We evaluated the association between genetic variation in TXNRD1, TXNRD2, TXNRD3, C11orf31 (SelH, SelW, SelN1, SelS, SepX, and SeP15 with colorectal cancer risk. RESULTS: After adjustment for multiple comparisons, several associations were observed. Two SNPs in TXNRD3 were associated with rectal cancer (rs11718498 dominant OR 1.42 95% CI 1.16,1.74 pACT 0.0036 and rs9637365 recessive 0.70 95% CI 0.55,0.90 pACT 0.0208. Four SNPs in SepN1 were associated with rectal cancer (rs11247735 recessive OR 1.30 95% CI 1.04,1.63 pACT 0.0410; rs2072749 GGvsAA OR 0.53 95% CI 0.36,0.80 pACT 0.0159; rs4659382 recessive OR 0.58 95% CI 0.39,0.86 pACT 0.0247; rs718391 dominant OR 0.76 95% CI 0.62,0.94 pACT 0.0300. Interaction between these genes and exposures that could influence these genes showed numerous significant associations after adjustment for multiple comparisons. Two SNPs in TXNRD1 and four SNPs in TXNRD2 interacted with aspirin/NSAID to influence colon cancer; one SNP in TXNRD1, two SNPs in TXNRD2, and one SNP in TXNRD3 interacted with aspirin/NSAIDs to influence rectal cancer. Five SNPs in TXNRD2 and one in SelS, SeP15, and SelW1 interacted with estrogen to modify colon cancer risk; one SNP in SelW1 interacted with estrogen to alter rectal cancer risk. Several SNPs in this candidate pathway influenced survival after diagnosis with colon cancer (SeP15 and SepX1 increased HRR and rectal cancer (SepX1 increased HRR. CONCLUSIONS: Findings support an association between selenoprotein genes and colon and rectal cancer development and survival after diagnosis. Given the interactions observed, it is likely that the impact of cancer susceptibility from genotype is

Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

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Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

2015-01-01

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

xSyn: A Software Tool for Identifying Sophisticated 3-Way Interactions From Cancer Expression Data

Directory of Open Access Journals (Sweden)

Baishali Bandyopadhyay

2017-08-01

Full Text Available Background: Constructing gene co-expression networks from cancer expression data is important for investigating the genetic mechanisms underlying cancer. However, correlation coefficients or linear regression models are not able to model sophisticated relationships among gene expression profiles. Here, we address the 3-way interaction that 2 genes’ expression levels are clustered in different space locations under the control of a third gene’s expression levels. Results: We present xSyn, a software tool for identifying such 3-way interactions from cancer gene expression data based on an optimization procedure involving the usage of UPGMA (Unweighted Pair Group Method with Arithmetic Mean and synergy. The effectiveness is demonstrated by application to 2 real gene expression data sets. Conclusions: xSyn is a useful tool for decoding the complex relationships among gene expression profiles. xSyn is available at http://www.bdxconsult.com/xSyn.html .

Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis.

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Chu, Xin-Yi; Jiang, Ling-Han; Zhou, Xiong-Hui; Cui, Ze-Jia; Zhang, Hong-Yu

2017-07-14

The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information.

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.

Science.gov (United States)

Doungpan, Narumol; Engchuan, Worrawat; Chan, Jonathan H; Meechai, Asawin

2016-12-05

Gene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results. The two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three

A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype

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Wallace Helen M

2006-10-01

Full Text Available Abstract Background The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis, gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins. Method A new model for gene-gene and gene-environment interactions is developed that abandons the assumptions of the classical twin study, including Fisher's (1918 assumption that genes act as risk factors for common traits in a manner necessarily dominated by an additive polygenic term. Provided there are no confounders, the model can be used to implement a top-down approach to quantifying the potential utility of genetic prediction and prevention, using twin, family and environmental data. The results describe a solution space for each disease or trait, which may or may not include the classical twin study result. Each point in the solution space corresponds to a different model of genotypic risk and gene-environment interaction. Conclusion The results show that the potential for reducing the incidence of common diseases using environmental interventions targeted by genotype may be limited, except in special cases. The model also confirms that the importance of an individual's genotype in determining their risk of complex diseases tends to be exaggerated by the classical twin studies method, owing to the 'equal environments' assumption and the assumption of no gene-environment interaction. In addition, if phenotypes are genetically robust, because of epistasis

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

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Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

2014-01-01

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. 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. 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.

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.

Intracellular delivery of potential therapeutic genes: prospects in cancer gene therapy.

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Bakhtiar, Athirah; Sayyad, Mustak; Rosli, Rozita; Maruyama, Atsushi; Chowdhury, Ezharul H

2014-01-01

Conventional therapies for malignant cancer such as chemotherapy and radiotherapy are associated with poor survival rates owing to the development of cellular resistance to cancer drugs and the lack of targetability, resulting in unwanted adverse effects on healthy cells and necessitating the lowering of therapeutic dose with consequential lower efficacy of the treatment. Gene therapy employing different types of viral and non-viral carriers to transport gene(s) of interest and facilitating production of the desirable therapeutic protein(s) has tremendous prospects in cancer treatments due to the high-level of specificity in therapeutic action of the expressed protein(s) with diminished off-target effects, although cancer cell-specific delivery of transgene(s) still poses some challenges to be addressed. Depending on the potential therapeutic target genes, cancer gene therapy could be categorized into tumor suppressor gene replacement therapy, immune gene therapy and enzyme- or prodrug-based therapy. This review would shed light on the current progress of delivery of potentially therapeutic genes into various cancer cells in vitro and animal models utilizing a variety of viral and non-viral vectors.

Duplicability of self-interacting human genes.

LENUS (Irish Health Repository)

Pérez-Bercoff, Asa

2010-01-01

BACKGROUND: There is increasing interest in the evolution of protein-protein interactions because this should ultimately be informative of the patterns of evolution of new protein functions within the cell. One model proposes that the evolution of new protein-protein interactions and protein complexes proceeds through the duplication of self-interacting genes. This model is supported by data from yeast. We examined the relationship between gene duplication and self-interaction in the human genome. RESULTS: We investigated the patterns of self-interaction and duplication among 34808 interactions encoded by 8881 human genes, and show that self-interacting proteins are encoded by genes with higher duplicability than genes whose proteins lack this type of interaction. We show that this result is robust against the system used to define duplicate genes. Finally we compared the presence of self-interactions amongst proteins whose genes have duplicated either through whole-genome duplication (WGD) or small-scale duplication (SSD), and show that the former tend to have more interactions in general. After controlling for age differences between the two sets of duplicates this result can be explained by the time since the gene duplication. CONCLUSIONS: Genes encoding self-interacting proteins tend to have higher duplicability than proteins lacking self-interactions. Moreover these duplicate genes have more often arisen through whole-genome rather than small-scale duplication. Finally, self-interacting WGD genes tend to have more interaction partners in general in the PIN, which can be explained by their overall greater age. This work adds to our growing knowledge of the importance of contextual factors in gene duplicability.

Towards prostate cancer gene therapy: Development of a chlorotoxin-targeted nanovector for toxic (melittin) gene delivery.

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Tarokh, Zahra; Naderi-Manesh, Hossein; Nazari, Mahboobeh

2017-03-01

Prostate cancer is the second leading cause of death due to cancer in men. Owing to shortcomings in the current treatments, other therapies are being considered. Toxic gene delivery is one of the most effective methods for cancer therapy. Cationic polymers are able to form stable nanoparticles via interaction with nucleic acids electrostatically. Branched polyethylenimine that contains amine groups has notable buffering capacity and the ability to escape from endosome through the proton sponge effect. However, the cytotoxicity of this polymer is high, and modification is one of the applicable strategies to overcome this problem. In this study, PEI was targeted with chlorotoxin (CTX) via N-succinimidyl 3-(2-pyridyldithio) propionate (SPDP) cross-linker. CTX can bind specifically to matrix metalloproteinase-2 that is overexpressed in certain cancers. Melittin as the major component of bee venom has been reported to have anti-cancer activity. This was thus selected to deliver to PC3 cell line. Flow cytometry analysis revealed that transfection efficiency of targeted nanoparticles is significantly higher compared to non-targeted nanoparticles. Targeted nanoparticles carrying the melittin gene also decreased cell viability of PC3 cells significantly while no toxic effects were observed on NIH3T3 cell line. Therefore, CTX-targeted nanoparticles carrying the melittin gene could serve as an appropriate gene delivery system for prostate and other MMP-2 positive cancer cells. Copyright © 2016 Elsevier B.V. All rights reserved.

DDPC: Dragon database of genes associated with prostate cancer

KAUST Repository

Maqungo, Monique

2010-09-29

Prostate cancer (PC) is one of the most commonly diagnosed cancers in men. PC is relatively difficult to diagnose due to a lack of clear early symptoms. Extensive research of PC has led to the availability of a large amount of data on PC. Several hundred genes are implicated in different stages of PC, which may help in developing diagnostic methods or even cures. In spite of this accumulated information, effective diagnostics and treatments remain evasive. We have developed Dragon Database of Genes associated with Prostate Cancer (DDPC) as an integrated knowledgebase of genes experimentally verified as implicated in PC. DDPC is distinctive from other databases in that (i) it provides pre-compiled biomedical text-mining information on PC, which otherwise require tedious computational analyses, (ii) it integrates data on molecular interactions, pathways, gene ontologies, gene regulation at molecular level, predicted transcription factor binding sites on promoters of PC implicated genes and transcription factors that correspond to these binding sites and (iii) it contains DrugBank data on drugs associated with PC. We believe this resource will serve as a source of useful information for research on PC. DDPC is freely accessible for academic and non-profit users via http://apps.sanbi.ac.za/ddpc/ and http://cbrc .kaust.edu.sa/ddpc/. The Author(s) 2010.

Cancer suicide gene therapy: a patent review.

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Navarro, Saúl Abenhamar; Carrillo, Esmeralda; Griñán-Lisón, Carmen; Martín, Ana; Perán, Macarena; Marchal, Juan Antonio; Boulaiz, Houria

2016-09-01

Cancer is considered the second leading cause of death worldwide despite the progress made in early detection and advances in classical therapies. Advancing in the fight against cancer requires the development of novel strategies, and the suicide gene transfer to tumor cells is providing new possibilities for cancer therapy. In this manuscript, authors present an overview of suicide gene systems and the latest innovations done to enhance cancer suicide gene therapy strategies by i) improving vectors for targeted gene delivery using tissue specific promoter and receptors; ii) modification of the tropism; and iii) combining suicide genes and/or classical therapies for cancer. Finally, the authors highlight the main challenges to be addressed in the future. Even if many efforts are needed for suicide gene therapy to be a real alternative for cancer treatment, we believe that the significant progress made in the knowledge of cancer biology and characterization of cancer stem cells accompanied by the development of novel targeted vectors will enhance the effectiveness of this type of therapeutic strategy. Moreover, combined with current treatments, suicide gene therapy will improve the clinical outcome of patients with cancer in the future.

The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice.

Science.gov (United States)

Abbott, Kenneth L; Nyre, Erik T; Abrahante, Juan; Ho, Yen-Yi; Isaksson Vogel, Rachel; Starr, Timothy K

2015-01-01

Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

A genetic ensemble approach for gene-gene interaction identification

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Ho Joshua WK

2010-10-01

Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

A Multiple Interaction Analysis Reveals ADRB3 as a Potential Candidate for Gallbladder Cancer Predisposition via a Complex Interaction with Other Candidate Gene Variations

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Rajani Rai

2015-11-01

Full Text Available Gallbladder cancer is the most common and a highly aggressive biliary tract malignancy with a dismal outcome. The pathogenesis of the disease is multifactorial, comprising the combined effect of multiple genetic variations of mild consequence along with numerous dietary and environmental risk factors. Previously, we demonstrated the association of several candidate gene variations with GBC risk. In this study, we aimed to identify the combination of gene variants and their possible interactions contributing towards genetic susceptibility of GBC. Here, we performed Multifactor-Dimensionality Reduction (MDR and Classification and Regression Tree Analysis (CRT to investigate the gene–gene interactions and the combined effect of 14 SNPs in nine genes (DR4 (rs20576, rs6557634; FAS (rs2234767; FASL (rs763110; DCC (rs2229080, rs4078288, rs7504990, rs714; PSCA (rs2294008, rs2978974; ADRA2A (rs1801253; ADRB1 (rs1800544; ADRB3 (rs4994; CYP17 (rs2486758 involved in various signaling pathways. Genotyping was accomplished by PCR-RFLP or Taqman allelic discrimination assays. SPSS software version 16.0 and MDR software version 2.0 were used for all the statistical analysis. Single locus investigation demonstrated significant association of DR4 (rs20576, rs6557634, DCC (rs714, rs2229080, rs4078288 and ADRB3 (rs4994 polymorphisms with GBC risk. MDR analysis revealed ADRB3 (rs4994 to be crucial candidate in GBC susceptibility that may act either alone (p < 0.0001, CVC = 10/10 or in combination with DCC (rs714 and rs2229080, p < 0.0001, CVC = 9/10. Our CRT results are in agreement with the above findings. Further, in-silico results of studied SNPs advocated their role in splicing, transcriptional and/or protein coding regulation. Overall, our result suggested complex interactions amongst the studied SNPs and ADRB3 rs4994 as candidate influencing GBC susceptibility.

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium

Directory of Open Access Journals (Sweden)

Dhir Rajiv

2004-08-01

Full Text Available Abstract Background Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations. Results Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using

Progress in Gene Therapy for Prostate Cancer

International Nuclear Information System (INIS)

Ahmed, Kamran A.; Davis, Brian J.; Wilson, Torrence M.; Wiseman, Gregory A.; Federspiel, Mark J.; Morris, John C.

2012-01-01

Gene therapy has held promise to correct various disease processes. Prostate cancer represents the second leading cause of cancer death in American men. A number of clinical trials involving gene therapy for the treatment of prostate cancer have been reported. The ability to efficiently transduce tumors with effective levels of therapeutic genes has been identified as a fundamental barrier to effective cancer gene therapy. The approach utilizing gene therapy in prostate cancer patients at our institution attempts to address this deficiency. The sodium-iodide symporter (NIS) is responsible for the ability of the thyroid gland to transport and concentrate iodide. The characteristics of the NIS gene suggest that it could represent an ideal therapeutic gene for cancer therapy. Published results from Mayo Clinic researchers have indicated several important successes with the use of the NIS gene and prostate gene therapy. Studies have demonstrated that transfer of the human NIS gene into prostate cancer using adenovirus vectors in vitro and in vivo results in efficient uptake of radioactive iodine and significant tumor growth delay with prolongation of survival. Preclinical successes have culminated in the opening of a phase I trial for patients with advanced prostate disease which is currently accruing patients. Further study will reveal the clinical promise of NIS gene therapy in the treatment of prostate as well as other malignancies.

Progress in Gene Therapy for Prostate Cancer

Energy Technology Data Exchange (ETDEWEB)

Ahmed, Kamran A.; Davis, Brian J. [Department of Radiation Oncology, Mayo Clinic, Rochester, MN (United States); Wilson, Torrence M. [Department of Urology, Mayo Clinic, Rochester, MN (United States); Wiseman, Gregory A. [Division of Nuclear Medicine, Mayo Clinic, Rochester, MN (United States); Federspiel, Mark J. [Department of Molecular Medicine, Mayo Clinic, Rochester, MN (United States); Morris, John C., E-mail: davis.brian@mayo.edu [Division of Endocrinology, Mayo Clinic, Rochester, MN (United States)

2012-11-19

Gene therapy has held promise to correct various disease processes. Prostate cancer represents the second leading cause of cancer death in American men. A number of clinical trials involving gene therapy for the treatment of prostate cancer have been reported. The ability to efficiently transduce tumors with effective levels of therapeutic genes has been identified as a fundamental barrier to effective cancer gene therapy. The approach utilizing gene therapy in prostate cancer patients at our institution attempts to address this deficiency. The sodium-iodide symporter (NIS) is responsible for the ability of the thyroid gland to transport and concentrate iodide. The characteristics of the NIS gene suggest that it could represent an ideal therapeutic gene for cancer therapy. Published results from Mayo Clinic researchers have indicated several important successes with the use of the NIS gene and prostate gene therapy. Studies have demonstrated that transfer of the human NIS gene into prostate cancer using adenovirus vectors in vitro and in vivo results in efficient uptake of radioactive iodine and significant tumor growth delay with prolongation of survival. Preclinical successes have culminated in the opening of a phase I trial for patients with advanced prostate disease which is currently accruing patients. Further study will reveal the clinical promise of NIS gene therapy in the treatment of prostate as well as other malignancies.

Polymorphisms in ATP-binding cassette transporter genes and interaction with diet and life style factors in relation to colorectal cancer in a Danish prospective case-cohort study

DEFF Research Database (Denmark)

Kopp, Tine Iskov; Andersen, Vibeke; Tjonneland, Anne

2015-01-01

to assess whether polymorphisms in ABCB1, ABCC2 and ABCG2 were associated with risk of colorectal cancer (CRC) and to investigate gene-environment (dietary factors, smoking and use of non-steroidal anti-inflammatory drugs) and gene-gene interactions between previously studied polymorphisms in IL1B and IL10......The ATP-binding cassette (ABC) transporter family transports various molecules across the enterocytes in the gut protecting the intestine against potentially harmful substances. Moreover, ABC transporters are involved in mucosal immune defence through interaction with cytokines. The study aimed...... of the polymorphisms were associated with CRC, but ABCB1 and ABCG2 haplotypes were associated with risk of CRC. ABCB1/rs1045642 interacted with intake of cereals and fiber (p-Value for interaction (Pint) = 0.001 and 0.01, respectively). In a three-way analysis, both ABCB1/rs1045642 and ABCG2/rs2231137 in combination...

Research progress in machine learning methods for gene-gene interaction detection.

Science.gov (United States)

Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis.

Science.gov (United States)

Zhao, Hongying; Yuan, Huating; Hu, Jing; Xu, Chaohan; Liao, Gaoming; Yin, Wenkang; Xu, Liwen; Wang, Li; Zhang, Xinxin; Shi, Aiai; Li, Jing; Xiao, Yun

2017-12-12

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as 'cell adhesion' and 'cell migration'. Furthermore, they are most significantly correlated with 'tissue invasion and metastasis', a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.

Gene panel testing for inherited cancer risk.

Science.gov (United States)

Hall, Michael J; Forman, Andrea D; Pilarski, Robert; Wiesner, Georgia; Giri, Veda N

2014-09-01

Next-generation sequencing technologies have ushered in the capability to assess multiple genes in parallel for genetic alterations that may contribute to inherited risk for cancers in families. Thus, gene panel testing is now an option in the setting of genetic counseling and testing for cancer risk. This article describes the many gene panel testing options clinically available to assess inherited cancer susceptibility, the potential advantages and challenges associated with various types of panels, clinical scenarios in which gene panels may be particularly useful in cancer risk assessment, and testing and counseling considerations. Given the potential issues for patients and their families, gene panel testing for inherited cancer risk is recommended to be offered in conjunction or consultation with an experienced cancer genetic specialist, such as a certified genetic counselor or geneticist, as an integral part of the testing process. Copyright © 2014 by the National Comprehensive Cancer Network.

Genetic polymorphisms in CYP1A1, GSTM1, GSTP1 and GSTT1 metabolic genes and risk of lung cancer in Asturias

International Nuclear Information System (INIS)

López-Cima, M Felicitas; Álvarez-Avellón, Sara M; Pascual, Teresa; Fernández-Somoano, Ana; Tardón, Adonina

2012-01-01

Metabolic genes have been associated with the function of metabolizing and detoxifying environmental carcinogens. Polymorphisms present in these genes could lead to changes in their metabolizing and detoxifying ability and thus may contribute to individual susceptibility to different types of cancer. We investigated if the individual and/or combined modifying effects of the CYP1A1 MspI T6235C, GSTM1 present/null, GSTT1 present/null and GSTP1 Ile105Val polymorphisms are related to the risk of developing lung cancer in relation to tobacco consumption and occupation in Asturias, Northern Spain. A hospital-based case–control study (CAPUA Study) was designed including 789 lung cancer patients and 789 control subjects matched in ethnicity, age, sex, and hospital. Genotypes were determined by PCR or PCR-RFLP. Individual and combination effects were analysed using an unconditional logistic regression adjusting for age, pack-years, family history of any cancer and occupation. No statistically significant main effects were observed for the carcinogen metabolism genes in relation to lung cancer risk. In addition, the analysis did not reveal any significant gene-gene, gene-tobacco smoking or gene-occupational exposure interactions relative to lung cancer susceptibility. Lastly, no significant gene-gene combination effects were observed. These results suggest that genetic polymorphisms in the CYP1A1, GSTM1, GSTT1 and GSTP1 metabolic genes were not significantly associated with lung cancer risk in the current study. The results of the analysis of gene-gene interactions of CYP1A1 MspI T6235C, GSTM1 present/null, GSTT1 present/null and GSTP1 Ile105Val polymorphisms in lung cancer risk indicate that these genes do not interact in lung cancer development

ETS Gene Fusions as Predictive Biomarkers of Resistance to Radiation Therapy for Prostate Cancer

Science.gov (United States)

2016-05-01

phenotype  in   preclinical  models  of  prostate  cancer,  2)  to  explore  the  mechanism  of  interaction  between   ERG  (the  predominant  ETS...established  this  axis  as  a  potential  therapeutic   target.         15. SUBJECT  TERMS Prostate cancer, ETS gene fusions, ERG , radiation resistance, DNA...interaction  between   ERG   (the   predominant   ETS   gene   fusion   product)   and   the   DNA   repair   protein   DNA-­PK,   and   3)   to

Inflammatory Gene Polymorphisms in Lung Cancer Susceptibility.

Science.gov (United States)

Eaton, Keith D; Romine, Perrin E; Goodman, Gary E; Thornquist, Mark D; Barnett, Matt J; Petersdorf, Effie W

2018-05-01

Chronic inflammation has been implicated in carcinogenesis, with increasing evidence of its role in lung cancer. We aimed to evaluate the role of genetic polymorphisms in inflammation-related genes in the risk for development of lung cancer. A nested case-control study design was used, and 625 cases and 625 well-matched controls were selected from participants in the β-Carotene and Retinol Efficacy Trial, which is a large, prospective lung cancer chemoprevention trial. The association between lung cancer incidence and survival and 23 polymorphisms descriptive of 11 inflammation-related genes (interferon gamma gene [IFNG], interleukin 10 gene [IL10], interleukin 1 alpha gene [IL1A], interleukin 1 beta gene [IL1B], interleukin 2 gene [IL2], interleukin 4 receptor gene [IL4R], interleukin 4 gene [IL4], interleukin 6 gene [IL6], prostaglandin-endoperoxide synthase 2 gene [PTGS2] (also known as COX2), transforming growth factor beta 1 gene [TGFB1], and tumor necrosis factor alpha gene [TNFA]) was evaluated. Of the 23 polymorphisms, two were associated with risk for lung cancer. Compared with individuals with the wild-type (CC) variant, individuals carrying the minor allele variants of the IL-1β-511C>T promoter polymorphism (rs16944) (CT and TT) had decreased odds of lung cancer (OR = 0.74, [95% confidence interval (CI): 0.58-0.94] and OR = 0.71 [95% CI: 0.50-1.01], respectively, p = 0.03). Similar results were observed for the IL-1β-1464 C>G promoter polymorphism (rs1143623), with presence of the minor variants CG and CC having decreased odds of lung cancer (OR = 0.75 [95% CI: 0.59-0.95] and OR = 0.69 [95% CI: 0.46-1.03], respectively, p = 0.03). Survival was not influenced by genotype. This study provides further evidence that IL1B promoter polymorphisms may modulate the risk for development of lung cancer. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

Science.gov (United States)

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

Genetic polymorphisms of the GNRH1 and GNRHR genes and risk of breast cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3

Directory of Open Access Journals (Sweden)

Lund Eiliv

2009-07-01

Full Text Available Abstract Background Gonadotropin releasing hormone (GNRH1 triggers the release of follicle stimulating hormone and luteinizing hormone from the pituitary. Genetic variants in the gene encoding GNRH1 or its receptor may influence breast cancer risk by modulating production of ovarian steroid hormones. We studied the association between breast cancer risk and polymorphisms in genes that code for GNRH1 and its receptor (GNRHR in the large National Cancer Institute Breast and Prostate Cancer Cohort Consortium (NCI-BPC3. Methods We sequenced exons of GNRH1 and GNRHR in 95 invasive breast cancer cases. Resulting single nucleotide polymorphisms (SNPs were genotyped and used to identify haplotype-tagging SNPs (htSNPS in a panel of 349 healthy women. The htSNPs were genotyped in 5,603 invasive breast cancer cases and 7,480 controls from the Cancer Prevention Study-II (CPS-II, European Prospective Investigation on Cancer and Nutrition (EPIC, Multiethnic Cohort (MEC, Nurses' Health Study (NHS, and Women's Health Study (WHS. Circulating levels of sex steroids (androstenedione, estradiol, estrone and testosterone were also measured in 4713 study subjects. Results Breast cancer risk was not associated with any polymorphism or haplotype in the GNRH1 and GNRHR genes, nor were there any statistically significant interactions with known breast cancer risk factors. Polymorphisms in these two genes were not strongly associated with circulating hormone levels. Conclusion Common variants of the GNRH1 and GNRHR genes are not associated with risk of invasive breast cancer in Caucasians.

Genetic polymorphisms of the GNRH1 and GNRHR genes and risk of breast cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3)

International Nuclear Information System (INIS)

Canzian, Federico; Calle, Eugenia E; Chanock, Stephen; Clavel-Chapelon, Francoise; Dossus, Laure; Feigelson, Heather Spencer; Haiman, Christopher A; Hankinson, Susan E; Hoover, Robert; Hunter, David J; Isaacs, Claudine; Kaaks, Rudolf; Lenner, Per; Lund, Eiliv; Overvad, Kim; Palli, Domenico; Pearce, Celeste Leigh; Quiros, Jose R; Riboli, Elio; Stram, Daniel O; Thomas, Gilles; Thun, Michael J; Cox, David G; Trichopoulos, Dimitrios; Gils, Carla H van; Ziegler, Regina G; Henderson, Katherine D; Henderson, Brian E; Berg, Christine; Bingham, Sheila; Boeing, Heiner; Buring, Julie

2009-01-01

Gonadotropin releasing hormone (GNRH1) triggers the release of follicle stimulating hormone and luteinizing hormone from the pituitary. Genetic variants in the gene encoding GNRH1 or its receptor may influence breast cancer risk by modulating production of ovarian steroid hormones. We studied the association between breast cancer risk and polymorphisms in genes that code for GNRH1 and its receptor (GNRHR) in the large National Cancer Institute Breast and Prostate Cancer Cohort Consortium (NCI-BPC3). We sequenced exons of GNRH1 and GNRHR in 95 invasive breast cancer cases. Resulting single nucleotide polymorphisms (SNPs) were genotyped and used to identify haplotype-tagging SNPs (htSNPS) in a panel of 349 healthy women. The htSNPs were genotyped in 5,603 invasive breast cancer cases and 7,480 controls from the Cancer Prevention Study-II (CPS-II), European Prospective Investigation on Cancer and Nutrition (EPIC), Multiethnic Cohort (MEC), Nurses' Health Study (NHS), and Women's Health Study (WHS). Circulating levels of sex steroids (androstenedione, estradiol, estrone and testosterone) were also measured in 4713 study subjects. Breast cancer risk was not associated with any polymorphism or haplotype in the GNRH1 and GNRHR genes, nor were there any statistically significant interactions with known breast cancer risk factors. Polymorphisms in these two genes were not strongly associated with circulating hormone levels. Common variants of the GNRH1 and GNRHR genes are not associated with risk of invasive breast cancer in Caucasians

IGF-Regulated Genes in Prostate Cancer

National Research Council Canada - National Science Library

Roberts, Charles

2003-01-01

We hypothesized that genes that are differentially expressed as a result of the decreased IGF-I receptor gene expression seen in metastatic prostate cancer contribute to prostate cancer progression...

IGF-Regulated Genes in Prostate Cancer

National Research Council Canada - National Science Library

Roberts, Charles T., Jr

2005-01-01

We hypothesized that genes that are differentially expressed as a result of the decreased IGF-I receptor gene expression seen in metastatic prostate cancer contribute to prostate cancer progression...

Expression of circadian clock genes and proteins in urothelial cancer is related to cancer-associated genes

International Nuclear Information System (INIS)

Litlekalsoy, Jorunn; Rostad, Kari; Kalland, Karl-Henning; Hostmark, Jens G.; Laerum, Ole Didrik

2016-01-01

The purpose of this study was to evaluate invasive and metastatic potential of urothelial cancer by investigating differential expression of various clock genes/proteins participating in the 24 h circadian rhythms and to compare these gene expressions with transcription of other cancer-associated genes. Twenty seven paired samples of tumour and benign tissue collected from patients who underwent cystectomy were analysed and compared to 15 samples of normal bladder tissue taken from patients who underwent cystoscopy for benign prostate hyperplasia (unrelated donors). Immunohistochemical analyses were made for clock and clock-related proteins. In addition, the gene-expression levels of 22 genes (clock genes, casein kinases, oncogenes, tumour suppressor genes and cytokeratins) were analysed by real-time quantitative PCR (qPCR). Considerable up- or down-regulation and altered cellular distribution of different clock proteins, a reduction of casein kinase1A1 (CSNK1A1) and increase of casein kinase alpha 1 E (CSNK1E) were found. The pattern was significantly correlated with simultaneous up-regulation of stimulatory tumour markers, and a down-regulation of several suppressor genes. The pattern was mainly seen in aneuploid high-grade cancers. Considerable alterations were also found in the neighbouring bladder mucosa. The close correlation between altered expression of various clock genes and common tumour markers in urothelial cancer indicates that disturbed function in the cellular clock work may be an important additional mechanism contributing to cancer progression and malignant behaviour. The online version of this article (doi:10.1186/s12885-016-2580-y) contains supplementary material, which is available to authorized users

Haplotype analysis of common variants in the BRCA1 gene and risk of sporadic breast cancer

International Nuclear Information System (INIS)

Cox, David G; Kraft, Peter; Hankinson, Susan E; Hunter, David J

2005-01-01

Truncation mutations in the BRCA1 gene cause a substantial increase in risk of breast cancer. However, these mutations are rare in the general population and account for little of the overall incidence of sporadic breast cancer. We used whole-gene resequencing data to select haplotype tagging single nucleotide polymorphisms, and examined the association between common haplotypes of BRCA1 and breast cancer in a nested case-control study in the Nurses' Health Study (1323 cases and 1910 controls). One haplotype was associated with a slight increase in risk (odds ratio 1.18, 95% confidence interval 1.02–1.37). A significant interaction (P = 0.05) was seen between this haplotype, positive family history of breast cancer, and breast cancer risk. Although not statistically significant, similar interactions were observed with age at diagnosis and with menopausal status at diagnosis; risk tended to be higher among younger, pre-menopausal women. We have described a haplotype in the BRCA1 gene that was associated with an approximately 20% increase in risk of sporadic breast cancer in the general population. However, the functional variant(s) responsible for the association are unclear

SNP-SNP interactions of three new pri-miRNAs with the target gene PGC and multidimensional analysis of H. pylori in the gastric cancer/atrophic gastritis risk in a Chinese population.

Science.gov (United States)

Xu, Qian; Wu, Ye-Feng; Li, Ying; He, Cai-Yun; Sun, Li-Ping; Liu, Jing-Wei; Yuan, Yuan

2016-04-26

Gastric cancer (GC) is a multistep complex disease involving multiple genes, and gene-gene interactions have a greater effect than a single gene in determining cancer susceptibility. This study aimed to explore the interaction of the let-7e rs8111742, miR-365b rs121224, and miR-4795 rs1002765 single nucleotide polymorphisms (SNPs) with SNPs of the predicted target gene PGC and Helicobacter pylori status in GC and atrophic gastritis (AG) risk. Three miRNA SNPs and seven PGC SNPs were detected in 2448 cases using the Sequenom MassArray platform. Two pairwise combinations of miRNA and PGC SNPs were associated with increased AG risk (let-7e rs8111742 - PGC rs6458238 and miR-4795 rs1002765 - PGC rs9471643). Singly, miR-365b rs121224 and PGC rs6912200 had no effect individually but in combination they demonstrated an epistatic interaction associated with AG risk. Similarly, let-7e rs8111742 and miR-4795 rs1002765 SNPs interacted with H. pylori infection to increase GC risk (rs8111742: Pinteraction = 0.024; rs1002765: Pinteraction = 0.031, respectively). A three-dimensional interaction analysis found miR-4795 rs1002765, PGC rs9471643, and H. pylori infection positively interacted to increase AG risk (Pinteraction = 0.027). Also, let-7e rs8111742, PGC rs6458238, and H. pylori infection positively interacted to increase GC risk (Pinteraction = 0.036). Furthermore, both of these three-dimensional interactions had a dosage-effect correspondence (Ptrend < 0.001) and were verified by MDR. In conclusion, the miRNAs SNPs (let-7e rs8111742 and miR-4795 rs1002765) might have more superior efficiency when combined with PGC SNPs and/or H. pylori for GC or AG risk than a single SNP on its own.

Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis.

Directory of Open Access Journals (Sweden)

Nigel P S Crawford

2007-11-01

Full Text Available A novel candidate metastasis modifier, ribosomal RNA processing 1 homolog B (Rrp1b, was identified through two independent approaches. First, yeast two-hybrid, immunoprecipitation, and functional assays demonstrated a physical and functional interaction between Rrp1b and the previous identified metastasis modifier Sipa1. In parallel, using mouse and human metastasis gene expression data it was observed that extracellular matrix (ECM genes are common components of metastasis predictive signatures, suggesting that ECM genes are either important markers or causal factors in metastasis. To investigate the relationship between ECM genes and poor prognosis in breast cancer, expression quantitative trait locus analysis of polyoma middle-T transgene-induced mammary tumor was performed. ECM gene expression was found to be consistently associated with Rrp1b expression. In vitro expression of Rrp1b significantly altered ECM gene expression, tumor growth, and dissemination in metastasis assays. Furthermore, a gene signature induced by ectopic expression of Rrp1b in tumor cells predicted survival in a human breast cancer gene expression dataset. Finally, constitutional polymorphism within RRP1B was found to be significantly associated with tumor progression in two independent breast cancer cohorts. These data suggest that RRP1B may be a novel susceptibility gene for breast cancer progression and metastasis.

A Nonlinear Model for Gene-Based Gene-Environment Interaction

Directory of Open Access Journals (Sweden)

Jian Sa

2016-06-01

Full Text Available A vast amount of literature has confirmed the role of gene-environment (G×E interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Folate intake, alcohol consumption, and the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism: influence on prostate cancer risk and interactions

International Nuclear Information System (INIS)

Kobayashi, Lindsay C.; Limburg, Heather; Miao, Qun; Woolcott, Christy; Bedard, Leanne L.; Massey, Thomas E.; Aronson, Kristan J.

2012-01-01

Purpose: Folate is essential to DNA methylation and synthesis and may have a complex dualistic role in prostate cancer. Alcohol use may increase risk and epigenetic factors may interact with lifestyle exposures. We aimed to characterize the independent and joint effects of folate intake, alcohol consumption, and the MTHFR C677T gene polymorphism on prostate cancer risk, while accounting for intakes of vitamins B 2 , B 6 , B 12 , methionine, total energy, and confounders. Methods: A case-control study was conducted at Kingston General Hospital of 80 incident primary prostate cancer cases and 334 urology clinic controls, all with normal age-specific PSA levels (to exclude latent prostate cancers). Participants completed a questionnaire on folate and alcohol intakes and potential confounders prior to knowledge of diagnosis, eliminating recall bias, and blood was drawn for MTHFR genotyping. Joint effects of exposures were assessed using unconditional logistic regression and significance of multiplicative and additive interactions using general linear models. Results: Folate, vitamins B 2 , B 6 , B 12 , methionine, and the CT and TT genotypes were not associated with prostate cancer risk. The highest tertile of lifetime alcohol consumption was associated with increased risk (OR = 2.08; 95% CI: 1.12–3.86). Consumption of >5 alcoholic drinks per week was associated with increased prostate cancer risk among men with low folate intake (OR = 2.38; 95% CI: 1.01–5.57), and higher risk among those with the CC MTHFR genotype (OR = 4.43; 95% CI: 1.15–17.05). Increased risk was also apparent for average weekly alcohol consumption when accounting for the multiplicative interaction between folate intake and MTHFR C677T genotype (OR = 3.22; 95% CI: 1.36–7.59). Conclusion: Alcohol consumption is associated with increased prostate cancer risk, and this association is stronger among men with low folate intake, with the CC MTHFR genotype, and when accounting for the joint effect

Folate intake, alcohol consumption, and the methylenetetrahydrofolate reductase (MTHFR C677T gene polymorphism: influence on prostate cancer risk and interactions

Directory of Open Access Journals (Sweden)

Lindsay C Kobayashi

2012-08-01

Full Text Available Purpose: Folate is essential to DNA methylation and synthesis and may have a complex dualistic role in prostate cancer. Alcohol use may increase risk and epigenetic factors may interact with lifestyle exposures. We aimed to characterize the independent and joint effects of folate intake, alcohol consumption, and the MTHFR C677T gene polymorphism on prostate cancer risk, while accounting for intakes of vitamins B2, B6, B12, methionine, total energy, and confounders.Methods: A case-control study was conducted at Kingston General Hospital of 80 incident primary prostate cancer cases and 334 urology clinic controls, all with normal age-specific PSA levels (to exclude latent prostate cancers. Participants completed a questionnaire on folate and alcohol intakes and potential confounders prior to knowledge of diagnosis, eliminating recall bias, and blood was drawn for MTHFR genotyping. Joint effects of exposures were assessed using unconditional logistic regression and significance of multiplicative and additive interactions using general linear models.Results: Folate, vitamins B2, B6, B12, methionine, and the CT and TT genotypes were not associated with prostate cancer risk. The highest tertile of lifetime alcohol consumption was associated with increased risk (OR=2.08; 95% CI: 1.12-3.86. Consumption of >5 alcoholic drinks/week was associated with increased prostate cancer risk among men with low folate intake (OR=2.38; 95% CI: 1.01-5.57 and higher risk among those with the CC MTHFR genotype (OR=4.43; 95% CI: 1.15-17.05. Increased risk was also apparent for weekly alcohol consumption when accounting for the multiplicative interaction between folate intake and MTHFR C677T genotype (OR=3.22; 95% CI: 1.36-7.59.Conclusion: Alcohol consumption is associated with increased prostate cancer risk, and this association is stronger among men with low folate intake, with the CC MTHFR genotype, and when accounting for the joint effect of folate intake and MTHFR C

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

Science.gov (United States)

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Methylated genes as new cancer biomarkers.

LENUS (Irish Health Repository)

Duffy, M J

2012-02-01

Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested that measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. Promising DNA methylation biomarkers include the use of methylated GSTP1 for aiding the early diagnosis of prostate cancer, methylated PITX2 for predicting outcome in lymph node-negative breast cancer patients and methylated MGMT in predicting benefit from alkylating agents in patients with glioblastomas. However, prior to clinical utilisation, these findings require validation in prospective clinical studies. Furthermore, assays for measuring gene methylation need to be standardised, simplified and evaluated in external quality assurance programmes. It is concluded that methylated genes have the potential to provide a new generation of cancer biomarkers.

Identification of genes containing expanded purine repeats in the human genome and their apparent protective role against cancer.

Science.gov (United States)

Singh, Himanshu Narayan; Rajeswari, Moganty R

2016-01-01

Purine repeat sequences present in a gene are unique as they have high propensity to form unusual DNA-triple helix structures. Friedreich's ataxia is the only human disease that is well known to be associated with DNA-triplexes formed by purine repeats. The purpose of this study was to recognize the expanded purine repeats (EPRs) in human genome and find their correlation with cancer pathogenesis. We developed "PuRepeatFinder.pl" algorithm to identify non-overlapping EPRs without pyrimidine interruptions in the human genome and customized for searching repeat lengths, n ≥ 200. A total of 1158 EPRs were identified in the genome which followed Wakeby distribution. Two hundred and ninety-six EPRs were found in geneic regions of 282 genes (EPR-genes). Gene clustering of EPR-genes was done based on their cellular function and a large number of EPR-genes were found to be enzymes/enzyme modulators. Meta-analysis of 282 EPR-genes identified only 63 EPR-genes in association with cancer, mostly in breast, lung, and blood cancers. Protein-protein interaction network analysis of all 282 EPR-genes identified proteins including those in cadherins and VEGF. The two observations, that EPRs can induce mutations under malignant conditions and that identification of some EPR-gene products in vital cell signaling-mediated pathways, together suggest the crucial role of EPRs in carcinogenesis. The new link between EPR-genes and their functionally interacting proteins throws a new dimension in the present understanding of cancer pathogenesis and can help in planning therapeutic strategies. Validation of present results using techniques like NGS is required to establish the role of the EPR genes in cancer pathology.

Discovery of cancer common and specific driver gene sets

Science.gov (United States)

2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

Association of genetic markers in the BCL-2 family of apoptosis-related genes with endometrial cancer risk in a Chinese population.

Directory of Open Access Journals (Sweden)

Tsogzolmaa Dorjgochoo

Full Text Available In vitro studies have demonstrated the role of the BCL-2 family of genes in endometrial carcinogenesis. The role of genetic variants in BCL-2 genes and their interactions with non-genetic factors in the development of endometrial cancer has not been investigated in epidemiological studies.We examined the relationship between BCL-2 gene family variants and endometrial cancer risk among 1,028 patients and 1,922 age-matched community controls from Shanghai, China. We also investigated possible interactions between genetic variants and established risk factors (demographic, lifestyle and clinical. Individuals were genotyped for 86 tagging single nucleotide polymorphisms (SNPs in the BCL2, BAX, BAD and BAK1 genes.Significant associations with endometrial cancer risk were found for 9 SNPs in the BCL2 gene (P trend<0.05 for all. For SNPs rs17759659 and rs7243091 (minor allele for both: G, the associations were independent. The odds ratio was 1.27 (95% CI: 1.04-1.53 for women with AG genotype for the SNP rs17759659 and 1.82 (95% CI: 1.21-2.73 for women with the GG genotype for the SNP rs7243091. No interaction between these two SNPs and established non-genetic risk factors of endometrial cancer was noticed.Genetic polymorphisms in the BCL2 gene may be associated with the risk of endometrial cancer in Chinese women.

Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm.

Science.gov (United States)

Lu, Jing; Chen, Lei; Yin, Jun; Huang, Tao; Bi, Yi; Kong, Xiangyin; Zheng, Mingyue; Cai, Yu-Dong

2016-01-01

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

Theranostic Imaging of Cancer Gene Therapy.

Science.gov (United States)

Sekar, Thillai V; Paulmurugan, Ramasamy

2016-01-01

Gene-directed enzyme prodrug therapy (GDEPT) is a promising therapeutic approach for treating cancers of various phenotypes. This strategy is independent of various other chemotherapeutic drugs used for treating cancers where the drugs are mainly designed to target endogenous cellular mechanisms, which are different in various cancer subtypes. In GDEPT an external enzyme, which is different from the cellular proteins, is expressed to convert the injected prodrug in to a toxic metabolite, that normally kill cancer cells express this protein. Theranostic imaging is an approach used to directly monitor the expression of these gene therapy enzymes while evaluating therapeutic effect. We recently developed a dual-GDEPT system where we combined mutant human herpes simplex thymidine kinase (HSV1sr39TK) and E. coli nitroreductase (NTR) enzyme, to improve therapeutic efficiency of cancer gene therapy by simultaneously injecting two prodrugs at a lower dose. In this approach we use two different prodrugs such as ganciclovir (GCV) and CB1954 to target two different cellular mechanisms to kill cancer cells. The developed dual GDEPT system was highly efficacious than that of either of the system used independently. In this chapter, we describe the complete protocol involved for in vitro and in vivo imaging of therapeutic cancer gene therapy evaluation.

The Key Genes of Chronic Pancreatitis which Bridge Chronic Pancreatitis and Pancreatic Cancer Can be Therapeutic Targets.

Science.gov (United States)

Li, Shuang; Li, Rui; Wang, Heping; Li, Lisha; Li, Huiyu; Li, Yulin

2018-04-01

An important question in systems biology is what role the underlying molecular mechanisms play in disease progression. The relationship between chronic pancreatitis and pancreatic cancer needs further exploration in a system view. We constructed the disease network based on gene expression data and protein-protein interaction. We proposed an approach to discover the underlying core network and molecular factors in the progression of pancreatic diseases, which contain stages of chronic pancreatitis and pancreatic cancer. The chronic pancreatitis and pancreatic cancer core network and key factors were revealed and then verified by gene set enrichment analysis of pathways and diseases. The key factors provide the microenvironment for tumor initiation and the change of gene expression level of key factors bridge chronic pancreatitis and pancreatic cancer. Some new candidate genes need further verification by experiments. Transcriptome profiling-based network analysis reveals the importance of chronic pancreatitis genes and pathways in pancreatic cancer development on a system level by computational method and they can be therapeutic targets.

Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer

Science.gov (United States)

Makondi, Precious Takondwa; Lee, Chia-Hwa; Huang, Chien-Yu; Chu, Chi-Ming; Chang, Yu-Jia

2018-01-01

Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways

Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer.

Directory of Open Access Journals (Sweden)

Precious Takondwa Makondi

Full Text Available Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO database (dataset, GSE86525 was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs. Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID. Protein-protein interaction (PPI networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs; the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A, toll-like receptor 4 (TLR4, CD19 molecule (CD19, breast cancer 1, early onset (BRCA1, platelet-derived growth factor subunit A (PDGFA, and matrix metallopeptidase 1 (MMP1 were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4 revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS. The identified genes and pathways

Methylated genes as new cancer biomarkers

DEFF Research Database (Denmark)

Brunner, Nils; Duffy, M.J; Napieralski, R.

2009-01-01

Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested that meas......Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested...... that measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. Promising DNA methylation biomarkers include the use of methylated GSTP1 for aiding the early diagnosis of prostate cancer, methylated PITX2...... for predicting outcome in lymph node-negative breast cancer patients and methylated MGMT in predicting benefit from alkylating agents in patients with glioblastomas. However, prior to clinical utilisation, these findings require validation in prospective clinical studies. Furthermore, assays for measuring gene...

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression

Directory of Open Access Journals (Sweden)

Mao Yu

2009-07-01

this research, several gene interaction networks inferred could provide clues for the mechanism of prostate cancer progression. Conclusion The SIG method reliably identifies cancer progression correlated gene pairs, and performs well both in gene pair ontology analysis and in pathway enrichment analysis. This method provides an effective means of understanding the molecular mechanism of carcinogenesis by appropriately tracking down the process of cancer progression.

P-MartCancer–Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets

Energy Technology Data Exchange (ETDEWEB)

Webb-Robertson, Bobbie-Jo M.; Bramer, Lisa M.; Jensen, Jeffrey L.; Kobold, Markus A.; Stratton, Kelly G.; White, Amanda M.; Rodland, Karin D.

2017-10-31

P-MartCancer is a new interactive web-based software environment that enables biomedical and biological scientists to perform in-depth analyses of global proteomics data without requiring direct interaction with the data or with statistical software. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access to multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium (CPTAC) at the peptide, gene and protein levels. P-MartCancer is deployed using Azure technologies (http://pmart.labworks.org/cptac.html), the web-service is alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/) and many statistical functions can be utilized directly from an R package available on GitHub (https://github.com/pmartR).

Identification of upstream transcription factors (TFs) for expression signature genes in breast cancer.

Science.gov (United States)

Zang, Hongyan; Li, Ning; Pan, Yuling; Hao, Jingguang

2017-03-01

Breast cancer is a common malignancy among women with a rising incidence. Our intention was to detect transcription factors (TFs) for deeper understanding of the underlying mechanisms of breast cancer. Integrated analysis of gene expression datasets of breast cancer was performed. Then, functional annotation of differentially expressed genes (DEGs) was conducted, including Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Furthermore, TFs were identified and a global transcriptional regulatory network was constructed. Seven publically available GEO datasets were obtained, and a set of 1196 DEGs were identified (460 up-regulated and 736 down-regulated). Functional annotation results showed that cell cycle was the most significantly enriched pathway, which was consistent with the fact that cell cycle is closely related to various tumors. Fifty-three differentially expressed TFs were identified, and the regulatory networks consisted of 817 TF-target interactions between 46 TFs and 602 DEGs in the context of breast cancer. Top 10 TFs covering the most downstream DEGs were SOX10, NFATC2, ZNF354C, ARID3A, BRCA1, FOXO3, GATA3, ZEB1, HOXA5 and EGR1. The transcriptional regulatory networks could enable a better understanding of regulatory mechanisms of breast cancer pathology and provide an opportunity for the development of potential therapy.

Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

Directory of Open Access Journals (Sweden)

Bing Jiang

2017-01-01

Full Text Available Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

Science.gov (United States)

Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

2013-04-15

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

The bystander effect of cancer gene therapy

International Nuclear Information System (INIS)

Lumniczky, K.; Safrany, G.

2008-01-01

Cancer gene therapy is a new, promising therapeutic agent. In the clinic, it should be used in combination with existing modalities, such as tumour irradiation. First, we summarise the most important fields of cancer gene therapy: gene directed enzyme pro-drug therapy; the activation of an anti-tumour immune attack; restoration of the wild type p53 status; the application of new, replication competent and oncolytic viral vectors; tumour specific, as well as radiation- and hypoxia-induced gene expression. Special emphasizes are put on the combined effect of these modalities with local tumour irradiation. Using the available vector systems, only a small portion of the cancer cells will contain the therapeutic genes under therapeutic situations. Bystander cell killing might contribute to the success of various gene therapy protocols. We summarise the evidences that lethal bystander effects may occur during cancer gene therapy. Bystander effects are especially important in the gene directed enzyme pro-drug therapy. There, bystander cell killing might have different routes: cell communication through gap junction intercellular contacts; release of toxic metabolites into the neighbourhood or to larger distances; phagocytosis of apoptotic bodies; and the activation of the immune system. Bystander cell killing can be enhanced by the introduction of gap junction proteins into the cells, by further activating the immune system with immune-stimulatory molecules, or by introducing genes into the cells that help the transfer of cytotoxic genes and / or metabolites into the bystander cells. In conclusion, there should be additional improvements in cancer gene therapy for the more efficient clinical application. (orig.)

High Frequency of Interactions between Lung Cancer Susceptibility Genes in the Mouse : Mapping of Sluc5 to Sluc14

NARCIS (Netherlands)

Fijneman, Remond J.A.; Jansen, Ritsert C.; Valk, Martin A. van der; Demant, Peter

1998-01-01

Although several genes that cause monogenic familial cancer syndromes have been identified, susceptibility to sporadic cancer remains unresolved. Animal experiments have demonstrated multigenic control of tumor susceptibility. Recently, we described four mouse lung cancer susceptibility (Sluc) loci,

Identifying functional cancer-specific miRNA-mRNA interactions in testicular germ cell tumor.

Science.gov (United States)

Sedaghat, Nafiseh; Fathy, Mahmood; Modarressi, Mohammad Hossein; Shojaie, Ali

2016-09-07

Testicular cancer is the most common cancer in men aged between 15 and 35 and more than 90% of testicular neoplasms are originated at germ cells. Recent research has shown the impact of microRNAs (miRNAs) in different types of cancer, including testicular germ cell tumor (TGCT). MicroRNAs are small non-coding RNAs which affect the development and progression of cancer cells by binding to mRNAs and regulating their expressions. The identification of functional miRNA-mRNA interactions in cancers, i.e. those that alter the expression of genes in cancer cells, can help delineate post-regulatory mechanisms and may lead to new treatments to control the progression of cancer. A number of sequence-based methods have been developed to predict miRNA-mRNA interactions based on the complementarity of sequences. While necessary, sequence complementarity is, however, not sufficient for presence of functional interactions. Alternative methods have thus been developed to refine the sequence-based interactions using concurrent expression profiles of miRNAs and mRNAs. This study aims to find functional cancer-specific miRNA-mRNA interactions in TGCT. To this end, the sequence-based predicted interactions are first refined using an ensemble learning method, based on two well-known methods of learning miRNA-mRNA interactions, namely, TaLasso and GenMiR++. Additional functional analyses were then used to identify a subset of interactions to be most likely functional and specific to TGCT. The final list of 13 miRNA-mRNA interactions can be potential targets for identifying TGCT-specific interactions and future laboratory experiments to develop new therapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

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Victor M. Bii

2016-10-01

Full Text Available Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types.

Characterization of HPV and host genome interactions in primary head and neck cancers

Science.gov (United States)

Parfenov, Michael; Pedamallu, Chandra Sekhar; Gehlenborg, Nils; Freeman, Samuel S.; Danilova, Ludmila; Bristow, Christopher A.; Lee, Semin; Hadjipanayis, Angela G.; Ivanova, Elena V.; Wilkerson, Matthew D.; Protopopov, Alexei; Yang, Lixing; Seth, Sahil; Song, Xingzhi; Tang, Jiabin; Ren, Xiaojia; Zhang, Jianhua; Pantazi, Angeliki; Santoso, Netty; Xu, Andrew W.; Mahadeshwar, Harshad; Wheeler, David A.; Haddad, Robert I.; Jung, Joonil; Ojesina, Akinyemi I.; Issaeva, Natalia; Yarbrough, Wendell G.; Hayes, D. Neil; Grandis, Jennifer R.; El-Naggar, Adel K.; Meyerson, Matthew; Park, Peter J.; Chin, Lynda; Seidman, J. G.; Hammerman, Peter S.; Kucherlapati, Raju; Ally, Adrian; Balasundaram, Miruna; Birol, Inanc; Bowlby, Reanne; Butterfield, Yaron S.N.; Carlsen, Rebecca; Cheng, Dean; Chu, Andy; Dhalla, Noreen; Guin, Ranabir; Holt, Robert A.; Jones, Steven J.M.; Lee, Darlene; Li, Haiyan I.; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Robertson, A. Gordon; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Wong, Tina; Protopopov, Alexei; Santoso, Netty; Lee, Semin; Parfenov, Michael; Zhang, Jianhua; Mahadeshwar, Harshad S.; Tang, Jiabin; Ren, Xiaojia; Seth, Sahil; Haseley, Psalm; Zeng, Dong; Yang, Lixing; Xu, Andrew W.; Song, Xingzhi; Pantazi, Angeliki; Bristow, Christopher; Hadjipanayis, Angela; Seidman, Jonathan; Chin, Lynda; Park, Peter J.; Kucherlapati, Raju; Akbani, Rehan; Casasent, Tod; Liu, Wenbin; Lu, Yiling; Mills, Gordon; Motter, Thomas; Weinstein, John; Diao, Lixia; Wang, Jing; Fan, You Hong; Liu, Jinze; Wang, Kai; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Buda, Elizabeth; Hayes, D. Neil; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Kimes, Patrick K.; Marron, J.S.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Parker, Joel S.; Perou, Charles M.; Prins, Jan F.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Singh, Darshan; Soloway, Mathew G.; Tan, Donghui; Veluvolu, Umadevi; Walter, Vonn; Waring, Scot; Wilkerson, Matthew D.; Wu, Junyuan; Zhao, Ni; Cherniack, Andrew D.; Hammerman, Peter S.; Tward, Aaron D.; Pedamallu, Chandra Sekhar; Saksena, Gordon; Jung, Joonil; Ojesina, Akinyemi I.; Carter, Scott L.; Zack, Travis I.; Schumacher, Steven E.; Beroukhim, Rameen; Freeman, Samuel S.; Meyerson, Matthew; Cho, Juok; Chin, Lynda; Getz, Gad; Noble, Michael S.; DiCara, Daniel; Zhang, Hailei; Heiman, David I.; Gehlenborg, Nils; Voet, Doug; Lin, Pei; Frazer, Scott; Stojanov, Petar; Liu, Yingchun; Zou, Lihua; Kim, Jaegil; Lawrence, Michael S.; Sougnez, Carrie; Lichtenstein, Lee; Cibulskis, Kristian; Lander, Eric; Gabriel, Stacey B.; Muzny, Donna; Doddapaneni, HarshaVardhan; Kovar, Christie; Reid, Jeff; Morton, Donna; Han, Yi; Hale, Walker; Chao, Hsu; Chang, Kyle; Drummond, Jennifer A.; Gibbs, Richard A.; Kakkar, Nipun; Wheeler, David; Xi, Liu; Ciriello, Giovanni; Ladanyi, Marc; Lee, William; Ramirez, Ricardo; Sander, Chris; Shen, Ronglai; Sinha, Rileen; Weinhold, Nils; Taylor, Barry S.; Aksoy, B. Arman; Dresdner, Gideon; Gao, Jianjiong; Gross, Benjamin; Jacobsen, Anders; Reva, Boris; Schultz, Nikolaus; Sumer, S. Onur; Sun, Yichao; Chan, Timothy; Morris, Luc; Stuart, Joshua; Benz, Stephen; Ng, Sam; Benz, Christopher; Yau, Christina; Baylin, Stephen B.; Cope, Leslie; Danilova, Ludmila; Herman, James G.; Bootwalla, Moiz; Maglinte, Dennis T.; Laird, Peter W.; Triche, Timothy; Weisenberger, Daniel J.; Van Den Berg, David J.; Agrawal, Nishant; Bishop, Justin; Boutros, Paul C.; Bruce, Jeff P; Byers, Lauren Averett; Califano, Joseph; Carey, Thomas E.; Chen, Zhong; Cheng, Hui; Chiosea, Simion I.; Cohen, Ezra; Diergaarde, Brenda; Egloff, Ann Marie; El-Naggar, Adel K.; Ferris, Robert L.; Frederick, Mitchell J.; Grandis, Jennifer R.; Guo, Yan; Haddad, Robert I.; Hammerman, Peter S.; Harris, Thomas; Hayes, D. Neil; Hui, Angela BY; Lee, J. Jack; Lippman, Scott M.; Liu, Fei-Fei; McHugh, Jonathan B.; Myers, Jeff; Ng, Patrick Kwok Shing; Perez-Ordonez, Bayardo; Pickering, Curtis R.; Prystowsky, Michael; Romkes, Marjorie; Saleh, Anthony D.; Sartor, Maureen A.; Seethala, Raja; Seiwert, Tanguy Y.; Si, Han; Tward, Aaron D.; Van Waes, Carter; Waggott, Daryl M.; Wiznerowicz, Maciej; Yarbrough, Wendell; Zhang, Jiexin; Zuo, Zhixiang; Burnett, Ken; Crain, Daniel; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candance; Shelton, Troy; Sherman, Mark; Yena, Peggy; Black, Aaron D.; Bowen, Jay; Frick, Jessica; Gastier-Foster, Julie M.; Harper, Hollie A.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Baboud, Julien; Jensen, Mark A.; Kahn, Ari B.; Pihl, Todd D.; Pot, David A.; Srinivasan, Deepak; Walton, Jessica S.; Wan, Yunhu; Burton, Robert; Davidsen, Tanja; Demchok, John A.; Eley, Greg; Ferguson, Martin L.; Shaw, Kenna R. Mills; Ozenberger, Bradley A.; Sheth, Margi; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean Claude; Saller, Charles; Tarvin, Katherine; Chen, Chu; Bollag, Roni; Weinberger, Paul; Golusiński, Wojciech; Golusiński, Paweł; Ibbs, Matthiew; Korski, Konstanty; Mackiewicz, Andrzej; Suchorska, Wiktoria; Szybiak, Bartosz; Wiznerowicz, Maciej; Burnett, Ken; Curley, Erin; Gardner, Johanna; Mallery, David; Penny, Robert; Shelton, Troy; Yena, Peggy; Beard, Christina; Mitchell, Colleen; Sandusky, George; Agrawal, Nishant; Ahn, Julie; Bishop, Justin; Califano, Joseph; Khan, Zubair; Bruce, Jeff P; Hui, Angela BY; Irish, Jonathan; Liu, Fei-Fei; Perez-Ordonez, Bayardo; Waldron, John; Boutros, Paul C.; Waggott, Daryl M.; Myers, Jeff; Lippman, Scott M.; Egea, Sophie; Gomez-Fernandez, Carmen; Herbert, Lynn; Bradford, Carol R.; Carey, Thomas E.; Chepeha, Douglas B.; Haddad, Andrea S.; Jones, Tamara R.; Komarck, Christine M.; Malakh, Mayya; McHugh, Jonathan B.; Moyer, Jeffrey S.; Nguyen, Ariane; Peterson, Lisa A.; Prince, Mark E.; Rozek, Laura S.; Sartor, Maureen A.; Taylor, Evan G.; Walline, Heather M.; Wolf, Gregory T.; Boice, Lori; Chera, Bhishamjit S.; Funkhouser, William K.; Gulley, Margaret L.; Hackman, Trevor G.; Hayes, D. Neil; Hayward, Michele C.; Huang, Mei; Rathmell, W. Kimryn; Salazar, Ashley H.; Shockley, William W.; Shores, Carol G.; Thorne, Leigh; Weissler, Mark C.; Wrenn, Sylvia; Zanation, Adam M.; Chiosea, Simion I.; Diergaarde, Brenda; Egloff, Ann Marie; Ferris, Robert L.; Romkes, Marjorie; Seethala, Raja; Brown, Brandee T.; Guo, Yan; Pham, Michelle; Yarbrough, Wendell G.

2014-01-01

Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis. PMID:25313082

Gene therapy for lung cancer.

Science.gov (United States)

Toloza, Eric M; Morse, Michael A; Lyerly, H Kim

2006-09-01

Lung cancer patients suffer a 15% overall survival despite advances in chemotherapy, radiation therapy, and surgery. This unacceptably low survival rate is due to the usual finding of advanced disease at diagnosis. However, multimodality strategies using conventional therapies only minimally improve survival rates even in early stages of lung cancer. Attempts to improve survival in advanced disease using various combinations of platinum-based chemotherapy have demonstrated that no regimen is superior, suggesting a therapeutic plateau and the need for novel, more specific, and less toxic therapeutic strategies. Over the past three decades, the genetic etiology of cancer has been gradually delineated, albeit not yet completely. Understanding the molecular events that occur during the multistep process of bronchogenic carcinogenesis may make these tasks more surmountable. During these same three decades, techniques have been developed which allow transfer of functional genes into mammalian cells. For example, blockade of activated tumor-promoting oncogenes or replacement of inactivated tumor-suppressing or apoptosis-promoting genes can be achieved by gene therapy. This article will discuss the therapeutic implications of these molecular changes associated with bronchogenic carcinomas and will then review the status of gene therapies for treatment of lung cancer. (c) 2006 Wiley-Liss, Inc.

A novel approach to simulate gene-environment interactions in complex diseases

Directory of Open Access Journals (Sweden)

Nicodemi Mario

2010-01-01

Full Text Available Abstract Background Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.. Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS, a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte

Nonviral Delivery Systems For Cancer Gene Therapy: Strategies And Challenges.

Science.gov (United States)

Shim, Gayong; Kim, Dongyoon; Le, Quoc-Viet; Park, Gyu Thae; Kwon, Taekhyun; Oh, Yu-Kyoung

2018-01-19

Gene therapy has been receiving widespread attention due to its unique advantage in regulating the expression of specific target genes. In the field of cancer gene therapy, modulation of gene expression has been shown to decrease oncogenic factors in cancer cells or increase immune responses against cancer. Due to the macromolecular size and highly negative physicochemical features of plasmid DNA, efficient delivery systems are an essential ingredient for successful gene therapy. To date, a variety of nanostructures and materials have been studied as nonviral gene delivery systems. In this review, we will cover nonviral delivery strategies for cancer gene therapy, with a focus on target cancer genes and delivery materials. Moreover, we will address current challenges and perspectives for nonviral delivery-based cancer gene therapeutics. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Deling Wang

2018-03-01

Full Text Available Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS, random forest (RF, and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms.

Science.gov (United States)

Wang, Deling; Li, Jia-Rui; Zhang, Yu-Hang; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2018-03-12

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors.

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Trigos, Anna S; Pearson, Richard B; Papenfuss, Anthony T; Goode, David L

2017-06-13

Tumors of distinct tissues of origin and genetic makeup display common hallmark cellular phenotypes, including sustained proliferation, suppression of cell death, and altered metabolism. These phenotypic commonalities have been proposed to stem from disruption of conserved regulatory mechanisms evolved during the transition to multicellularity to control fundamental cellular processes such as growth and replication. Dating the evolutionary emergence of human genes through phylostratigraphy uncovered close association between gene age and expression level in RNA sequencing data from The Cancer Genome Atlas for seven solid cancers. Genes conserved with unicellular organisms were strongly up-regulated, whereas genes of metazoan origin were primarily inactivated. These patterns were most consistent for processes known to be important in cancer, implicating both selection and active regulation during malignant transformation. The coordinated expression of strongly interacting multicellularity and unicellularity processes was lost in tumors. This separation of unicellular and multicellular functions appeared to be mediated by 12 highly connected genes, marking them as important general drivers of tumorigenesis. Our findings suggest common principles closely tied to the evolutionary history of genes underlie convergent changes at the cellular process level across a range of solid cancers. We propose altered activity of genes at the interfaces between multicellular and unicellular regions of human gene regulatory networks activate primitive transcriptional programs, driving common hallmark features of cancer. Manipulation of cross-talk between biological processes of different evolutionary origins may thus present powerful and broadly applicable treatment strategies for cancer.

Environmental confounding in gene-environment interaction studies.

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Vanderweele, Tyler J; Ko, Yi-An; Mukherjee, Bhramar

2013-07-01

We show that, in the presence of uncontrolled environmental confounding, joint tests for the presence of a main genetic effect and gene-environment interaction will be biased if the genetic and environmental factors are correlated, even if there is no effect of either the genetic factor or the environmental factor on the disease. When environmental confounding is ignored, such tests will in fact reject the joint null of no genetic effect with a probability that tends to 1 as the sample size increases. This problem with the joint test vanishes under gene-environment independence, but it still persists if estimating the gene-environment interaction parameter itself is of interest. Uncontrolled environmental confounding will bias estimates of gene-environment interaction parameters even under gene-environment independence, but it will not do so if the unmeasured confounding variable itself does not interact with the genetic factor. Under gene-environment independence, if the interaction parameter without controlling for the environmental confounder is nonzero, then there is gene-environment interaction either between the genetic factor and the environmental factor of interest or between the genetic factor and the unmeasured environmental confounder. We evaluate several recently proposed joint tests in a simulation study and discuss the implications of these results for the conduct of gene-environment interaction studies.

Genotype x diet interactions in mice predisposed to mammary cancer: II. Tumors and metastasis

DEFF Research Database (Denmark)

Gordon, Ryan R; Hunter, Kent W; Merrill, Michele La

2008-01-01

either a very high-fat or a matched-control-fat diet, and we measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL × diet interaction effects. Here we describe......High dietary fat intake and obesity may increase the risk of susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F2 mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F2 mice...... effects of diet on mammary tumor and metastases phenotypes, mapping of tumor/metastasis modifier genes, and the interaction between dietary fat levels and effects of cancer modifiers. Results demonstrate that animals fed a high-fat diet are not only more likely to experience decreased mammary cancer...

Methylation of the SPARC gene promoter and its clinical implication in pancreatic cancer

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Lv Shunli

2010-03-01

Full Text Available Abstract Background The secreted protein acidic and rich in cysteine (SPARC plays a pivotal role in regulating cell-matrix interactions and tumor angiogenesis, proliferation, and migration. Detection of SPARC gene methylation may be useful as a tumorigenesis marker for early detection of pancreatic cancer. Methods Methylation of the SPARC gene transcriptional regulation region (TRR was detected using bisulfite-specific (BSP PCR-based sequencing analysis in 40 cases of pancreatic cancer and the adjacent normal tissues, 6 chronic pancreatitis tissues, and 6 normal pancreatic tissues. BSP cloning-based sequencing analysis was also performed in selected cases. Clinicopathological data from the cancer patients were collected and analyzed. Results Analysis of SPARC gene TRR methylation showed two hypermethylation wave peak regions: CpG Region 1 (CpG site 1-7 and CpG Region 2 (CpG site 8-12. Pancreatic tissues have shown methylation in both regions with gradual increases from normal, chronic pancreatitis, and adjacent normal tissues to cancerous tissues. However, Methylation of CpG Region 2 was more sensitive than CpG Region 1 in pancreatic tumorigenesis. Furthermore, the methylation level of CpG Region 2 was associated with increased tumor size and exposure to the risk factors (tobacco smoke and alcohol consumption for developing pancreatic cancer. Conclusion Methylation of the SPARC gene, specifically CpG Region 2, may be an early event during pancreatic tumorigenesis and should be further evaluated as a tumorigenesis marker for early detection of pancreatic cancer.

A sparse regulatory network of copy-number driven gene expression reveals putative breast cancer oncogenes.

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Yuan, Yinyin; Curtis, Christina; Caldas, Carlos; Markowetz, Florian

2012-01-01

Copy number aberrations are recognized to be important in cancer as they may localize to regions harboring oncogenes or tumor suppressors. Such genomic alterations mediate phenotypic changes through their impact on expression. Both cis- and transacting alterations are important since they may help to elucidate putative cancer genes. However, amidst numerous passenger genes, trans-effects are less well studied due to the computational difficulty in detecting weak and sparse signals in the data, and yet may influence multiple genes on a global scale. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream transcriptional targets in breast cancer. With respect to goodness of fit on both simulated and real data, the performance of sparse network inference is no worse than other state-of-the-art models but with the advantage of simultaneous feature selection and efficiency. The DNA-RNA interaction network helps to distinguish copy-number driven expression alterations from those that are copy-number independent. Further, our approach yields a quantitative copy-number dependency score, which distinguishes cis- versus trans-effects. When applied to a breast cancer data set, numerous expression profiles were impacted by cis-acting copy-number alterations, including several known oncogenes such as GRB7, ERBB2, and LSM1. Several trans-acting alterations were also identified, impacting genes such as ADAM2 and BAGE, which warrant further investigation. An R package named lol is available from www.markowetzlab.org/software/lol.html.

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

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Keita Mori

2013-01-01

Full Text Available Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

Interaction of insulin-like growth factor-I and insulin resistance-related genetic variants with lifestyle factors on postmenopausal breast cancer risk.

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Jung, Su Yon; Ho, Gloria; Rohan, Thomas; Strickler, Howard; Bea, Jennifer; Papp, Jeanette; Sobel, Eric; Zhang, Zuo-Feng; Crandall, Carolyn

2017-07-01

Genetic variants and traits in metabolic signaling pathways may interact with obesity, physical activity, and exogenous estrogen (E), influencing postmenopausal breast cancer risk, but these inter-related pathways are incompletely understood. We used 75 single-nucleotide polymorphisms (SNPs) in genes related to insulin-like growth factor-I (IGF-I)/insulin resistance (IR) traits and signaling pathways, and data from 1003 postmenopausal women in Women's Health Initiative Observation ancillary studies. Stratifying via obesity and lifestyle modifiers, we assessed the role of IGF-I/IR traits (fasting IGF-I, IGF-binding protein 3, insulin, glucose, and homeostatic model assessment-insulin resistance) in breast cancer risk as a mediator or influencing factor. Seven SNPs in IGF-I and INS genes were associated with breast cancer risk. These associations differed between non-obese/active and obese/inactive women and between exogenous E non-users and users. The mediation effects of IGF-I/IR traits on the relationship between these SNPs and cancer differed between strata, but only roughly 35% of the cancer risk due to the SNPs was mediated by traits. Similarly, carriers of 20 SNPs in PIK3R1, AKT1/2, and MAPK1 genes (signaling pathways-genetic variants) had different associations with breast cancer between strata, and the proportion of the SNP-cancer relationship explained by traits varied 45-50% between the strata. Our findings suggest that IGF-I/IR genetic variants interact with obesity and lifestyle factors, altering cancer risk partially through pathways other than IGF-I/IR traits. Unraveling gene-phenotype-lifestyle interactions will provide data on potential genetic targets in clinical trials for cancer prevention and intervention strategies to reduce breast cancer risk.

Gene therapy for prostate cancer.

LENUS (Irish Health Repository)

Tangney, Mark

2012-01-31

Cancer remains a leading cause of morbidity and mortality. Despite advances in understanding, detection, and treatment, it accounts for almost one-fourth of all deaths per year in Western countries. Prostate cancer is currently the most commonly diagnosed noncutaneous cancer in men in Europe and the United States, accounting for 15% of all cancers in men. As life expectancy of individuals increases, it is expected that there will also be an increase in the incidence and mortality of prostate cancer. Prostate cancer may be inoperable at initial presentation, unresponsive to chemotherapy and radiotherapy, or recur following appropriate treatment. At the time of presentation, patients may already have metastases in their tissues. Preventing tumor recurrence requires systemic therapy; however, current modalities are limited by toxicity or lack of efficacy. For patients with such metastatic cancers, the development of alternative therapies is essential. Gene therapy is a realistic prospect for the treatment of prostate and other cancers, and involves the delivery of genetic information to the patient to facilitate the production of therapeutic proteins. Therapeutics can act directly (eg, by inducing tumor cells to produce cytotoxic agents) or indirectly by upregulating the immune system to efficiently target tumor cells or by destroying the tumor\\'s vasculature. However, technological difficulties must be addressed before an efficient and safe gene medicine is achieved (primarily by developing a means of delivering genes to the target cells or tissue safely and efficiently). A wealth of research has been carried out over the past 20 years, involving various strategies for the treatment of prostate cancer at preclinical and clinical trial levels. The therapeutic efficacy observed with many of these approaches in patients indicates that these treatment modalities will serve as an important component of urological malignancy treatment in the clinic, either in isolation or

A novel test for gene-ancestry interactions in genome-wide association data.

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Joanna L Davies

Full Text Available Genome-wide association study (GWAS data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into "ancestry groups" and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.

Gene-gene interactions and gene polymorphisms of VEGFA and EG-VEGF gene systems in recurrent pregnancy loss.

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Su, Mei-Tsz; Lin, Sheng-Hsiang; Chen, Yi-Chi; Kuo, Pao-Lin

2014-06-01

Both vascular endothelial growth factor A (VEGFA) and endocrine gland-derived vascular endothelial growth factor (EG-VEGF) systems play major roles in angiogenesis. A body of evidence suggests VEGFs regulate critical processes during pregnancy and have been associated with recurrent pregnancy loss (RPL). However, little information is available regarding the interaction of these two major major angiogenesis-related systems in early human pregnancy. This study was conducted to investigate the association of gene polymorphisms and gene-gene interaction among genes in VEGFA and EG-VEGF systems and idiopathic RPL. A total of 98 women with history of idiopathic RPL and 142 controls were included, and 5 functional SNPs selected from VEGFA, KDR, EG-VEGF (PROK1), PROKR1 and PROKR2 were genotyped. We used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions. Ingenuity pathways analysis (IPA) was introduced to explore possible complex interactions. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL (P<0.01). The MDR test revealed that the KDR (Q472H) polymorphism was the best loci to be associated with RPL (P=0.02). IPA revealed EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3 signaling pathways. Two receptor gene polymorphisms [KDR (Q472H) and PROKR2 (V331M)] were significantly associated with idiopathic RPL. EG-VEGF and VEGFA systems shared several canonical signaling pathways that may contribute to gene-gene interactions, including the Akt, IL-8, EGFR, MAPK, SRC, VHL, HIF-1A and STAT3.

Interaction between common breast cancer susceptibility variants, genetic ancestry, and nongenetic risk factors in Hispanic women.

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Fejerman, Laura; Stern, Mariana C; John, Esther M; Torres-Mejía, Gabriela; Hines, Lisa M; Wolff, Roger K; Baumgartner, Kathy B; Giuliano, Anna R; Ziv, Elad; Pérez-Stable, Eliseo J; Slattery, Martha L

2015-11-01

Most genetic variants associated with breast cancer risk have been discovered in women of European ancestry, and only a few genome-wide association studies (GWAS) have been conducted in minority groups. This research disparity persists in post-GWAS gene-environment interaction analyses. We tested the interaction between hormonal and lifestyle risk factors for breast cancer, and ten GWAS-identified SNPs among 2,107 Hispanic women with breast cancer and 2,587 unaffected controls, to gain insight into a previously reported gene by ancestry interaction in this population. We estimated genetic ancestry with a set of 104 ancestry-informative markers selected to discriminate between Indigenous American and European ancestry. We used logistic regression models to evaluate main effects and interactions. We found that the rs13387042-2q35(G/A) SNP was associated with breast cancer risk only among postmenopausal women who never used hormone therapy [per A allele OR: 0.94 (95% confidence intervals, 0.74-1.20), 1.20 (0.94-1.53), and 1.49 (1.28-1.75) for current, former, and never hormone therapy users, respectively, Pinteraction 0.002] and premenopausal women who breastfed >12 months [OR: 1.01 (0.72-1.42), 1.19 (0.98-1.45), and 1.69 (1.26-2.26) for never, 12 months breastfeeding, respectively, Pinteraction 0.014]. The correlation between genetic ancestry, hormone replacement therapy use, and breastfeeding behavior partially explained a previously reported interaction between a breast cancer risk variant and genetic ancestry in Hispanic women. These results highlight the importance of understanding the interplay between genetic ancestry, genetics, and nongenetic risk factors and their contribution to breast cancer risk. ©2015 American Association for Cancer Research.

Structural effects and competition mechanisms targeting the interactions between p53 and MDM2 for cancer therapy

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Liu, Shu-Xia; Geng, Yi-Zhao; Yan, Shi-Wei

2017-06-01

Approximately half of all human cancers show normal TP53 gene expression but aberrant overexpression of MDM2 and/or MDMX. This fact suggests a promising cancer therapeutic strategy in targeting the interactions between p53 and MDM2/MDMX. To help realize the goal of developing effective inhibitors to disrupt the p53-MDM2/MDMX interaction, we systematically investigated the structural and interaction characteristics of p53 with inhibitors of its interactions with MDM2 and MDMX from an atomistic perspective using stochastic molecular dynamics simulations. We found that some specific α helices in the structures of MDM2 and MDMX play key roles in their binding to inhibitors, and that the hydrogen bond formed by the Trp23 residue of p53 with its counterpart in MDM2 or MDMX determines the dynamic competition processes of the disruption of the MDM2-p53 interaction and replacement of p53 from the MDM2-p53 complex in vivo. The results reported in this paper are expected to provide basic information for designing functional inhibitors and realizing new strategies of cancer gene therapy.

Inference of gene-phenotype associations via protein-protein interaction and orthology.

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Panwen Wang

Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.

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Jian Gong

2016-10-01

Full Text Available Genome-wide association studies (GWAS have identified many genetic susceptibility loci for colorectal cancer (CRC. However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO. Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8 region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4 and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6 but not associated among those with the CC genotype (p = 0.059. No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

EXSPRESSION OF MDR-GENES AND MONORESISTANCE GENES IN NON-SMALL-CELL LUNG CANCER

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E. L. Yumov

2014-01-01

Full Text Available We studied the expression of multidrug resistance genes (MDR and monoresistance genes in normal bronchial tissue and tumor tissue of the non-small cell lung cancer (NSCLC after neoadjuvant chemotherapy (NACT (vinorelbine-carboplatine. The study included 30 patients with NSCLC (Т2–4N0–3M0. Normal bronchial tissue, normal lung tissue and tumor tissue collected during surgery following neoadjuvant chemotherapy (NACT served as a material of the study. The expression levels of MDR genes (ABCB1, ABCB2, ABCC1, ABCC2, ABCС5, ABCG1, ABCG2, GSTP and MVP, and monoresistance genes (BRCA1, ERCC1, RRM1, TOP1, TOP2A, TUBB3 and TYMS were estimated by quantitative reverse transcriptase PCR (RT-qPCR. The expression levels of some MDR genes and monoresistance genes (АВСВ1, АВСВ2, ABCG1, ERCC1, GSTP1 and MVP were significantly higher in the bronchi than in tumor tissue. The expression of ABCG1, ABCG2 and ERCC1 genes was higher in patients with T1-2 cancer than in patients with T3-4 cancer. Patients with adenocarcinoma had higher expression of BRCA1, MVP and ABCB1 genes than patients with squamous cell lung cancer. A tendency towards reduction in the expression level of MDR-genes and monoresistance genes was observed in patients with partial tumor regression compared to that observed in patients with stable disease. These findings were consistent with the previous data on reduction in the MDR-gene expression after chemotherapy with a good response in breast cancer patients.

Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

Science.gov (United States)

Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

2013-01-01

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

Analysis of the interaction of extracellular matrix and phenotype of bladder cancer cells

International Nuclear Information System (INIS)

Dozmorov, Mikhail G; Kyker, Kimberly D; Saban, Ricardo; Knowlton, Nicholas; Dozmorov, Igor; Centola, Michael B; Hurst, Robert E

2006-01-01

The extracellular matrix has a major effect upon the malignant properties of bladder cancer cells both in vitro in 3-dimensional culture and in vivo. Comparing gene expression of several bladder cancer cells lines grown under permissive and suppressive conditions in 3-dimensional growth on cancer-derived and normal-derived basement membrane gels respectively and on plastic in conventional tissue culture provides a model system for investigating the interaction of malignancy and extracellular matrix. Understanding how the extracellular matrix affects the phenotype of bladder cancer cells may provide important clues to identify new markers or targets for therapy. Five bladder cancer cell lines and one immortalized, but non-tumorigenic, urothelial line were grown on Matrigel, a cancer-derived ECM, on SISgel, a normal-derived ECM, and on plastic, where the only ECM is derived from the cells themselves. The transcriptomes were analyzed on an array of 1186 well-annotated cancer derived cDNAs containing most of the major pathways for malignancy. Hypervariable genes expressing more variability across cell lines than a set expressing technical variability were analyzed further. Expression values were clustered, and to identify genes most likely to represent biological factors, statistically over-represented ontologies and transcriptional regulatory elements were identified. Approximately 400 of the 1186 total genes were expressed 2 SD above background. Approximately 100 genes were hypervariable in cells grown on each ECM, but the pattern was different in each case. A core of 20 were identified as hypervariable under all 3 growth conditions, and 33 were hypervariable on both SISgel and Matrigel, but not on plastic. Clustering of the hypervariable genes showed very different patterns for the same 6 cell types on the different ECM. Even when loss of cell cycle regulation was identified, different genes were involved, depending on the ECM. Under the most permissive conditions

Interaction of cytochrome P4501A1 genotypes with other risk factors and susceptibility to lung cancer

Energy Technology Data Exchange (ETDEWEB)

Shah, Parag P.; Singh, Arvind P.; Singh, Madhu; Mathur, Neeraj [Developmental Toxicology Division, Industrial Toxicology Research Centre, P.O. Box 80, M.G. Marg, Lucknow 226001 (India); Pant, Mohan C. [Department of Radiotherapy, King George' s Medical University, Shahmina Road, Lucknow 226001 (India); Mishra, Bhartendu N. [Department of Biotechnology, IET, Sitapur Road, Lucknow 226021 (India); Parmar, Devendra [Developmental Toxicology Division, Industrial Toxicology Research Centre, P.O. Box 80, M.G. Marg, Lucknow 226001 (India)], E-mail: parmar_devendra@hotmail.com

2008-03-01

Lung cancer is the most common cause of death throughout the world with cigarette smoking being established as the major etiological factor in lung cancer. Since not much information is available regarding the polymorphism in drug metabolizing enzymes and lung cancer risk in the Indian population, the present case-control study attempted to investigate the association of polymorphisms in cytochrome P450 1A1 (CYP1A1) and glutathione-S-transferase M1 (GSTM1) with risk to squamous cell carcinoma of lung malignancy. Patients suffering from lung cancer (n = 200) and visiting OPD facility of Department of Radiotherapy, King George's Medical University, Lucknow, were included in the study. Equal number (n = 200) of age and sex matched healthy individuals were also enrolled in the study. Our data revealed that the variant genotypes of CYP1A1*2A, CYP1A1*2C and CYP1A1*4 were found to be over represented in the lung cancer patients when compared to controls. CYP1A1*2A variant genotypes (combined heterozygous and mutant genotypes) revealed significant association towards the lung cancer risk (OR: 1.93, 95%CI: 1.28-2.89, p = 0.002). Likewise, GSTM1 null genotypes were found to be over represented in patients when compared to controls. Haplotype analysis revealed that CYP1A1 haplotype, C-G-C increased the lung cancer risk (OR: 3.90, 95%CI: 1.00-15.04, p = 0.025) in the patients. The lung cancer risk was increased several two-to fourfold in the patients carrying the genotype combinations of CYP1A1*2A and GSTM1 suggesting the role of gene-gene interaction in lung cancer. Cigarette smoking or tobacco chewing or alcohol consumption was also found to interact with CYP1A1 genotypes in increasing the risk to lung cancer further demonstrating the role of gene-environment interaction in development of lung cancer.

Interaction of cytochrome P4501A1 genotypes with other risk factors and susceptibility to lung cancer

International Nuclear Information System (INIS)

Shah, Parag P.; Singh, Arvind P.; Singh, Madhu; Mathur, Neeraj; Pant, Mohan C.; Mishra, Bhartendu N.; Parmar, Devendra

2008-01-01

Lung cancer is the most common cause of death throughout the world with cigarette smoking being established as the major etiological factor in lung cancer. Since not much information is available regarding the polymorphism in drug metabolizing enzymes and lung cancer risk in the Indian population, the present case-control study attempted to investigate the association of polymorphisms in cytochrome P450 1A1 (CYP1A1) and glutathione-S-transferase M1 (GSTM1) with risk to squamous cell carcinoma of lung malignancy. Patients suffering from lung cancer (n = 200) and visiting OPD facility of Department of Radiotherapy, King George's Medical University, Lucknow, were included in the study. Equal number (n = 200) of age and sex matched healthy individuals were also enrolled in the study. Our data revealed that the variant genotypes of CYP1A1*2A, CYP1A1*2C and CYP1A1*4 were found to be over represented in the lung cancer patients when compared to controls. CYP1A1*2A variant genotypes (combined heterozygous and mutant genotypes) revealed significant association towards the lung cancer risk (OR: 1.93, 95%CI: 1.28-2.89, p = 0.002). Likewise, GSTM1 null genotypes were found to be over represented in patients when compared to controls. Haplotype analysis revealed that CYP1A1 haplotype, C-G-C increased the lung cancer risk (OR: 3.90, 95%CI: 1.00-15.04, p = 0.025) in the patients. The lung cancer risk was increased several two-to fourfold in the patients carrying the genotype combinations of CYP1A1*2A and GSTM1 suggesting the role of gene-gene interaction in lung cancer. Cigarette smoking or tobacco chewing or alcohol consumption was also found to interact with CYP1A1 genotypes in increasing the risk to lung cancer further demonstrating the role of gene-environment interaction in development of lung cancer

Can gene fusions serve for fingerprints of radiogenic cancers?

International Nuclear Information System (INIS)

Nakamura, Nori

2016-01-01

It has been recognized that malignancies in blood cells often bear specific chromosome translocations or gene fusions. In recent years, the presence of fusion genes became to be known also among solid cancers as driver mutations. However, representative solid cancers bearing specific gene fusions are limited to cancers of thyroid, prostate, and sarcomas among which only thyroid cancer risk is known to be related to radiation exposures. On the other hand, it is extremely rare to find recurrent reciprocal translocations among common cancers such as in the lung, stomach, breast, and colon, which form a major component of radiation risks. It is therefore unlikely that radiation increases the risk of cancer by inducing specific translocations (gene fusions) but more likely through induction of mutations (including deletions). Although gene fusions could play a role in radiation carcinogenesis, it does not seem good enough to serve for a radiation fingerprint. (author)

HFE gene C282Y variant is associated with colorectal cancer in Caucasians: a meta-analysis.

Science.gov (United States)

Chen, Weidong; Zhao, Hua; Li, Tiegang; Yao, Hongliang

2013-08-01

The HFE gene has been suggested to play an important role in the pathogenesis of colorectal cancer. However, the results have been conflicting. In this study, we performed a meta-analysis to clarify the association of HFE gene C282Y variant with colorectal cancer. PubMed and Embase were retrieved to identify the potential literature. Pooled odds ratio (OR) with 95 % confidence interval (CI) was calculated using fixed- or random-effects model. A total of eight papers including nine studies (7,588 colorectal cancer cases and 81,571 controls) for HFE gene C282Y variant were included in the meta-analysis. The result indicated that HFE gene C282Y variant was significantly associated with colorectal cancer under recessive model (OR = 2.00, 95 % CI = 1.32-3.04), with no evidence of between-study heterogeneity (I (2) = 0.2 %, p = 0.432). Further subgroup analysis by number of cases suggested the effect was significant in studies with more than 500 cases (OR = 2.51, 95 % CI = 1.58-3.98, I (2) = 0.0 %, p = 0.921), but not in studies with less than 500 cases (OR = 0.75, 95 % CI = 0.28-1.97, I (2) = 0.0 %, p = 0.622). The current meta-analysis supported the positive association of HFE gene C282Y variant with colorectal cancer. Further large-scale studies with the consideration for gene-gene/gene-environment interactions should be conducted to investigate the association.

The interaction effects of pri-let-7a-1 rs10739971 with PGC and ERCC6 gene polymorphisms in gastric cancer and atrophic gastritis.

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Qian Xu

Full Text Available BACKGROUND: The aim of this study was to investigate the interaction effects of pri-let-7a-1 rs10739971 with pepsinogen C (PGC and excision repair cross complementing group 6 (ERCC6 gene polymorphisms and its association with the risks of gastric cancer and atrophic gastritis. We hoped to identify miRNA polymorphism or a combination of several polymorphisms that could serve as biomarkers for predicting the risk of gastric cancer and its precancerous diseases. METHODS: Sequenom MassARRAY platform method was used to detect polymorphisms of pri-let-7a-1 rs10739971 G → A, PGC rs4711690 C → G, PGC rs6458238 G → A, PGC rs9471643 G → C, and ERCC6 rs1917799 in 471 gastric cancer patients, 645 atrophic gastritis patients and 717 controls. RESULTS: An interaction effect of pri-let-7a-1 rs10739971 polymorphism with ERCC6 rs1917799 polymorphism was observed for the risk of gastric cancer (P interaction = 0.026; and interaction effects of pri-let-7a-1 rs10739971 polymorphism with PGC rs6458238 polymorphism (P interaction = 0.012 and PGC rs9471643 polymorphism (P interaction = 0.039 were observed for the risk of atrophic gastritis. CONCLUSION: The combination of pri-let-7a-1 rs10739971 polymorphism and ERCC6 and PGC polymorphisms could provide a greater prediction potential than a single polymorphism on its own. Large-scale studies and molecular mechanism research are needed to confirm our findings.

AURKA Phe31Ile polymorphism interacted with use of alcohol, betel quid, and cigarettes at multiplicative risk of oral cancer occurrence.

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Lee, Chi-Pin; Chiang, Shang-Lun; Lee, Chien-Hung; Tsai, Yi-Shan; Wang, Zhi-Hong; Hua, Chun-Hung; Chen, Yuan-Chien; Tsai, Eing-Mei; Ko, Ying-Chin

2015-11-01

The expression levels of two DNA repair genes (CHAF1A and CHAF1B) and a chromosome segregation gene (AURKA) were susceptible to arecoline exposure, a major alkaloid of areca nut. We hypothesize that genetic variants of these genes might also be implicated in the risk of oral cancer and could be modified by substance use of betel quid or alcohol and cigarettes. A case-control study, which included 507 patients with oral cancer and 717 matched controls, was performed in order to evaluate the cancer susceptibility by the tagging single-nucleotide polymorphisms (tagSNPs) in AURKA, CHAF1A, and CHAF1B using a genotyping assay and gene-environment interaction analysis. The Phe31Ile polymorphism (rs2273535, T91A) of AURKA was significantly associated with an increased risk of oral cancer (odds ratio (OR) = 2.1, 95% confidence interval (CI) 1.2-3.5). The gene dosage of the 91A allele also showed a significant trend in risk of oral cancer (P = 0.008). Furthermore, we found the AURKA 91AA homozygote was modifiable by substance use of alcohol, betel quid, and cigarettes (ABC), leading to increased risk of oral cancer in an additive or a multiplicative model (combined effect indexes = 1.2-4.0 and 1.5-2.2, respectively). However, no association was observed between the genetic variants of CHAF1A or CHAF1B and oral cancer risk in the study. These findings reveal the functional Phe31Ile polymorphism tagSNP of AURKA may be a strong susceptibility gene in ABC-related oral cancer occurrence. The results of this betel-related oral cancer study provide the evidence of environment-gene interaction for early prediction and molecular diagnosis.

Interactions between household air pollution and GWAS-identified lung cancer susceptibility markers in the Female Lung Cancer Consortium in Asia (FLCCA).

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Hosgood, H Dean; Song, Minsun; Hsiung, Chao Agnes; Yin, Zhihua; Shu, Xiao-Ou; Wang, Zhaoming; Chatterjee, Nilanjan; Zheng, Wei; Caporaso, Neil; Burdette, Laurie; Yeager, Meredith; Berndt, Sonja I; Landi, Maria Teresa; Chen, Chien-Jen; Chang, Gee-Chen; Hsiao, Chin-Fu; Tsai, Ying-Huang; Chien, Li-Hsin; Chen, Kuan-Yu; Huang, Ming-Shyan; Su, Wu-Chou; Chen, Yuh-Min; Chen, Chung-Hsing; Yang, Tsung-Ying; Wang, Chih-Liang; Hung, Jen-Yu; Lin, Chien-Chung; Perng, Reury-Perng; Chen, Chih-Yi; Chen, Kun-Chieh; Li, Yao-Jen; Yu, Chong-Jen; Chen, Yi-Song; Chen, Ying-Hsiang; Tsai, Fang-Yu; Kim, Christopher; Seow, Wei Jie; Bassig, Bryan A; Wu, Wei; Guan, Peng; He, Qincheng; Gao, Yu-Tang; Cai, Qiuyin; Chow, Wong-Ho; Xiang, Yong-Bing; Lin, Dongxin; Wu, Chen; Wu, Yi-Long; Shin, Min-Ho; Hong, Yun-Chul; Matsuo, Keitaro; Chen, Kexin; Wong, Maria Pik; Lu, Dara; Jin, Li; Wang, Jiu-Cun; Seow, Adeline; Wu, Tangchun; Shen, Hongbing; Fraumeni, Joseph F; Yang, Pan-Chyr; Chang, I-Shou; Zhou, Baosen; Chanock, Stephen J; Rothman, Nathaniel; Lan, Qing

2015-03-01

We previously carried out a multi-stage genome-wide association study (GWAS) on lung cancer among never smokers in the Female Lung Cancer Consortium in Asia (FLCCA) (6,609 cases, 7,457 controls) that identified novel susceptibility loci at 10q25.2, 6q22.2, and 6p21.32, and confirmed two previously identified loci at 5p15.33 and 3q28. Household air pollution (HAP) attributed to solid fuel burning for heating and cooking, is the leading cause of the overall disease burden in Southeast Asia, and is known to contain lung carcinogens. To evaluate the gene-HAP interactions associated with lung cancer in loci independent of smoking, we analyzed data from studies participating in FLCCA with fuel use information available (n = 3; 1,731 cases; 1,349 controls). Coal use was associated with a 30% increased risk of lung cancer (OR 1.3, 95% CI 1.0-1.6). Among the five a priori SNPs identified by our GWAS, two showed a significant interaction with coal use (HLA Class II rs2395185, p = 0.02; TP63 rs4488809 (rs4600802), p = 0.04). The risk of lung cancer associated with coal exposure varied with the respective alleles for these two SNPs. Our observations provide evidence that genetic variation in HLA Class II and TP63 may modify the association between HAP and lung cancer risk. The roles played in the cell cycle and inflammation pathways by the proteins encoded by these two genes provide biological plausibility for these interactions; however, additional replication studies are needed in other non-smoking populations.

Evaluation of candidate stromal epithelial cross-talk genes identifies association between risk of serous ovarian cancer and TERT, a cancer susceptibility "hot-spot".

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Sharon E Johnatty

2010-07-01

Full Text Available We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs in 173 genes involved in stromal epithelial interactions in the Ovarian Cancer Association Consortium (OCAC. In the discovery stage, cases with epithelial ovarian cancer (n=675 and controls (n=1,162 were genotyped at 1,536 SNPs using an Illumina GoldenGate assay. Based on Positive Predictive Value estimates, three SNPs-PODXL rs1013368, ITGA6 rs13027811, and MMP3 rs522616-were selected for replication using TaqMan genotyping in up to 3,059 serous invasive cases and 8,905 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-alleleor=0.5. However genotypes at TERT rs7726159 were associated with ovarian cancer risk in the smaller, five-study replication study (Pper-allele=0.03. Combined analysis of the discovery and replication sets for this TERT SNP showed an increased risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04-1.24 p=0.003]. Our study adds to the growing evidence that, like the 8q24 locus, the telomerase reverse transcriptase locus at 5p15.33, is a general cancer susceptibility locus.

Development of Peptidomimetic Inhibitors of the ERG Gene Fusion Product in Prostate Cancer.

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Wang, Xiaoju; Qiao, Yuanyuan; Asangani, Irfan A; Ateeq, Bushra; Poliakov, Anton; Cieślik, Marcin; Pitchiaya, Sethuramasundaram; Chakravarthi, Balabhadrapatruni V S K; Cao, Xuhong; Jing, Xiaojun; Wang, Cynthia X; Apel, Ingrid J; Wang, Rui; Tien, Jean Ching-Yi; Juckette, Kristin M; Yan, Wei; Jiang, Hui; Wang, Shaomeng; Varambally, Sooryanarayana; Chinnaiyan, Arul M

2017-04-10

Transcription factors play a key role in the development of diverse cancers, and therapeutically targeting them has remained a challenge. In prostate cancer, the gene encoding the transcription factor ERG is recurrently rearranged and plays a critical role in prostate oncogenesis. Here, we identified a series of peptides that interact specifically with the DNA binding domain of ERG. ERG inhibitory peptides (EIPs) and derived peptidomimetics bound ERG with high affinity and specificity, leading to proteolytic degradation of the ERG protein. The EIPs attenuated ERG-mediated transcription, chromatin recruitment, protein-protein interactions, cell invasion and proliferation, and tumor growth. Thus, peptidomimetic targeting of transcription factor fusion products may provide a promising therapeutic strategy for prostate cancer as well as other malignancies. Copyright © 2017 Elsevier Inc. All rights reserved.

miRNA-Processing Gene Methylation and Cancer Risk.

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Joyce, Brian T; Zheng, Yinan; Zhang, Zhou; Liu, Lei; Kocherginsky, Masha; Murphy, Robert; Achenbach, Chad J; Musa, Jonah; Wehbe, Firas; Just, Allan; Shen, Jincheng; Vokonas, Pantel; Schwartz, Joel; Baccarelli, Andrea A; Hou, Lifang

2018-05-01

Background: Dysregulation of miRNA and methylation levels are epigenetic hallmarks of cancer, potentially linked via miRNA-processing genes. Studies have found genetic alterations to miRNA-processing genes in cancer cells and human population studies. Our objective was to prospectively examine changes in DNA methylation of miRNA-processing genes and their associations with cancer risk. Methods: We examined cohort data from the Department of Veterans' Affairs Normative Aging Study. Participants were assessed every 3 to 5 years starting in 1999 through 2013 including questionnaires, medical record review, and blood collection. Blood from 686 consenting participants was analyzed using the Illumina 450K BeadChip array to measure methylation at CpG sites throughout the genome. We selected 19 genes based on a literature review, with 519 corresponding CpG sites. We then used Cox proportional hazards models to examine associations with cancer incidence, and generalized estimating equations to examine associations with cancer prevalence. Associations at false discovery rate time to cancer development (positively for cg06751583, inversely for cg23230564 and cg21034183), whereas methylation of one CpG site ( DROSHA : cg16131300) was positively associated with cancer prevalence. Conclusions: DNA methylation of DROSHA , a key miRNA-processing gene, and TNRC6B may play a role in early carcinogenesis. Impact: Changes in miRNA processing may exert multiple effects on cancer development, including protecting against it via altered global miRNAs, and may be a useful early detection biomarker of cancer. Cancer Epidemiol Biomarkers Prev; 27(5); 550-7. ©2018 AACR . ©2018 American Association for Cancer Research.

Methylation of Breast Cancer Predisposition Genes in Early-Onset Breast Cancer: Australian Breast Cancer Family Registry.

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Cameron M Scott

Full Text Available DNA methylation can mimic the effects of both germline and somatic mutations for cancer predisposition genes such as BRCA1 and p16INK4a. Constitutional DNA methylation of the BRCA1 promoter has been well described and is associated with an increased risk of early-onset breast cancers that have BRCA1-mutation associated histological features. The role of methylation in the context of other breast cancer predisposition genes has been less well studied and often with conflicting or ambiguous outcomes. We examined the role of methylation in known breast cancer susceptibility genes in breast cancer predisposition and tumor development. We applied the Infinium HumanMethylation450 Beadchip (HM450K array to blood and tumor-derived DNA from 43 women diagnosed with breast cancer before the age of 40 years and measured the methylation profiles across promoter regions of BRCA1, BRCA2, ATM, PALB2, CDH1, TP53, FANCM, CHEK2, MLH1, MSH2, MSH6 and PMS2. Prior genetic testing had demonstrated that these women did not carry a germline mutation in BRCA1, ATM, CHEK2, PALB2, TP53, BRCA2, CDH1 or FANCM. In addition to the BRCA1 promoter region, this work identified regions with variable methylation at multiple breast cancer susceptibility genes including PALB2 and MLH1. Methylation at the region of MLH1 in these breast cancers was not associated with microsatellite instability. This work informs future studies of the role of methylation in breast cancer susceptibility gene silencing.

Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes

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Ashkani, Jahanshah; Naidoo, Kevin J.

2016-05-01

Aberrant glycosylation in tumours stem from altered glycosyltransferase (GT) gene expression but can the expression profiles of these signature genes be used to classify cancer types and lead to cancer subtype discovery? The differential structural changes to cellular glycan structures are predominantly regulated by the expression patterns of GT genes and are a hallmark of neoplastic cell metamorphoses. We found that the expression of 210 GT genes taken from 1893 cancer patient samples in The Cancer Genome Atlas (TCGA) microarray data are able to classify six cancers; breast, ovarian, glioblastoma, kidney, colon and lung. The GT gene expression profiles are used to develop cancer classifiers and propose subtypes. The subclassification of breast cancer solid tumour samples illustrates the discovery of subgroups from GT genes that match well against basal-like and HER2-enriched subtypes and correlates to clinical, mutation and survival data. This cancer type glycosyltransferase gene signature finding provides foundational evidence for the centrality of glycosylation in cancer.

Finding biological process modifications in cancer tissues by mining gene expression correlations

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Storari Sergio

2006-01-01

Full Text Available Abstract Background Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This technology yields a global view of gene expression that can be used in several ways. Functional insight into expression profiles is routinely obtained by using Gene Ontology terms associated to the cellular genes. In this paper, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO. By using this "functional correlations comparison" we explore all possible pairs of genes identifying the affected biological processes by analyzing in a pair-wise manner gene expression patterns and linking correlated pairs with Gene Ontology terms. Results We apply here this "functional correlations comparison" approach to identify the existing correlations in hepatocarcinoma (161 microarray experiments and to reveal functional differences between normal liver and cancer tissues. The number of well-correlated pairs in each GO term highlights several differences in genetic interactions between cancer and normal tissues. We performed a bootstrap analysis in order to compute false detection rates (FDR and confidence limits. Conclusion Experimental results show the main advantage of the applied method: it both picks up general and specific GO terms (in particular it shows a fine resolution in the specific GO terms. The results obtained by this novel method are highly coherent with the ones proposed by other cancer biology studies. But additionally they highlight the most specific and interesting GO terms helping the biologist to focus his/her studies on the most relevant biological processes.

A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types

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Lin, Chen-Ching; Zhao, Junfei; Jia, Peilin; Li, Wen-Hsiung; Zhao, Zhongming

2015-01-01

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics. PMID:26352260

Expression of KLK2 gene in prostate cancer

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Sajad Shafai

2018-01-01

Conclusion: The expression of KLK2 gene in people with prostate cancer is the higher than the healthy person; finally, according to the results, it could be mentioned that the KLK2 gene considered as a useful factor in prostate cancer, whose expression is associated with progression and development of the prostate cancer.

Undefined familial colorectal cancer and the role of pleiotropism in cancer susceptibility genes.

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Dobbins, Sara E; Broderick, Peter; Chubb, Daniel; Kinnersley, Ben; Sherborne, Amy L; Houlston, Richard S

2016-10-01

Although family history is a major risk factor for colorectal cancer (CRC) a genetic diagnosis cannot be obtained in over 50 % of familial cases when screened for known CRC cancer susceptibility genes. The genetics of undefined-familial CRC is complex and recent studies have implied additional clinically actionable mutations for CRC in susceptibility genes for other cancers. To clarify the contribution of non-CRC susceptibility genes to undefined-familial CRC we conducted a mutational screen of 114 cancer susceptibility genes in 847 patients with early-onset undefined-familial CRC and 1609 controls by analysing high-coverage exome sequencing data. We implemented American College of Medical Genetics and Genomics standards and guidelines for assigning pathogenicity to variants. Globally across all 114 cancer susceptibility genes no statistically significant enrichment of likely pathogenic variants was shown (6.7 % cases 57/847, 5.3 % controls 85/1609; P = 0.15). Moreover there was no significant enrichment of mutations in genes such as TP53 or BRCA2 which have been proposed for clinical testing in CRC. In conclusion, while we identified genes that may be considered interesting candidates as determinants of CRC risk warranting further research, there is currently scant evidence to support a role for genes other than those responsible for established CRC syndromes in the clinical management of familial CRC.

Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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Chen Xin

2012-10-01

Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

Vitamin D receptor gene polymorphisms, dietary promotion of insulin resistance, and colon and rectal cancer.

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Murtaugh, Maureen A; Sweeney, Carol; Ma, Khe-Ni; Potter, John D; Caan, Bette J; Wolff, Roger K; Slattery, Martha L

2006-01-01

Modifiable risk factors in colorectal cancer etiology and their interactions with genetic susceptibility are of particular interest. Functional vitamin D receptor (VDR) gene polymorphisms may influence carcinogenesis through modification of cell growth, protection from oxidative stress, cell-cell matrix effects, or insulin and insulin-like growth factor pathways. We investigated interactions between foods (dairy products, red and processed meat, and whole and refined grains) and dietary patterns (sucrose-to-fiber ratio and glycemic index) associated with insulin resistance with the FokI polymorphism of the VDR gene and colon and rectal cancer risk. Data (diet, anthropometrics, and lifestyle) and DNA came from case-control studies of colon (1,698 cases and 1,861 controls) and rectal cancer (752 cases and 960 controls) in northern California, Utah, and the Twin Cities metropolitan area, Minnesota (colon cancer study only). Unconditional logistic regression models were adjusted for smoking, race, sex, age, body mass index, physical activity, energy intake, dietary fiber, and calcium. The lowest colon cancer risk was observed with the Ff/ff FokI genotypes and a low sucrose-to-fiber ratio. Rectal cancer risk decreased with greater consumption of dairy products and increased with red or processed meat consumption and the FF genotype. Modifiable dietary risk factors may be differentially important among individuals by VDR genotype and may act through the insulin pathway to affect colon cancer risk and through fat, calcium, or other means to influence rectal cancer risk.

Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions

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Schoeps, Anja; Rudolph, Anja; Seibold, Petra

2014-01-01

recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714......,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three...... in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10(-07)), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8...

Trends in gastrectomy and ADH1B and ALDH2 genotypes in Japanese alcoholic men and their gene-gastrectomy, gene-gene and gene-age interactions for risk of alcoholism.

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Yokoyama, Akira; Yokoyama, Tetsuji; Matsui, Toshifumi; Mizukami, Takeshi; Kimura, Mitsuru; Matsushita, Sachio; Higuchi, Susumu; Maruyama, Katsuya

2013-01-01

The life-time drinking profiles of Japanese alcoholics have shown that gastrectomy increases susceptibility to alcoholism. We investigated the trends in gastrectomy and alcohol dehydrogenase-1B (ADH1B) and aldehyde dehydrogenase-2 (ALDH2) genotypes and their interactions in alcoholics. This survey was conducted on 4879 Japanese alcoholic men 40 years of age or older who underwent routine gastrointestinal endoscopic screening during the period 1996-2010. ADH1B/ALDH2 genotyping was performed in 3702 patients. A history of gastrectomy was found in 508 (10.4%) patients. The reason for the gastrectomy was peptic ulcer in 317 patients and gastric cancer in 187 patients. The frequency of gastrectomy had gradually decreased from 13.3% in 1996-2000 to 10.5% in 2001-2005 and to 7.8% in 2006-2010 (P alcoholism-susceptibility genotypes, ADH1B*1/*1 and ALDH2*1/*1, modestly but significantly tended not to occur in the same individual (P = 0.026). The frequency of ADH1B*1/*1 decreased with ascending age groups. The high frequency of history of gastrectomy suggested that gastrectomy is still a risk factor for alcoholism, although the percentage decreased during the period. The alcoholism-susceptibility genotype ADH1B*1/*1 was less frequent in the gastrectomy group, suggesting a competitive gene-gastrectomy interaction for alcoholism. A gene-gene interaction and gene-age interactions regarding the ADH1B genotype were observed.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

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Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

Combining gene signatures improves prediction of breast cancer survival.

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Xi Zhao

Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

Predictability of Genetic Interactions from Functional Gene Modules

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Jonathan H. Young

2017-02-01

Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

Association of -330 interleukin-2 gene polymorphism with oral cancer.

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Singh, Prithvi Kumar; Kumar, Vijay; Ahmad, Mohammad Kaleem; Gupta, Rajni; Mahdi, Abbas Ali; Jain, Amita; Bogra, Jaishri; Chandra, Girish

2017-12-01

Cytokines play an important role in the development of cancer. Several single-nucleotide polymorphisms (SNPs) of cytokine genes have been reported to be associated with the development and severity of inflammatory diseases and cancer predisposition. This study was undertaken to evaluate a possible association of interleukin 2 (IL-2) (- 330A>C) gene polymorphisms with the susceptibility to oral cancer. The SNP in IL-2 (-330A>C) gene was genotyped in 300 oral cancer patients and in similar number of healthy volunteers by polymerase chain reaction (PCR)-restriction fragment length polymorphism and the association of the gene with the disease was evaluated. IL-2 (-330A>C) gene polymorphism was significantly associated with oral cancer whereas it was neither associated with clinicopathological status nor with cancer pain. The AC heterozygous genotype was significantly associated with oral cancer patients as compared to controls [odds ratio (OR): 3.0; confidence interval (CI): 2.14-4.20; Poral cancer (OR: 1.80; CI: 1.39-2.33; PC) gene polymorphism was also associated with oral cancer in tobacco smokers and chewers. Our results showed that oral cancer patients had significantly higher frequency of AA genotype but significantly lower frequency of AC genotype and C allele compared to controls. The IL-2 AC genotype and C allele of IL-2 (-330A>C) gene polymorphisms could be potential protective factors and might reduce the risk of oral cancer in Indian population.

Association of MTHFR gene polymorphisms with breast cancer survival

International Nuclear Information System (INIS)

Martin, Damali N; Boersma, Brenda J; Howe, Tiffany M; Goodman, Julie E; Mechanic, Leah E; Chanock, Stephen J; Ambs, Stefan

2006-01-01

Two functional single nucleotide polymorphisms (SNPs) in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, C677T and A1298C, lead to decreased enzyme activity and affect chemosensitivity of tumor cells. We investigated whether these MTHFR SNPs were associated with breast cancer survival in African-American and Caucasian women. African-American (n = 143) and Caucasian (n = 105) women, who had incident breast cancer with surgery, were recruited between 1993 and 2003 from the greater Baltimore area, Maryland, USA. Kaplan-Meier survival and multivariate Cox proportional hazards regression analyses were used to examine the relationship between MTHFR SNPs and disease-specific survival. We observed opposite effects of the MTHFR polymorphisms A1298C and C677T on breast cancer survival. Carriers of the variant allele at codon 1298 (A/C or C/C) had reduced survival when compared to homozygous carriers of the common A allele [Hazard ratio (HR) = 2.05; 95% confidence interval (CI), 1.05–4.00]. In contrast, breast cancer patients with the variant allele at codon 677 (C/T or T/T) had improved survival, albeit not statistically significant, when compared to individuals with the common C/C genotype (HR = 0.65; 95% CI, 0.31–1.35). The effects were stronger in patients with estrogen receptor-negative tumors (HR = 2.70; 95% CI, 1.17–6.23 for A/C or C/C versus A/A at codon 1298; HR = 0.36; 95% CI, 0.12–1.04 for C/T or T/T versus C/C at codon 677). Interactions between the two MTHFR genotypes and race/ethnicity on breast cancer survival were also observed (A1298C, p interaction = 0.088; C677T, p interaction = 0.026). We found that the MTHFR SNPs, C677T and A1298C, were associated with breast cancer survival. The variant alleles had opposite effects on disease outcome in the study population. Race/ethnicity modified the association between the two SNPs and breast cancer survival

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

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Kar, Siddhartha P; Adler, Emily; Tyrer, Jonathan

2017-01-01

BACKGROUND: Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors...... (TFs) critical to somatic tumorigenesis. METHODS: All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery...... to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P

Anti-EGFR immunonanoparticles containing IL12 and salmosin genes for targeted cancer gene therapy.

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Kim, Jung Seok; Kang, Seong Jae; Jeong, Hwa Yeon; Kim, Min Woo; Park, Sang Il; Lee, Yeon Kyung; Kim, Hong Sung; Kim, Keun Sik; Park, Yong Serk

2016-09-01

Tumor-directed gene delivery is of major interest in the field of cancer gene therapy. Varied functionalizations of non-viral vectors have been suggested to enhance tumor targetability. In the present study, we prepared two different types of anti-EGF receptor (EGFR) immunonanoparticles containing pDNA, neutrally charged liposomes and cationic lipoplexes, for tumor-directed transfection of cancer therapeutic genes. Even though both anti-EGFR immunonanoparticles had a high binding affinity to the EGFR-positive cancer cells, the anti-EGFR immunolipoplex formulation exhibited approximately 100-fold higher transfection to the target cells than anti-EGFR immunoliposomes. The lipoplex formulation also showed a higher transfection to SK-OV-3 tumor xenografts in mice. Thus, IL12 and/or salmosin genes were loaded in the anti-EGFR immunolipoplexes and intravenously administered to mice carrying SK-OV-3 tumors. Co-transfection of IL12 and salmosin genes using anti-EGFR immunolipoplexes significantly reduced tumor growth and pulmonary metastasis. Furthermore, combinatorial treatment with doxorubicin synergistically inhibited tumor growth. These results suggest that anti-EGFR immunolipoplexes containing pDNA encoding therapeutic genes could be utilized as a gene-transfer modality for cancer gene therapy.

Human cancer cells express Slug-based epithelial-mesenchymal transition gene expression signature obtained in vivo

International Nuclear Information System (INIS)

Anastassiou, Dimitris; Rumjantseva, Viktoria; Cheng, Weiyi; Huang, Jianzhong; Canoll, Peter D; Yamashiro, Darrell J; Kandel, Jessica J

2011-01-01

The biological mechanisms underlying cancer cell motility and invasiveness remain unclear, although it has been hypothesized that they involve some type of epithelial-mesenchymal transition (EMT). We used xenograft models of human cancer cells in immunocompromised mice, profiling the harvested tumors separately with species-specific probes and computationally analyzing the results. Here we show that human cancer cells express in vivo a precise multi-cancer invasion-associated gene expression signature that prominently includes many EMT markers, among them the transcription factor Slug, fibronectin, and α-SMA. We found that human, but not mouse, cells express the signature and Slug is the only upregulated EMT-inducing transcription factor. The signature is also present in samples from many publicly available cancer gene expression datasets, suggesting that it is produced by the cancer cells themselves in multiple cancer types, including nonepithelial cancers such as neuroblastoma. Furthermore, we found that the presence of the signature in human xenografted cells was associated with a downregulation of adipocyte markers in the mouse tissue adjacent to the invasive tumor, suggesting that the signature is triggered by contextual microenvironmental interactions when the cancer cells encounter adipocytes, as previously reported. The known, precise and consistent gene composition of this cancer mesenchymal transition signature, particularly when combined with simultaneous analysis of the adjacent microenvironment, provides unique opportunities for shedding light on the underlying mechanisms of cancer invasiveness as well as identifying potential diagnostic markers and targets for metastasis-inhibiting therapeutics

Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer

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Newcomb, Polly A.; Campbell, Peter T.; Baron, John A.; Berndt, Sonja I.; Bezieau, Stephane; Brenner, Hermann; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Du, Mengmeng; Figueiredo, Jane C.; Gallinger, Steven; Giovannucci, Edward L.; Haile, Robert W.; Harrison, Tabitha A.; Hayes, Richard B.; Hoffmeister, Michael; Hopper, John L.; Hudson, Thomas J.; Jeon, Jihyoun; Jenkins, Mark A.; Küry, Sébastien; Le Marchand, Loic; Lin, Yi; Lindor, Noralane M.; Nishihara, Reiko; Ogino, Shuji; Potter, John D.; Rudolph, Anja; Schoen, Robert E.; Seminara, Daniela; Slattery, Martha L.; Thibodeau, Stephen N.; Thornquist, Mark; Toth, Reka; Wallace, Robert; White, Emily; Jiao, Shuo; Lemire, Mathieu; Hsu, Li; Peters, Ulrike

2016-01-01

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk. PMID:27723779

Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

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Cielito C Reyes-Gibby

Full Text Available Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA, a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive and thymine degradation pathways (p = 1.06-08 were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis. The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67. In conclusion, gene network analysis identified novel molecules and

Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis.

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Reyes-Gibby, Cielito C; Melkonian, Stephanie C; Wang, Jian; Yu, Robert K; Shelburne, Samuel A; Lu, Charles; Gunn, Gary Brandon; Chambers, Mark S; Hanna, Ehab Y; Yeung, Sai-Ching J; Shete, Sanjay

2017-01-01

Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06-08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological

The role of gene-gene interaction in the prediction of criminal behavior.

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Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

2014-04-01

A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

Deregulation of an imprinted gene network in prostate cancer.

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Ribarska, Teodora; Goering, Wolfgang; Droop, Johanna; Bastian, Klaus-Marius; Ingenwerth, Marc; Schulz, Wolfgang A

2014-05-01

Multiple epigenetic alterations contribute to prostate cancer progression by deregulating gene expression. Epigenetic mechanisms, especially differential DNA methylation at imprinting control regions (termed DMRs), normally ensure the exclusive expression of imprinted genes from one specific parental allele. We therefore wondered to which extent imprinted genes become deregulated in prostate cancer and, if so, whether deregulation is due to altered DNA methylation at DMRs. Therefore, we selected presumptive deregulated imprinted genes from a previously conducted in silico analysis and from the literature and analyzed their expression in prostate cancer tissues by qRT-PCR. We found significantly diminished expression of PLAGL1/ZAC1, MEG3, NDN, CDKN1C, IGF2, and H19, while LIT1 was significantly overexpressed. The PPP1R9A gene, which is imprinted in selected tissues only, was strongly overexpressed, but was expressed biallelically in benign and cancerous prostatic tissues. Expression of many of these genes was strongly correlated, suggesting co-regulation, as in an imprinted gene network (IGN) reported in mice. Deregulation of the network genes also correlated with EZH2 and HOXC6 overexpression. Pyrosequencing analysis of all relevant DMRs revealed generally stable DNA methylation between benign and cancerous prostatic tissues, but frequent hypo- and hyper-methylation was observed at the H19 DMR in both benign and cancerous tissues. Re-expression of the ZAC1 transcription factor induced H19, CDKN1C and IGF2, supporting its function as a nodal regulator of the IGN. Our results indicate that a group of imprinted genes are coordinately deregulated in prostate cancers, independently of DNA methylation changes.

Study of miRNA Based Gene Regulation, Involved in Solid Cancer, by the Assistance of Argonaute Protein

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Surya Narayan Rath

2016-09-01

Full Text Available Solid tumor is generally observed in tissues of epithelial or endothelial cells of lung, breast, prostate, pancreases, colorectal, stomach, and bladder, where several genes transcription is regulated by the microRNAs (miRNAs. Argonaute (AGO protein is a family of protein which assists in miRNAs to bind with mRNAs of the target genes. Hence, study of the binding mechanism between AGO protein and miRNAs, and also with miRNAs-mRNAs duplex is crucial for understanding the RNA silencing mechanism. In the current work, 64 genes and 23 miRNAs have been selected from literatures, whose deregulation is well established in seven types of solid cancer like lung, breast, prostate, pancreases, colorectal, stomach, and bladder cancer. In silico study reveals, miRNAs namely, miR-106a, miR-21, and miR-29b-2 have a strong binding affinity towards PTEN, TGFBR2, and VEGFA genes, respectively, suggested as important factors in RNA silencing mechanism. Furthermore, interaction between AGO protein (PDB ID-3F73, chain A with selected miRNAs and with miRNAs-mRNAs duplex were studied computationally to understand their binding at molecular level. The residual interaction and hydrogen bonding are inspected in Discovery Studio 3.5 suites. The current investigation throws light on understanding miRNAs based gene silencing mechanism in solid cancer.

Nanoparticles for cancer gene therapy: Recent advances, challenges, and strategies.

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Wang, Kui; Kievit, Forrest M; Zhang, Miqin

2016-12-01

Compared to conventional treatments, gene therapy offers a variety of advantages for cancer treatment including high potency and specificity, low off-target toxicity, and delivery of multiple genes that concurrently target cancer tumorigenesis, recurrence, and drug resistance. In the past decades, gene therapy has undergone remarkable progress, and is now poised to become a first line therapy for cancer. Among various gene delivery systems, nanoparticles have attracted much attention because of their desirable characteristics including low toxicity profiles, well-controlled and high gene delivery efficiency, and multi-functionalities. This review provides an overview on gene therapeutics and gene delivery technologies, and highlight recent advances, challenges and insights into the design and the utility of nanoparticles in gene therapy for cancer treatment. Copyright © 2016. Published by Elsevier Ltd.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

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Tuo, Youlin; An, Ning; Zhang, Ming

2018-03-01

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of PSVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several

Germline pathogenic variants in PALB2 and other cancer-predisposing genes in families with hereditary diffuse gastric cancer without CDH1 mutation: a whole-exome sequencing study.

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Fewings, Eleanor; Larionov, Alexey; Redman, James; Goldgraben, Mae A; Scarth, James; Richardson, Susan; Brewer, Carole; Davidson, Rosemarie; Ellis, Ian; Evans, D Gareth; Halliday, Dorothy; Izatt, Louise; Marks, Peter; McConnell, Vivienne; Verbist, Louis; Mayes, Rebecca; Clark, Graeme R; Hadfield, James; Chin, Suet-Feung; Teixeira, Manuel R; Giger, Olivier T; Hardwick, Richard; di Pietro, Massimiliano; O'Donovan, Maria; Pharoah, Paul; Caldas, Carlos; Fitzgerald, Rebecca C; Tischkowitz, Marc

2018-04-26

Germline pathogenic variants in the E-cadherin gene (CDH1) are strongly associated with the development of hereditary diffuse gastric cancer. There is a paucity of data to guide risk assessment and management of families with hereditary diffuse gastric cancer that do not carry a CDH1 pathogenic variant, making it difficult to make informed decisions about surveillance and risk-reducing surgery. We aimed to identify new candidate genes associated with predisposition to hereditary diffuse gastric cancer in affected families without pathogenic CDH1 variants. We did whole-exome sequencing on DNA extracted from the blood of 39 individuals (28 individuals diagnosed with hereditary diffuse gastric cancer and 11 unaffected first-degree relatives) in 22 families without pathogenic CDH1 variants. Genes with loss-of-function variants were prioritised using gene-interaction analysis to identify clusters of genes that could be involved in predisposition to hereditary diffuse gastric cancer. Protein-affecting germline variants were identified in probands from six families with hereditary diffuse gastric cancer; variants were found in genes known to predispose to cancer and in lesser-studied DNA repair genes. A frameshift deletion in PALB2 was found in one member of a family with a history of gastric and breast cancer. Two different MSH2 variants were identified in two unrelated affected individuals, including one frameshift insertion and one previously described start-codon loss. One family had a unique combination of variants in the DNA repair genes ATR and NBN. Two variants in the DNA repair gene RECQL5 were identified in two unrelated families: one missense variant and a splice-acceptor variant. The results of this study suggest a role for the known cancer predisposition gene PALB2 in families with hereditary diffuse gastric cancer and no detected pathogenic CDH1 variants. We also identified new candidate genes associated with disease risk in these families. UK Medical

GOBO: gene expression-based outcome for breast cancer online.

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Markus Ringnér

Full Text Available Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo, allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1 rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2 identification of co-expressed genes for creation of potential metagenes, 3 association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.

Interaction between polymorphisms in aspirin metabolic pathways, regular aspirin use and colorectal cancer risk: A case-control study in unselected white European populations.

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Harsh Sheth

Full Text Available Regular aspirin use is associated with reduced risk of colorectal cancer (CRC. Variation in aspirin's chemoprevention efficacy has been attributed to the presence of single nucleotide polymorphisms (SNPs. We conducted a meta-analysis using two large population-based case-control datasets, the UK-Leeds Colorectal Cancer Study Group and the NIH-Colon Cancer Family Registry, having a combined total of 3325 cases and 2262 controls. The aim was to assess 42 candidate SNPs in 15 genes whose association with colorectal cancer risk was putatively modified by aspirin use, in the literature. Log odds ratios (ORs and standard errors were estimated for each dataset separately using logistic regression adjusting for age, sex and study site, and dataset-specific results were combined using random effects meta-analysis. Meta-analysis showed association between SNPs rs6983267, rs11694911 and rs2302615 with CRC risk reduction (All P<0.05. Association for SNP rs6983267 in the CCAT2 gene only was noteworthy after multiple test correction (P = 0.001. Site-specific analysis showed association between SNPs rs1799853 and rs2302615 with reduced colon cancer risk only (P = 0.01 and P = 0.004, respectively, however neither reached significance threshold following multiple test correction. Meta-analysis of SNPs rs2070959 and rs1105879 in UGT1A6 gene showed interaction between aspirin use and CRC risk (Pinteraction = 0.01 and 0.02, respectively; stratification by aspirin use showed an association for decreased CRC risk for aspirin users having a wild-type genotype (rs2070959 OR = 0.77, 95% CI = 0.68-0.86; rs1105879 OR = 0.77 95% CI = 0.69-0.86 compared to variant allele cariers. The direction of the interaction however is in contrast to that published in studies on colorectal adenomas. Both SNPs showed potential site-specific interaction with aspirin use and colon cancer risk only (Pinteraction = 0.006 and 0.008, respectively, with the direction of association similar to

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

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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

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

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Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Gene Signature in Sessile Serrated Polyps Identifies Colon Cancer Subtype

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Kanth, Priyanka; Bronner, Mary P.; Boucher, Kenneth M.; Burt, Randall W.; Neklason, Deborah W.; Hagedorn, Curt H.; Delker, Don A.

2016-01-01

Sessile serrated colon adenoma/polyps (SSA/Ps) are found during routine screening colonoscopy and may account for 20–30% of colon cancers. However, differentiating SSA/Ps from hyperplastic polyps (HP) with little risk of cancer is challenging and complementary molecular markers are needed. Additionally, the molecular mechanisms of colon cancer development from SSA/Ps are poorly understood. RNA sequencing was performed on 21 SSA/Ps, 10 HPs, 10 adenomas, 21 uninvolved colon and 20 control colon specimens. Differential expression and leave-one-out cross validation methods were used to define a unique gene signature of SSA/Ps. Our SSA/P gene signature was evaluated in colon cancer RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify a subtype of colon cancers that may develop from SSA/Ps. A total of 1422 differentially expressed genes were found in SSA/Ps relative to controls. Serrated polyposis syndrome (n=12) and sporadic SSA/Ps (n=9) exhibited almost complete (96%) gene overlap. A 51-gene panel in SSA/P showed similar expression in a subset of TCGA colon cancers with high microsatellite instability (MSI-H). A smaller seven-gene panel showed high sensitivity and specificity in identifying BRAF mutant, CpG island methylator phenotype high (CIMP-H) and MLH1 silenced colon cancers. We describe a unique gene signature in SSA/Ps that identifies a subset of colon cancers likely to develop through the serrated pathway. These gene panels may be utilized for improved differentiation of SSA/Ps from HPs and provide insights into novel molecular pathways altered in colon cancer arising from the serrated pathway. PMID:27026680

Thyroid nodules, polymorphic variants in DNA repair and RET-related genes, and interaction with ionizing radiation exposure from nuclear tests in Kazakhstan

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Sigurdson, Alice J.; Land, Charles E.; Bhatti, Parveen; Pineda, Marbin; Brenner, Alina; Carr, Zhanat; Gusev, Boris I.; Zhumadilov, Zhaxibay; Simon, Steven L.; Bouville, Andre; Rutter, Joni L.; Ron, Elaine; Struewing, Jeffery P.

2010-01-01

Risk factors for thyroid cancer remain largely unknown except for ionizing radiation exposure during childhood and a history of benign thyroid nodules. Because thyroid nodules are more common than thyroid cancers and are associated with thyroid cancer risk, we evaluated several polymorphisms potentially relevant to thyroid tumors and assessed interaction with ionizing radiation exposure to the thyroid gland. Thyroid nodules were detected in 1998 by ultrasound screening of 2997 persons who lived near the Semipalatinsk nuclear test site in Kazakhstan when they were children (1949-62). Cases with thyroid nodules (n=907) were frequency matched (1:1) to those without nodules by ethnicity (Kazakh or Russian), gender, and age at screening. Thyroid gland radiation doses were estimated from fallout deposition patterns, residence history, and diet. We analyzed 23 polymorphisms in 13 genes and assessed interaction with ionizing radiation exposure using likelihood ratio tests (LRT). Elevated thyroid nodule risks were associated with the minor alleles of RET S836S (rs1800862, p = 0.03) and GFRA1 -193C>G (rs not assigned, p = 0.05) and decreased risk with XRCC1 R194W (rs1799782, p-trend = 0.03) and TGFB1 T263I (rs1800472, p = 0.009). Similar patterns of association were observed for a small number of papillary thyroid cancers (n=25). Ionizing radiation exposure to the thyroid gland was associated with significantly increased risk of thyroid nodules (age and gender adjusted excess odds ratio/Gy = 0.30, 95% confidence interval 0.05-0.56), with evidence for interaction by genotype found for XRCC1 R194W (LRT p value = 0.02). Polymorphisms in RET signaling, DNA repair, and proliferation genes may be related to risk of thyroid nodules, consistent with some previous reports on thyroid cancer. Borderline support for gene-radiation interaction was found for a variant in XRCC1, a key base excision repair protein. Other pathways, such as genes in double strand break repair, apoptosis, and

Polymorphic variations in the FANCA gene in high‐risk non‐BRCA1/2 breast cancer individuals from the French Canadian population

OpenAIRE

Litim, Nadhir; Labrie, Yvan; Desjardins, Sylvie; Ouellette, Geneviève; Plourde, Karine; Belleau, Pascal; Durocher, Francine

2012-01-01

The majority of genes associated with breast cancer susceptibility, including BRCA1 and BRCA2 genes, are involved in DNA repair mechanisms. Moreover, among the genes recently associated with an increased susceptibility to breast cancer, four are Fanconi Anemia (FA) genes: FANCD1/BRCA2, FANCJ/BACH1/BRIP1, FANCN/PALB2 and FANCO/RAD51C. FANCA is implicated in DNA repair and has been shown to interact directly with BRCA1. It has been proposed that the formation of FANCA/G (dependent upon the phos...

ANALYSES ON DIFFERENTIALLY EXPRESSED GENES ASSOCIATED WITH HUMAN BREAST CANCER

Institute of Scientific and Technical Information of China (English)

MENG Xu-li; DING Xiao-wen; XU Xiao-hong

2006-01-01

Objective: To investigate the molecular etiology of breast cancer by way of studying the differential expression and initial function of the related genes in the occurrence and development of breast cancer. Methods: Two hundred and eighty-eight human tumor related genes were chosen for preparation of the oligochips probe. mRNA was extracted from 16 breast cancer tissues and the corresponding normal breast tissues, and cDNA probe was prepared through reverse-transcription and hybridized with the gene chip. A laser focused fluorescent scanner was used to scan the chip. The different gene expressions were thereafter automatically compared and analyzed between the two sample groups. Cy3/Cy5＞3.5 meant significant up-regulation. Cy3/Cy5＜0.25 meant significant down-regulation. Results: The comparison between the breast cancer tissues and their corresponding normal tissues showed that 84 genes had differential expression in the Chip. Among the differently expressed genes, there were 4 genes with significant down-regulation and 6 with significant up-regulation. Compared with normal breast tissues, differentially expressed genes did partially exist in the breast cancer tissues. Conclusion: Changes in multi-gene expression regulations take place during the occurrence and development of breast cancer; and the research on related genes can help understanding the mechanism of tumor occurrence.

Interactions between environmental factors and melatonin receptor type 1A polymorphism in relation to oral cancer susceptibility and clinicopathologic development.

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Feng-Yan Lin

Full Text Available The purpose of this study was to explore the combined effect of melatonin receptor type 1A (MTNR1A gene polymorphisms and exposure to environmental carcinogens on the susceptibility and clinicopathological characteristics of oral cancer.Three polymorphisms of the MTNR1A gene from 618 patients with oral cancer and 560 non-cancer controls were analyzed by real-time polymerase chain reaction (PCR. The CTA haplotype of the studied MTNR1A polymorphisms (rs2119882, rs13140012, rs6553010 was related to a higher risk of oral cancer. Moreover, MTNR1A gene polymorphisms exhibited synergistic effects of environmental factors (betel quid and tobacco use on the susceptibility of oral cancer. Finally, oral-cancer patients with betel quid-chewing habit who had T/T allele of MTNR1A rs13140012 were at higher risk for developing an advanced clinical stage and lymph node metastasis.These results support gene-environment interactions of MTNR1A polymorphisms with smoking and betel quid-chewing habits possibly altering oral-cancer susceptibility and metastasis.

Animal models of gene-environment interactions in schizophrenia.

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Ayhan, Yavuz; Sawa, Akira; Ross, Christopher A; Pletnikov, Mikhail V

2009-12-07

The pathogenesis of schizophrenia and related mental illnesses likely involves multiple interactions between susceptibility genes of small effects and environmental factors. Gene-environment interactions occur across different stages of neurodevelopment to produce heterogeneous clinical and pathological manifestations of the disease. The main obstacle for mechanistic studies of gene-environment interplay has been the paucity of appropriate experimental systems for elucidating the molecular pathways that mediate gene-environment interactions relevant to schizophrenia. Recent advances in psychiatric genetics and a plethora of experimental data from animal studies allow us to suggest a new approach to gene-environment interactions in schizophrenia. We propose that animal models based on identified genetic mutations and measurable environment factors will help advance studies of the molecular mechanisms of gene-environment interplay.

Relevance of Fusion Genes in Pediatric Cancers: Toward Precision Medicine

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Célia Dupain

2017-03-01

Full Text Available Pediatric cancers differ from adult tumors, especially by their very low mutational rate. Therefore, their etiology could be explained in part by other oncogenic mechanisms such as chromosomal rearrangements, supporting the possible implication of fusion genes in the development of pediatric cancers. Fusion genes result from chromosomal rearrangements leading to the juxtaposition of two genes. Consequently, an abnormal activation of one or both genes is observed. The detection of fusion genes has generated great interest in basic cancer research and in the clinical setting, since these genes can lead to better comprehension of the biological mechanisms of tumorigenesis and they can also be used as therapeutic targets and diagnostic or prognostic biomarkers. In this review, we discuss the molecular mechanisms of fusion genes and their particularities in pediatric cancers, as well as their relevance in murine models and in the clinical setting. We also point out the difficulties encountered in the discovery of fusion genes. Finally, we discuss future perspectives and priorities for finding new innovative therapies in childhood cancer.

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity

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Sucheston Lara

2010-09-01

Full Text Available Abstract Background Multifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods. Methods The k-way interaction information (KWII metric for identifying variable combinations involved in gene-gene interactions (GGI was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR, restricted partitioning method (RPM and logistic regression. Results The power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression. Conclusions Information theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.

Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms

NARCIS (Netherlands)

Van Assche, Evelien; Moons, Tim; Cinar, Ozan; Viechtbauer, Wolfgang; Oldehinkel, Albertine J.; Van Leeuwen, Karla; Verschueren, Karine; Colpin, Hilde; Lambrechts, Diether; Van den Noortgate, Wim; Goossens, Luc; Claes, Stephan; van Winkel, Ruud

2017-01-01

BACKGROUND: Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric

Preliminary screening of the radiosensitivity-associated genes on colorectal cancer

International Nuclear Information System (INIS)

Xing Chungen; Yang Xiaodong; Zhou Liying; Wu Yongyou; Jiang Yinfen; Dai Hong; Lv Xiaodong; Gong Wei

2007-01-01

The screening of radiosensitive genes of human colorectal cancer was made by gene chip. Two human colorectal cancer cell lines LOVO and SW480 were cultivated and the total RNA was extracted from at least lxl0 7 cells. Then the gene expression profiling was performed by HG-U133 Plus 2.0 Array and the difference of gene expression has been analyzed. The results shows that there are 16882 genes expressed in LOVO cell and 17114 genes expressed in SW480 cell through gene expression profiling. It has been found that the genes with 2-fold expressed differentially include 908 genes up-regulated and 1312 genes down-regulated. The same genes, such as Fas and NFkB which is up-regulated, Caspas6, and RAD21 which is down-regulated, have been proved to be related to radiosensitivity. The genes with high expression level including CEACAM5, THBS1, SERPINE2, ARL7, HPGD in LOVO cell may also be related to the radiosensitivity. And the genes with high expression level including SCD, NQ01, LYZ, KRT20, ATP1B1 in SW480 cell may be related to the radioresistance of human colorectal cancer. It could be concluded that the radiosensitivity of colorectal cancer can be reflected from gene and protein expression level. And gene expression profiling is a fast and sensitive tool to predict the radiosensitivity and screen radiosensitive genes of colorectal cancer. (authors)

Id-1 gene and gene products as therapeutic targets for treatment of breast cancer and other types of carcinoma

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Desprez, Pierre-Yves; Campisi, Judith

2014-08-19

A method for treatment of breast cancer and other types of cancer. The method comprises targeting and modulating Id-1 gene expression, if any, for the Id-1 gene, or gene products in breast or other epithelial cancers in a patient by delivering products that modulate Id-1 gene expression. When expressed, Id-1 gene is a prognostic indicator that cancer cells are invasive and metastatic.

RRHGE: A Novel Approach to Classify the Estrogen Receptor Based Breast Cancer Subtypes

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Ashish Saini

2014-01-01

Full Text Available Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

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Kwon, Minseok; Leem, Sangseob; Yoon, Joon; Park, Taesung

2018-03-19

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer

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Duffy, Supipi; Fam, Hok Khim; Wang, Yi Kan; Styles, Erin B.; Kim, Jung-Hyun; Ang, J. Sidney; Singh, Tejomayee; Larionov, Vladimir; Shah, Sohrab P.; Andrews, Brenda; Boerkoel, Cornelius F.; Hieter, Philip

2016-01-01

Somatic copy number amplification and gene overexpression are common features of many cancers. To determine the role of gene overexpression on chromosome instability (CIN), we performed genome-wide screens in the budding yeast for yeast genes that cause CIN when overexpressed, a phenotype we refer to as dosage CIN (dCIN), and identified 245 dCIN genes. This catalog of genes reveals human orthologs known to be recurrently overexpressed and/or amplified in tumors. We show that two genes, TDP1, a tyrosyl-DNA-phosphdiesterase, and TAF12, an RNA polymerase II TATA-box binding factor, cause CIN when overexpressed in human cells. Rhabdomyosarcoma lines with elevated human Tdp1 levels also exhibit CIN that can be partially rescued by siRNA-mediated knockdown of TDP1. Overexpression of dCIN genes represents a genetic vulnerability that could be leveraged for selective killing of cancer cells through targeting of an unlinked synthetic dosage lethal (SDL) partner. Using SDL screens in yeast, we identified a set of genes that when deleted specifically kill cells with high levels of Tdp1. One gene was the histone deacetylase RPD3, for which there are known inhibitors. Both HT1080 cells overexpressing hTDP1 and rhabdomyosarcoma cells with elevated levels of hTdp1 were more sensitive to histone deacetylase inhibitors valproic acid (VPA) and trichostatin A (TSA), recapitulating the SDL interaction in human cells and suggesting VPA and TSA as potential therapeutic agents for tumors with elevated levels of hTdp1. The catalog of dCIN genes presented here provides a candidate list to identify genes that cause CIN when overexpressed in cancer, which can then be leveraged through SDL to selectively target tumors. PMID:27551064

Inferring causal genomic alterations in breast cancer using gene expression data

Science.gov (United States)

2011-01-01

Background One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies. Results We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments. Conclusions To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data. PMID:21806811

Comparative effects of DHEA and DHT on gene expression in human LNCaP prostate cancer cells.

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Steele, Vernon E; Arnold, Julia T; Lei, Hanh; Izmirlian, Grant; Blackman, Marc R

2006-01-01

DHEA is widely used as a dietary supplement in older men. Because DHEA can be converted to androgens or estrogens, such use may promote prostate cancer. In this study, the effects of DHEA were compared with those of DHT using gene expression array profiles in human LNCaP prostate cancer cells. LNCaP cells were exposed to DHEA (300 nM), DHT (300 nM), or vehicle for 48 h, and mRNA expression was measured using Affymetrix HU-95 gene chips. Gene expression values were sorted in ascending order on the p-values corresponding to the extent of differential RNA expression between control and either hormone treatment. S100 calcium binding protein, neurotensin, 24-dehydrocholesterol reductase, and anterior-gradient 2 homologue were the four most differentially expressed genes (p-values all DHT treatment (p DHT were used for pathway analysis. DHT decreased expression of more genes involved in intercellular communication, signal transduction, nucleic acid binding and transport, and in structural components, such as myosin and golgin, than DHEA. These data revealed consistent, measurable changes in gene expression patterns following treatment of LNCaP prostate cancer cells with DHEA and DHT. Understanding the mechanisms of DHEA versus DHT actions in the prostate may help clarify the separate and interactive effects of androgenic and estrogenic actions in prostate cancer progression.

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

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Yang X

2018-03-01

Full Text Available Xiao Yang,1 Shaoming Zhu,2 Li Li,3 Li Zhang,1 Shu Xian,1 Yanqing Wang,1 Yanxiang Cheng1 1Department of Obstetrics and Gynecology, 2Department of Urology, Renmin Hospital of Wuhan University, 3Department of Pharmacology, Wuhan University Health Science Center, Wuhan, Hubei, People’s Republic of China Background: The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms. Results: The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs and were deeply analyzed by bioinformatics methods. The gene ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the

F-box protein interactions with the hallmark pathways in cancer.

Science.gov (United States)

Randle, Suzanne J; Laman, Heike

2016-02-01

F-box proteins (FBP) are the substrate specifying subunit of Skp1-Cul1-FBP (SCF)-type E3 ubiquitin ligases and are responsible for directing the ubiquitination of numerous proteins essential for cellular function. Due to their ability to regulate the expression and activity of oncogenes and tumour suppressor genes, FBPs themselves play important roles in cancer development and progression. In this review, we provide a comprehensive overview of FBPs and their targets in relation to their interaction with the hallmarks of cancer cell biology, including the regulation of proliferation, epigenetics, migration and invasion, metabolism, angiogenesis, cell death and DNA damage responses. Each cancer hallmark is revealed to have multiple FBPs which converge on common signalling hubs or response pathways. We also highlight the complex regulatory interplay between SCF-type ligases and other ubiquitin ligases. We suggest six highly interconnected FBPs affecting multiple cancer hallmarks, which may prove sensible candidates for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Nature, Nurture, and Cancer Risks: Genetic and Nutritional Contributions to Cancer.

Science.gov (United States)

Theodoratou, Evropi; Timofeeva, Maria; Li, Xue; Meng, Xiangrui; Ioannidis, John P A

2017-08-21

It is speculated that genetic variants are associated with differential responses to nutrients (known as gene-diet interactions) and that these variations may be linked to different cancer risks. In this review, we critically evaluate the evidence across 314 meta-analyses of observational studies and randomized controlled trials of dietary risk factors and the five most common cancers (breast, lung, prostate, colorectal, and stomach). We also critically evaluate the evidence across 13 meta-analyses of observational studies of gene-diet interactions for the same cancers. Convincing evidence for association was found only for the intake of alcohol and whole grains in relation to colorectal cancer risk. Three nutrient associations had highly suggestive evidence and another 15 associations had suggestive evidence. Among the examined gene-diet interactions, only one had moderately strong evidence.

A comparison of 12-gene colon cancer assay gene expression in African American and Caucasian patients with stage II colon cancer.

Science.gov (United States)

Govindarajan, Rangaswamy; Posey, James; Chao, Calvin Y; Lu, Ruixiao; Jadhav, Trafina; Javed, Ahmed Y; Javed, Awais; Mahmoud, Fade A; Osarogiagbon, Raymond U; Manne, Upender

2016-06-18

African American (AA) colon cancer patients have a worse prognosis than Caucasian (CA) colon cancer patients, however, reasons for this disparity are not well understood. To determine if tumor biology might contribute to differential prognosis, we measured recurrence risk and gene expression using the Oncotype DX® Colon Cancer Assay (12-gene assay) and compared the Recurrence Score results and gene expression profiles between AA patients and CA patients with stage II colon cancer. We retrieved demographic, clinical, and archived tumor tissues from stage II colon cancer patients at four institutions. The 12-gene assay and mismatch repair (MMR) status were performed by Genomic Health (Redwood City, California). Student's t-test and the Wilcoxon rank sum test were used to compare Recurrence Score data and gene expression data from AA and CA patients (SAS Enterprise Guide 5.1). Samples from 122 AA and 122 CA patients were analyzed. There were 118 women (63 AA, 55 CA) and 126 men (59 AA, 67 CA). Median age was 66 years for AA patients and 68 for CA patients. Age, gender, year of surgery, pathologic T-stage, tumor location, the number of lymph nodes examined, lymphovascular invasion, and MMR status were not significantly different between groups (p = 0.93). The mean Recurrence Score result for AA patients (27.9 ± 12.8) and CA patients (28.1 ± 11.8) was not significantly different and the proportions of patients with high Recurrence Score values (≥41) were similar between the groups (17/122 AA; 15/122 CA). None of the gene expression variables, either single genes or gene groups (cell cycle group, stromal group, BGN1, FAP, INHBA1, Ki67, MYBL2, cMYC and GADD45B), was significantly different between the racial groups. After controlling for clinical and pathologic covariates, the means and distributions of Recurrence Score results and gene expression profiles showed no statistically significant difference between patient groups. The distribution of

A comparison of 12-gene colon cancer assay gene expression in African American and Caucasian patients with stage II colon cancer

International Nuclear Information System (INIS)

Govindarajan, Rangaswamy; Posey, James; Chao, Calvin Y.; Lu, Ruixiao; Jadhav, Trafina; Javed, Ahmed Y.; Javed, Awais; Mahmoud, Fade A.; Osarogiagbon, Raymond University; Manne, Upender

2016-01-01

African American (AA) colon cancer patients have a worse prognosis than Caucasian (CA) colon cancer patients, however, reasons for this disparity are not well understood. To determine if tumor biology might contribute to differential prognosis, we measured recurrence risk and gene expression using the Oncotype DX® Colon Cancer Assay (12-gene assay) and compared the Recurrence Score results and gene expression profiles between AA patients and CA patients with stage II colon cancer. We retrieved demographic, clinical, and archived tumor tissues from stage II colon cancer patients at four institutions. The 12-gene assay and mismatch repair (MMR) status were performed by Genomic Health (Redwood City, California). Student’s t-test and the Wilcoxon rank sum test were used to compare Recurrence Score data and gene expression data from AA and CA patients (SAS Enterprise Guide 5.1). Samples from 122 AA and 122 CA patients were analyzed. There were 118 women (63 AA, 55 CA) and 126 men (59 AA, 67 CA). Median age was 66 years for AA patients and 68 for CA patients. Age, gender, year of surgery, pathologic T-stage, tumor location, the number of lymph nodes examined, lymphovascular invasion, and MMR status were not significantly different between groups (p = 0.93). The mean Recurrence Score result for AA patients (27.9 ± 12.8) and CA patients (28.1 ± 11.8) was not significantly different and the proportions of patients with high Recurrence Score values (≥41) were similar between the groups (17/122 AA; 15/122 CA). None of the gene expression variables, either single genes or gene groups (cell cycle group, stromal group, BGN1, FAP, INHBA1, Ki67, MYBL2, cMYC and GADD45B), was significantly different between the racial groups. After controlling for clinical and pathologic covariates, the means and distributions of Recurrence Score results and gene expression profiles showed no statistically significant difference between patient groups. The distribution of Recurrence Score

DDEC: Dragon database of genes implicated in esophageal cancer

International Nuclear Information System (INIS)

Essack, Magbubah; Radovanovic, Aleksandar; Schaefer, Ulf; Schmeier, Sebastian; Seshadri, Sundararajan V; Christoffels, Alan; Kaur, Mandeep; Bajic, Vladimir B

2009-01-01

Esophageal cancer ranks eighth in order of cancer occurrence. Its lethality primarily stems from inability to detect the disease during the early organ-confined stage and the lack of effective therapies for advanced-stage disease. Moreover, the understanding of molecular processes involved in esophageal cancer is not complete, hampering the development of efficient diagnostics and therapy. Efforts made by the scientific community to improve the survival rate of esophageal cancer have resulted in a wealth of scattered information that is difficult to find and not easily amendable to data-mining. To reduce this gap and to complement available cancer related bioinformatic resources, we have developed a comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer) with esophageal cancer related information, as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. Manually curated 529 genes differentially expressed in EC are contained in the database. We extracted and analyzed the promoter regions of these genes and complemented gene-related information with transcription factors that potentially control them. We further, precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. The resulting database, DDEC, has a useful feature to display potential associations that are rarely reported and thus difficult to identify. Moreover, DDEC enables inspection of potentially new 'association hypotheses' generated based on the precompiled reports. We hope that this resource will serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in EC genetics. DDEC is freely accessible to academic

Effects of interactions between common genetic variants and alcohol consumption on colorectal cancer risk.

Science.gov (United States)

Song, Nan; Shin, Aesun; Oh, Jae Hwan; Kim, Jeongseon

2018-01-19

Genome-wide association studies (GWAS) have identified approximately 40 common genetic loci associated with colorectal cancer risk. To investigate possible gene-environment interactions (GEIs) between GWAS-identified single-nucleotide polymorphisms (SNPs) and alcohol consumption with respect to colorectal cancer, a hospital-based case-control study was conducted. Higher levels of alcohol consumption as calculated based on a standardized definition of a drink (1 drink=12.5g of ethanol) were associated with increased risk of colorectal cancer (OR=2.47, 95% CI=1.62-3.76 for heavy drinkers [>50g/day] compared to never drinkers; p trend colorectal cancer associated with the G allele of rs6687758 tended to increase among individuals in the heavier alcohol consumption strata. A statistically significant association between rs6687758 and colorectal cancer risk was observed among moderate alcohol drinkers who consumed between >12.5 and ≤50g of alcohol per day (OR=1.46, 95% CI=1.01-2.11). A total of 2,109 subjects (703 colorectal cancer patients and 1,406 healthy controls) were recruited from the Korean National Cancer Center. For genotyping, 30 GWAS-identified SNPs were selected. A logistic regression model was used to evaluate associations of SNPs and alcohol consumption with colorectal cancer risk. We also tested GEIs between SNPs and alcohol consumption using a logistic model with multiplicative interaction terms. Our results suggest that SNP rs6687758 at 1q41 may interact with alcohol consumption in the etiology of colorectal cancer.

Phase I metabolic genes and risk of lung cancer: multiple polymorphisms and mRNA expression.

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Melissa Rotunno

2009-05-01

Full Text Available Polymorphisms in genes coding for enzymes that activate tobacco lung carcinogens may generate inter-individual differences in lung cancer risk. Previous studies had limited sample sizes, poor exposure characterization, and a few single nucleotide polymorphisms (SNPs tested in candidate genes. We analyzed 25 SNPs (some previously untested in 2101 primary lung cancer cases and 2120 population controls from the Environment And Genetics in Lung cancer Etiology (EAGLE study from six phase I metabolic genes, including cytochrome P450s, microsomal epoxide hydrolase, and myeloperoxidase. We evaluated the main genotype effects and genotype-smoking interactions in lung cancer risk overall and in the major histology subtypes. We tested the combined effect of multiple SNPs on lung cancer risk and on gene expression. Findings were prioritized based on significance thresholds and consistency across different analyses, and accounted for multiple testing and prior knowledge. Two haplotypes in EPHX1 were significantly associated with lung cancer risk in the overall population. In addition, CYP1B1 and CYP2A6 polymorphisms were inversely associated with adenocarcinoma and squamous cell carcinoma risk, respectively. Moreover, the association between CYP1A1 rs2606345 genotype and lung cancer was significantly modified by intensity of cigarette smoking, suggesting an underlying dose-response mechanism. Finally, increasing number of variants at CYP1A1/A2 genes revealed significant protection in never smokers and risk in ever smokers. Results were supported by differential gene expression in non-tumor lung tissue samples with down-regulation of CYP1A1 in never smokers and up-regulation in smokers from CYP1A1/A2 SNPs. The significant haplotype associations emphasize that the effect of multiple SNPs may be important despite null single SNP-associations, and warrants consideration in genome-wide association studies (GWAS. Our findings emphasize the necessity of post

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

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Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

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Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

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Ching Lee Koo

2013-01-01

Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

Gene Expression Correlation for Cancer Diagnosis: A Pilot Study

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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.

Energy homeostasis genes and breast cancer risk: The influence of ancestry, body size, and menopausal status, the breast cancer health disparities study.

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Slattery, Martha L; Lundgreen, Abbie; Hines, Lisa; Wolff, Roger K; Torres-Mejia, Gabriella; Baumgartner, Kathy N; John, Esther M

2015-12-01

Obesity and breast cancer risk is multifaceted and genes associated with energy homeostasis may modify this relationship. We evaluated 10 genes that have been associated with obesity and energy homeostasis to determine their association with breast cancer risk in Hispanic/Native American (2111 cases, 2597 controls) and non-Hispanic white (1481 cases, 1585 controls) women. Cholecystokinin (CCK) rs747455 and proopiomelanocortin (POMC) rs6713532 and rs7565877 (for low Indigenous American (IA) ancestry); CCK rs8192472 and neuropeptide Y (NYP) rs16141 and rs14129 (intermediate IA ancestry); and leptin receptor (LEPR) rs11585329 (high IA ancestry) were strongly associated with multiple indicators of body size. There were no significant associations with breast cancer risk between genes and SNPs overall. However, LEPR was significantly associated with breast cancer risk among women with low IA ancestry (PARTP=0.024); POMC was significantly associated with breast cancer risk among women with intermediate (PARTP=0.015) and high (PARTP=0.012) IA ancestry. The overall pathway was statistically significant for pre-menopausal women with low IA ancestry (PARTP=0.05), as was cocaine and amphetamine regulated transcript protein (CARTPT) (PARTP=0.014) and ghrelin (GHRL) (PARTP=0.007). POMC was significantly associated with breast cancer risk among post-menopausal women with higher IA ancestry (PARTP=0.005). Three SNPs in LEPR (rs6704167, rs17412175, and rs7626141), and adiponectin (ADIPOQ); rs822391) showed significant 4-way interactions (GxExMenopausexAncestry) for multiple indicators of body size among pre-menopausal women. Energy homeostasis genes were associated with breast cancer risk; menopausal status, body size, and genetic ancestry influenced this relationship. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes.

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I-Hsuan Lin

Full Text Available Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs and the hypomethylation of the megabase-sized partially methylated domains (PMDs are the major forms of methylation changes observed in breast tumor samples. Hierarchical clustering of HMR revealed tumor-specific hypermethylated clusters and differential methylated enhancers specific to normal or breast cancer cell lines. Joint analysis of gene expression and DNA methylation data of normal breast and breast cancer cells identified differentially methylated and expressed genes associated with breast and/or ovarian cancers in cancer-specific HMR clusters. Furthermore, aberrant patterns of X-chromosome inactivation (XCI was found in breast cancer cell lines as well as breast tumor samples in the TCGA BRCA (breast invasive carcinoma dataset. They were characterized with differentially hypermethylated XIST promoter, reduced expression of XIST, and over-expression of hypomethylated X-linked genes. High expressions of these genes were significantly associated with lower survival rates in breast cancer patients. Comprehensive analysis of the normal and breast tumor methylomes suggests selective targeting of DNA methylation changes during breast cancer progression. The weak causal relationship between DNA methylation and gene expression observed in this study is evident of more complex role of DNA methylation in the regulation of gene expression in human epigenetics that deserves further investigation.

Vitamin D metabolic pathway genes and pancreatic cancer risk.

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Hannah Arem

Full Text Available Evidence on the association between vitamin D status and pancreatic cancer risk is inconsistent. This inconsistency may be partially attributable to variation in vitamin D regulating genes. We selected 11 vitamin D-related genes (GC, DHCR7, CYP2R1, VDR, CYP27B1, CYP24A1, CYP27A1, RXRA, CRP2, CASR and CUBN totaling 213 single nucleotide polymorphisms (SNPs, and examined associations with pancreatic adenocarcinoma. Our study included 3,583 pancreatic cancer cases and 7,053 controls from the genome-wide association studies of pancreatic cancer PanScans-I-III. We used the Adaptive Joint Test and the Adaptive Rank Truncated Product statistic for pathway and gene analyses, and unconditional logistic regression for SNP analyses, adjusting for age, sex, study and population stratification. We examined effect modification by circulating vitamin D concentration (≤50, >50 nmol/L for the most significant SNPs using a subset of cohort cases (n = 713 and controls (n = 878. The vitamin D metabolic pathway was not associated with pancreatic cancer risk (p = 0.830. Of the individual genes, none were associated with pancreatic cancer risk at a significance level of p<0.05. SNPs near the VDR (rs2239186, LRP2 (rs4668123, CYP24A1 (rs2762932, GC (rs2282679, and CUBN (rs1810205 genes were the top SNPs associated with pancreatic cancer (p-values 0.008-0.037, but none were statistically significant after adjusting for multiple comparisons. Associations between these SNPs and pancreatic cancer were not modified by circulating concentrations of vitamin D. These findings do not support an association between vitamin D-related genes and pancreatic cancer risk. Future research should explore other pathways through which vitamin D status might be associated with pancreatic cancer risk.

Suppression subtractive hybridization identified differentially expressed genes in lung adenocarcinoma: ERGIC3 as a novel lung cancer-related gene

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Wu, Mingsong; Tu, Tao; Huang, Yunchao; Cao, Yi

2013-01-01

To understand the carcinogenesis caused by accumulated genetic and epigenetic alterations and seek novel biomarkers for various cancers, studying differentially expressed genes between cancerous and normal tissues is crucial. In the study, two cDNA libraries of lung cancer were constructed and screened for identification of differentially expressed genes. Two cDNA libraries of differentially expressed genes were constructed using lung adenocarcinoma tissue and adjacent nonmalignant lung tissue by suppression subtractive hybridization. The data of the cDNA libraries were then analyzed and compared using bioinformatics analysis. Levels of mRNA and protein were measured by quantitative real-time polymerase chain reaction (q-RT-PCR) and western blot respectively, as well as expression and localization of proteins were determined by immunostaining. Gene functions were investigated using proliferation and migration assays after gene silencing and gene over-expression. Two libraries of differentially expressed genes were obtained. The forward-subtracted library (FSL) and the reverse-subtracted library (RSL) contained 177 and 59 genes, respectively. Bioinformatic analysis demonstrated that these genes were involved in a wide range of cellular functions. The vast majority of these genes were newly identified to be abnormally expressed in lung cancer. In the first stage of the screening for 16 genes, we compared lung cancer tissues with their adjacent non-malignant tissues at the mRNA level, and found six genes (ERGIC3, DDR1, HSP90B1, SDC1, RPSA, and LPCAT1) from the FSL were significantly up-regulated while two genes (GPX3 and TIMP3) from the RSL were significantly down-regulated (P < 0.05). The ERGIC3 protein was also over-expressed in lung cancer tissues and cultured cells, and expression of ERGIC3 was correlated with the differentiated degree and histological type of lung cancer. The up-regulation of ERGIC3 could promote cellular migration and proliferation in vitro. The

The Interaction of TXNIP and AFq1 Genes Increases the Susceptibility of Schizophrenia.

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Su, Yousong; Ding, Wenhua; Xing, Mengjuan; Qi, Dake; Li, Zezhi; Cui, Donghong

2017-08-01

Although previous studies showed the reduced risk of cancer in patients with schizophrenia, whether patients with schizophrenia possess genetic factors that also contribute to tumor suppressor is still unknown. In the present study, based on our previous microarray data, we focused on the tumor suppressor genes TXNIP and AF1q, which differentially expressed in patients with schizophrenia. A total of 413 patients and 578 healthy controls were recruited. We found no significant differences in genotype, allele, or haplotype frequencies at the selected five single nucleotide polymorphisms (SNPs) (rs2236566 and rs7211 in TXNIP gene; rs10749659, rs2140709, and rs3738481 in AF1q gene) between patients with schizophrenia and controls. However, we found the association between the interaction of TXNIP and AF1q with schizophrenia by using the MDR method followed by traditional statistical analysis. The best gene-gene interaction model identified was a three-locus model TXNIP (rs2236566, rs7211)-AF1q (rs2140709). After traditional statistical analysis, we found the high-risk genotype combination was rs2236566 (GG)-rs7211(CC)-rs2140709(CC) (OR = 1.35 [1.03-1.76]). The low-risk genotype combination was rs2236566 (GT)-rs7211(CC)-rs2140709(CC) (OR = 0.67 [0.49-0.91]). Our finding suggested statistically significant role of interaction of TXNIP and AF1q polymorphisms (TXNIP-rs2236566, TXNIP-rs7211, and AF1q-rs2769605) in schizophrenia susceptibility.

Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

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Baoman Wang

2015-01-01

Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

Bioinformatics analysis of breast cancer bone metastasis related gene-CXCR4

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Heng-Wei Zhang; Xian-Fu Sun; Ya-Ning He; Jun-Tao Li; Xu-Hui Guo; Hui Liu

2013-01-01

Objective: To analyze breast cancer bone metastasis related gene-CXCR4. Methods: This research screened breast cancer bone metastasis related genes by high-flux gene chip. Results:It was found that the expressions of 396 genes were different including 165 up-regulations and 231 down-regulations. The expression of chemokine receptor CXCR4 was obviously up-regulated in the tissue with breast cancer bone metastasis. Compared with the tissue without bone metastasis, there was significant difference, which indicated that CXCR4 played a vital role in breast cancer bone metastasis. Conclusions: The bioinformatics analysis of CXCR4 can provide a certain basis for the occurrence and diagnosis of breast cancer bone metastasis, target gene therapy and evaluation of prognosis.

Co-Targeting Prostate Cancer Epithelium and Bone Stroma by Human Osteonectin-Promoter-Mediated Suicide Gene Therapy Effectively Inhibits Androgen-Independent Prostate Cancer Growth.

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Shian-Ying Sung

Full Text Available Stromal-epithelial interaction has been shown to promote local tumor growth and distant metastasis. We sought to create a promising gene therapy approach that co-targets cancer and its supporting stromal cells for combating castration-resistant prostate tumors. Herein, we demonstrated that human osteonectin is overexpressed in the prostate cancer epithelium and tumor stroma in comparison with their normal counterpart. We designed a novel human osteonectin promoter (hON-522E containing positive transcriptional regulatory elements identified in both the promoter and exon 1 region of the human osteonectin gene. In vitro reporter assays revealed that the hON-522E promoter is highly active in androgen receptor negative and metastatic prostate cancer and bone stromal cells compared to androgen receptor-positive prostate cancer cells. Moreover, in vivo prostate-tumor-promoting activity of the hON-522E promoter was confirmed by intravenous administration of an adenoviral vector containing the hON-522E promoter-driven luciferase gene (Ad-522E-Luc into mice bearing orthotopic human prostate tumor xenografts. In addition, an adenoviral vector with the hON-522E-promoter-driven herpes simplex virus thymidine kinase gene (Ad-522E-TK was highly effective against the growth of androgen-independent human prostate cancer PC3M and bone stromal cell line in vitro and in pre-established PC3M tumors in vivo upon addition of the prodrug ganciclovir. Because of the heterogeneity of human prostate tumors, hON-522E promoter-mediated gene therapy has the potential for the treatment of hormone refractory and bone metastatic prostate cancers.

Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer.

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Merlino, Giuseppe; Miodini, Patrizia; Callari, Maurizio; D'Aiuto, Francesca; Cappelletti, Vera; Daidone, Maria Grazia

2017-10-01

None of the clinically relevant gene expression signatures available for breast cancer were specifically developed to capture the influence of the microenvironment on tumor cells. Here, we attempted to build subtype-specific signatures derived from an in vitro model reproducing tumor cell modifications after interaction with activated or normal stromal cells. Gene expression signatures derived from HER2+, luminal, and basal breast cancer cell lines (treated by normal fibroblasts or cancer-associated fibroblasts conditioned media) were evaluated in clinical tumors by in silico analysis on published gene expression profiles (GEPs). Patients were classified as microenvironment-positive (μENV+ve), that is, with tumors showing molecular profiles suggesting activation by the stroma, or microenvironment-negative (μENV-ve) based on correlation of their tumors' GEP with the respective subtype-specific signature. Patients with estrogen receptor alpha (ER)+/HER2-/μENV+ve tumors were characterized by 2.5-fold higher risk of developing distant metastases (HR = 2.546; 95% CI: 1.751-3.701, P = 9.84E-07), while μENV status did not affect, or only suggested the risk of distant metastases, in women with HER2+ (HR = 1.541; 95% CI: 0.788-3.012, P = 0.206) or ER-/HER2- tumors (HR = 1.894; 95% CI: 0.938-3.824; P = 0.0747), respectively. In ER+/HER2- tumors, the μENV status remained significantly associated with metastatic progression (HR = 2.098; CI: 1.214-3.624; P = 0.00791) in multivariable analysis including size, age, and Genomic Grade Index. Validity of our in vitro model was also supported by in vitro biological endpoints such as cell growth (MTT assay) and migration/invasion (Transwell assay). In vitro-derived gene signatures tracing the bidirectional interaction with cancer activated fibroblasts are subtype-specific and add independent prognostic information to classical prognostic variables in women with ER+/HER2- tumors. © 2017 The Authors. Published

Inference of cancer-specific gene regulatory networks using soft computing rules.

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Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

Screening for interaction effects in gene expression data.

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Peter J Castaldi

Full Text Available Expression quantitative trait (eQTL studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.

Association of Genetic Polymorphism in the Interleukin-8 Gene with Risk of Oral Cancer and Its Correlation with Pain.

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Singh, Prithvi Kumar; Chandra, Girish; Bogra, Jaishri; Gupta, Rajni; Kumar, Vijay; Hussain, Syed Rizwan; Jain, Amita; Mahdi, Abbas Ali; Ahmad, Mohammad Kaleem

2016-02-01

Oral cancer is a multifactorial disease process and involves complex interactions between gene to gene and gene to environmental factors. Interleukin 8 (IL-8), a pro-inflammatory cytokine, having angiogenic activity with elevated expression in tumor cells, is reported to play an essential role in oral cancer development. This study was conducted with the aim to investigate the role of IL-8 (-A251T) gene polymorphism in susceptibility, progression, and self-reporting pain in oral cancer. The single nucleotide polymorphisms of the IL-8 (-A251T) gene were screened in 300 patients with oral cancer and 300 healthy controls, by polymerase chain reaction-restriction fragment length polymorphism. Genotype and allele frequencies were evaluated by chi-square test and odds ratio (OR) with 95% confidence intervals (CIs) were used to evaluate the strength of associations. The results of the study demonstrated that IL-8 (-A251T) gene polymorphism was significantly associated with susceptibility of oral cancer, whereas its correlation with clinico-pathological status or pain due to oral cancer could not be established. The AT heterozygous (OR 5.31; CI 3.38-8.34; p 0.0001) and AA homozygous (OR 2.89; CI 1.76-4.75; p 0.0001) had a greater risk for oral cancer compared to TT homozygous. Furthermore, significantly increased values of A allele frequencies compared to T allele were observed in all patients (OR 1.56; CI 1.24-1.96; p 0.0002). Tobacco chewing and smoking were also found to influence the development of oral cancer and increased the incidence of pain in oral cancer patients. The findings of this study suggest that the IL-8 (-A251T) gene polymorphism may be associated with increased risk of oral cancer.

Modulation of Colorectal Cancer Risk by Polymorphisms in 51Gln/His, 64Ile/Val, and 148Asp/Glu of APEX Gene; 23Gly/Ala of XPA Gene; and 689Ser/Arg of ERCC4 Gene

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L. Dziki

2017-01-01

Full Text Available Polymorphisms in DNA repair genes may affect the activity of the BER (base excision repair and NER (nucleotide excision repair systems. Using DNA isolated from blood taken from patients (n=312 and a control group (n=320 with CRC, we have analyzed the polymorphisms of selected DNA repair genes and we have demonstrated that genotypes 51Gln/His and 148Asp/Glu of APEX gene and 23Gly/Ala of XPA gene may increase the risk of colorectal cancer. At the same time analyzing the gene-gene interactions, we suggest the thesis that the main factor to be considered when analyzing the impact of polymorphisms on the risk of malignant transformation should be intergenic interactions. Moreover, we are suggesting that some polymorphisms may have impact not only on the malignant transformation but also on the stage of the tumor.

Identification of Constrained Cancer Driver Genes Based on Mutation Timing

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Sakoparnig, Thomas; Fried, Patrick; Beerenwinkel, Niko

2015-01-01

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression. PMID:25569148

Integrative analysis of survival-associated gene sets in breast cancer.

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Varn, Frederick S; Ung, Matthew H; Lou, Shao Ke; Cheng, Chao

2015-03-12

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used

Bacterial Toxins for Oncoleaking Suicidal Cancer Gene Therapy.

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Pahle, Jessica; Walther, Wolfgang

For suicide gene therapy, initially prodrug-converting enzymes (gene-directed enzyme-producing therapy, GDEPT) were employed to intracellularly metabolize non-toxic prodrugs into toxic compounds, leading to the effective suicidal killing of the transfected tumor cells. In this regard, the suicide gene therapy has demonstrated its potential for efficient tumor eradication. Numerous suicide genes of viral or bacterial origin were isolated, characterized, and extensively tested in vitro and in vivo, demonstrating their therapeutic potential even in clinical trials to treat cancers of different entities. Apart from this, growing efforts are made to generate more targeted and more effective suicide gene systems for cancer gene therapy. In this regard, bacterial toxins are an alternative to the classical GDEPT strategy, which add to the broad spectrum of different suicide approaches. In this context, lytic bacterial toxins, such as streptolysin O (SLO) or the claudin-targeted Clostridium perfringens enterotoxin (CPE) represent attractive new types of suicide oncoleaking genes. They permit as pore-forming proteins rapid and also selective toxicity toward a broad range of cancers. In this chapter, we describe the generation and use of SLO as well as of CPE-based gene therapies for the effective tumor cell eradication as promising, novel suicide gene approach particularly for treatment of therapy refractory tumors.

Prevalence and Penetrance of Major Genes and Polygenes for Colorectal Cancer

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Win, Aung Ko; Jenkins, Mark A.; Dowty, James G.; Antoniou, Antonis C.; Lee, Andrew; Giles, Graham G.; Buchanan, Daniel D.; Clendenning, Mark; Rosty, Christophe; Ahnen, Dennis J.; Thibodeau, Stephen N.; Casey, Graham; Gallinger, Steven; Le Marchand, Loïc; Haile, Robert W.; Potter, John D.; Zheng, Yingye; Lindor, Noralane M.; Newcomb, Polly A.; Hopper, John L.; MacInnis, Robert J.

2016-01-01

Background While high-risk mutations in identified major susceptibility genes (DNA mismatch repair genes and MUTYH) account for some familial aggregation of colorectal cancer, their population prevalence and the causes of the remaining familial aggregation are not known. Methods We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the USA, Canada and Australia and screened probands for mutations in mismatch repair genes and MUTYH. We conducted modified segregation analyses using the cancer history of first-degree relatives, conditional on the proband’s age at diagnosis. We estimated the prevalence of mutations in the identified genes, the prevalence of and hazard ratio for unidentified major gene mutations, and the variance of the residual polygenic component. Results We estimated that 1 in 279 of the population carry mutations in mismatch repair genes (MLH1= 1 in 1946, MSH2= 1 in 2841, MSH6= 1 in 758, PMS2= 1 in 714), 1 in 45 carry mutations in MUTYH, and 1 in 504 carry mutations associated with an average 31-fold increased risk of colorectal cancer in unidentified major genes. The estimated polygenic variance was reduced by 30–50% after allowing for unidentified major genes and decreased from 3.3 for age colorectal cancer. Impact Our findings could aid gene discovery and development of better colorectal cancer risk prediction models. PMID:27799157

Expression of PAM50 Genes in Lung Cancer: Evidence that Interactions between Hormone Receptors and HER2/HER3 Contribute to Poor Outcome

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Jill M. Siegfried

2015-11-01

Full Text Available Non–small cell lung cancers (NSCLCs frequently express estrogen receptor (ER β, and estrogen signaling is active in many lung tumors. We investigated the ability of genes contained in the prediction analysis of microarray 50 (PAM50 breast cancer risk predictor gene signature to provide prognostic information in NSCLC. Supervised principal component analysis of mRNA expression data was used to evaluate the ability of the PAM50 panel to provide prognostic information in a stage I NSCLC cohort, in an all-stage NSCLC cohort, and in The Cancer Genome Atlas data. Immunohistochemistry was used to determine status of ERβ and other proteins in lung tumor tissue. Associations with prognosis were observed in the stage I cohort. Cross-validation identified seven genes that, when analyzed together, consistently showed survival associations. In pathway analysis, the seven-gene panel described one network containing the ER and progesterone receptor, as well as human epidermal growth factor receptor (HER2/HER3 and neuregulin-1. NSCLC cases also showed a significant association between ERβ and HER2 protein expression. Cases positive for HER2 expression were more likely to express HER3, and ERβ-positive cases were less likely to be both HER2 and HER3 negative. Prognostic ability of genes in the PAM50 panel was verified in an ERβ-positive cohort representing all NSCLC stages. In The Cancer Genome Atlas data sets, the PAM50 gene set was prognostic in both adenocarcinoma and squamous cell carcinoma, whereas the seven-gene panel was prognostic only in squamous cell carcinoma. Genes in the PAM50 panel, including those linking ER and HER2, identify lung cancer patients at risk for poor outcome, especially among ERβ-positive cases and squamous cell carcinoma.

Quantitative DNA methylation analysis of candidate genes in cervical cancer.

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Erin M Siegel

Full Text Available Aberrant DNA methylation has been observed in cervical cancer; however, most studies have used non-quantitative approaches to measure DNA methylation. The objective of this study was to quantify methylation within a select panel of genes previously identified as targets for epigenetic silencing in cervical cancer and to identify genes with elevated methylation that can distinguish cancer from normal cervical tissues. We identified 49 women with invasive squamous cell cancer of the cervix and 22 women with normal cytology specimens. Bisulfite-modified genomic DNA was amplified and quantitative pyrosequencing completed for 10 genes (APC, CCNA, CDH1, CDH13, WIF1, TIMP3, DAPK1, RARB, FHIT, and SLIT2. A Methylation Index was calculated as the mean percent methylation across all CpG sites analyzed per gene (~4-9 CpG site per sequence. A binary cut-point was defined at >15% methylation. Sensitivity, specificity and area under ROC curve (AUC of methylation in individual genes or a panel was examined. The median methylation index was significantly higher in cases compared to controls in 8 genes, whereas there was no difference in median methylation for 2 genes. Compared to HPV and age, the combination of DNA methylation level of DAPK1, SLIT2, WIF1 and RARB with HPV and age significantly improved the AUC from 0.79 to 0.99 (95% CI: 0.97-1.00, p-value = 0.003. Pyrosequencing analysis confirmed that several genes are common targets for aberrant methylation in cervical cancer and DNA methylation level of four genes appears to increase specificity to identify cancer compared to HPV detection alone. Alterations in DNA methylation of specific genes in cervical cancers, such as DAPK1, RARB, WIF1, and SLIT2, may also occur early in cervical carcinogenesis and should be evaluated.

Robust multi-tissue gene panel for cancer detection

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Talantov Dmitri

2010-06-01

Full Text Available Abstract Background We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. Methods Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA and Asterand (Detroit, MI. Biotinylated targets were prepared using published methods (Affymetrix, CA and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA. Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. Results We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. Conclusions Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays

The development of genes associated with radiosensitivity of cervical cancer

International Nuclear Information System (INIS)

Li Hongyan; Chen Zhihua; He Guifang

2007-01-01

It has a good application prospect to predict effects of radiotherapy by examining radiosensitivity of patients with cervical cancers before their radiotherapy. Prediction of tumor cell radiosensitivity according to their level of gene expression and gene therapy to reverse radio-resistance prior to radiation on cervical cancers are heated researches on tumor therapy. The expression of some proliferation-related genes, apoptosis-related genes and hypoxia-related genes can inerease the radiosensitivity of cervical cancer. Microarray technology may have more direct applications to the study of biological pathway contributing to radiation resistance and may lead to development of alternative treatment modalities. (authors)

Gene-gene, gene-environment, gene-nutrient interactions and single nucleotide polymorphisms of inflammatory cytokines.

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Nadeem, Amina; Mumtaz, Sadaf; Naveed, Abdul Khaliq; Aslam, Muhammad; Siddiqui, Arif; Lodhi, Ghulam Mustafa; Ahmad, Tausif

2015-05-15

Inflammation plays a significant role in the etiology of type 2 diabetes mellitus (T2DM). The rise in the pro-inflammatory cytokines is the essential step in glucotoxicity and lipotoxicity induced mitochondrial injury, oxidative stress and beta cell apoptosis in T2DM. Among the recognized markers are interleukin (IL)-6, IL-1, IL-10, IL-18, tissue necrosis factor-alpha (TNF-α), C-reactive protein, resistin, adiponectin, tissue plasminogen activator, fibrinogen and heptoglobins. Diabetes mellitus has firm genetic and very strong environmental influence; exhibiting a polygenic mode of inheritance. Many single nucleotide polymorphisms (SNPs) in various genes including those of pro and anti-inflammatory cytokines have been reported as a risk for T2DM. Not all the SNPs have been confirmed by unifying results in different studies and wide variations have been reported in various ethnic groups. The inter-ethnic variations can be explained by the fact that gene expression may be regulated by gene-gene, gene-environment and gene-nutrient interactions. This review highlights the impact of these interactions on determining the role of single nucleotide polymorphism of IL-6, TNF-α, resistin and adiponectin in pathogenesis of T2DM.

Bacteria as vectors for gene therapy of cancer.

LENUS (Irish Health Repository)

Baban, Chwanrow K

2012-01-31

Anti-cancer therapy faces major challenges, particularly in terms of specificity of treatment. The ideal therapy would eradicate tumor cells selectively with minimum side effects on normal tissue. Gene or cell therapies have emerged as realistic prospects for the treatment of cancer, and involve the delivery of genetic information to a tumor to facilitate the production of therapeutic proteins. However, there is still much to be done before an efficient and safe gene medicine is achieved, primarily developing the means of targeting genes to tumors safely and efficiently. An emerging family of vectors involves bacteria of various genera. It has been shown that bacteria are naturally capable of homing to tumors when systemically administered resulting in high levels of replication locally. Furthermore, invasive species can deliver heterologous genes intra-cellularly for tumor cell expression. Here, we review the use of bacteria as vehicles for gene therapy of cancer, detailing the mechanisms of action and successes at preclinical and clinical levels.

Targeted cancer gene therapy : the flexibility of adenoviral gene therapy vectors

NARCIS (Netherlands)

Rots, MG; Curiel, DT; Gerritsen, WR; Haisma, HJ

2003-01-01

Recombinant adenoviral vectors are promising reagents for therapeutic interventions in humans, including gene therapy for biologically complex diseases like cancer and cardiovascular diseases. In this regard, the major advantage of adenoviral vectors is their superior in vivo gene transfer

Phylostratigraphic tracking of cancer genes suggests a link to the emergence of multicellularity in metazoa

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Domazet-Lošo Tomislav

2010-05-01

Full Text Available Abstract Background Phylostratigraphy is a method used to correlate the evolutionary origin of founder genes (that is, functional founder protein domains of gene families with particular macroevolutionary transitions. It is based on a model of genome evolution that suggests that the origin of complex phenotypic innovations will be accompanied by the emergence of such founder genes, the descendants of which can still be traced in extant organisms. The origin of multicellularity can be considered to be a macroevolutionary transition, for which new gene functions would have been required. Cancer should be tightly connected to multicellular life since it can be viewed as a malfunction of interaction between cells in a multicellular organism. A phylostratigraphic tracking of the origin of cancer genes should, therefore, also provide insights into the origin of multicellularity. Results We find two strong peaks of the emergence of cancer related protein domains, one at the time of the origin of the first cell and the other around the time of the evolution of the multicellular metazoan organisms. These peaks correlate with two major classes of cancer genes, the 'caretakers', which are involved in general functions that support genome stability and the 'gatekeepers', which are involved in cellular signalling and growth processes. Interestingly, this phylogenetic succession mirrors the ontogenetic succession of tumour progression, where mutations in caretakers are thought to precede mutations in gatekeepers. Conclusions A link between multicellularity and formation of cancer has often been predicted. However, this has not so far been explicitly tested. Although we find that a significant number of protein domains involved in cancer predate the origin of multicellularity, the second peak of cancer protein domain emergence is, indeed, connected to a phylogenetic level where multicellular animals have emerged. The fact that we can find a strong and

Transcriptome analysis of recurrently deregulated genes across multiple cancers identifies new pan-cancer biomarkers

DEFF Research Database (Denmark)

Kaczkowski, Bogumil; Tanaka, Yuji; Kawaji, Hideya

2016-01-01

Genes that are commonly deregulated in cancer are clinically attractive as candidate pan-diagnostic markers and therapeutic targets. To globally identify such targets, we compared Cap Analysis of Gene Expression (CAGE) profiles from 225 different cancer cell lines and 339 corresponding primary cell...

Ancient genes establish stress-induced mutation as a hallmark of cancer.

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Cisneros, Luis; Bussey, Kimberly J; Orr, Adam J; Miočević, Milica; Lineweaver, Charles H; Davies, Paul

2017-01-01

Cancer is sometimes depicted as a reversion to single cell behavior in cells adapted to live in a multicellular assembly. If this is the case, one would expect that mutation in cancer disrupts functional mechanisms that suppress cell-level traits detrimental to multicellularity. Such mechanisms should have evolved with or after the emergence of multicellularity. This leads to two related, but distinct hypotheses: 1) Somatic mutations in cancer will occur in genes that are younger than the emergence of multicellularity (1000 million years [MY]); and 2) genes that are frequently mutated in cancer and whose mutations are functionally important for the emergence of the cancer phenotype evolved within the past 1000 million years, and thus would exhibit an age distribution that is skewed to younger genes. In order to investigate these hypotheses we estimated the evolutionary ages of all human genes and then studied the probability of mutation and their biological function in relation to their age and genomic location for both normal germline and cancer contexts. We observed that under a model of uniform random mutation across the genome, controlled for gene size, genes less than 500 MY were more frequently mutated in both cases. Paradoxically, causal genes, defined in the COSMIC Cancer Gene Census, were depleted in this age group. When we used functional enrichment analysis to explain this unexpected result we discovered that COSMIC genes with recessive disease phenotypes were enriched for DNA repair and cell cycle control. The non-mutated genes in these pathways are orthologous to those underlying stress-induced mutation in bacteria, which results in the clustering of single nucleotide variations. COSMIC genes were less common in regions where the probability of observing mutational clusters is high, although they are approximately 2-fold more likely to harbor mutational clusters compared to other human genes. Our results suggest this ancient mutational response to

Risk of colorectal cancer for people with a mutation in both a MUTYH and a DNA mismatch repair gene

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Win, Aung Ko; Reece, Jeanette C.; Buchanan, Daniel D.; Clendenning, Mark; Young, Joanne P.; Cleary, Sean P.; Kim, Hyeja; Cotterchio, Michelle; Dowty, James G.; MacInnis, Robert J.; Tucker, Katherine M.; Winship, Ingrid M.; Macrae, Finlay A.; Burnett, Terrilea; Le Marchand, Loïc; Casey, Graham; Haile, Robert W.; Newcomb, Polly A.; Thibodeau, Stephen N.; Lindor, Noralane M.; Hopper, John L.; Gallinger, Steven; Jenkins, Mark A.

2015-01-01

The base excision repair protein, MUTYH, functionally interacts with the DNA mismatch repair (MMR) system. As genetic testing moves from testing one gene at a time, to gene panel and whole exome next generation sequencing approaches, understanding the risk associated with co-existence of germline mutations in these genes will be important for clinical interpretation and management. From the Colon Cancer Family Registry, we identified 10 carriers who had both a MUTYH mutation (6 with c.1187G>A p.(Gly396Asp), 3 with c.821G>A p.(Arg274Gln), and 1 with c.536A>G p.(Tyr179Cys)) and a MMR gene mutation (3 in MLH1, 6 in MSH2, and 1 in PMS2), 375 carriers of a single (monoallelic) MUTYH mutation alone, and 469 carriers of a MMR gene mutation alone. Of the 10 carriers of both gene mutations, 8 were diagnosed with colorectal cancer. Using a weighted cohort analysis, we estimated that risk of colorectal cancer for carriers of both a MUTYH and a MMR gene mutation was substantially higher than that for carriers of a MUTYH mutation alone [hazard ratio (HR) 21.5, 95 % confidence interval (CI) 9.19–50.1; p colorectal cancer for carriers of a MMR gene mutation alone. Our finding suggests MUTYH mutation testing in MMR gene mutation carriers is not clinically informative. PMID:26202870

DDEC: Dragon database of genes implicated in esophageal cancer

KAUST Repository

Essack, Magbubah

2009-07-06

Background: Esophageal cancer ranks eighth in order of cancer occurrence. Its lethality primarily stems from inability to detect the disease during the early organ-confined stage and the lack of effective therapies for advanced-stage disease. Moreover, the understanding of molecular processes involved in esophageal cancer is not complete, hampering the development of efficient diagnostics and therapy. Efforts made by the scientific community to improve the survival rate of esophageal cancer have resulted in a wealth of scattered information that is difficult to find and not easily amendable to data-mining. To reduce this gap and to complement available cancer related bioinformatic resources, we have developed a comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer) with esophageal cancer related information, as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. Description: Manually curated 529 genes differentially expressed in EC are contained in the database. We extracted and analyzed the promoter regions of these genes and complemented gene-related information with transcription factors that potentially control them. We further, precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. The resulting database, DDEC, has a useful feature to display potential associations that are rarely reported and thus difficult to identify. Moreover, DDEC enables inspection of potentially new \\'association hypotheses\\' generated based on the precompiled reports. Conclusion: We hope that this resource will serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in EC genetics. DDEC is

A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia

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Liu, Xiaoming; Yang, Jiasheng; Zhang, Yi; Fang, Yun; Wang, Fayou; Wang, Jun; Zheng, Xiaoqi; Yang, Jialiang

2016-03-01

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.

Effects of interactions between common genetic variants and alcohol consumption on colorectal cancer risk

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Song, Nan; Shin, Aesun; Oh, Jae Hwan; Kim, Jeongseon

2018-01-01

Background Genome-wide association studies (GWAS) have identified approximately 40 common genetic loci associated with colorectal cancer risk. To investigate possible gene-environment interactions (GEIs) between GWAS-identified single-nucleotide polymorphisms (SNPs) and alcohol consumption with respect to colorectal cancer, a hospital-based case-control study was conducted. Results Higher levels of alcohol consumption as calculated based on a standardized definition of a drink (1 drink=12.5g of ethanol) were associated with increased risk of colorectal cancer (OR=2.47, 95% CI=1.62-3.76 for heavy drinkers [>50g/day] compared to never drinkers; ptrendcolorectal cancer associated with the G allele of rs6687758 tended to increase among individuals in the heavier alcohol consumption strata. A statistically significant association between rs6687758 and colorectal cancer risk was observed among moderate alcohol drinkers who consumed between >12.5 and ≤50g of alcohol per day (OR=1.46, 95% CI=1.01-2.11). Methods A total of 2,109 subjects (703 colorectal cancer patients and 1,406 healthy controls) were recruited from the Korean National Cancer Center. For genotyping, 30 GWAS-identified SNPs were selected. A logistic regression model was used to evaluate associations of SNPs and alcohol consumption with colorectal cancer risk. We also tested GEIs between SNPs and alcohol consumption using a logistic model with multiplicative interaction terms. Conclusions Our results suggest that SNP rs6687758 at 1q41 may interact with alcohol consumption in the etiology of colorectal cancer. PMID:29464080

ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

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Zhou, Yuanshuai; Liu, Yongjing; Li, Kening; Zhang, Rui; Qiu, Fujun; Zhao, Ning; Xu, Yan

2015-01-01

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.

Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

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Xiaosheng Wang

2010-03-01

Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

The bone morphogenetic protein antagonist gremlin 1 is overexpressed in human cancers and interacts with YWHAH protein

International Nuclear Information System (INIS)

Namkoong, Hong; Shin, Seung Min; Kim, Hyun Kee; Ha, Seon-Ah; Cho, Goang Won; Hur, Soo Young; Kim, Tae Eung; Kim, Jin Woo

2006-01-01

Basic studies of oncogenesis have demonstrated that either the elevated production of particular oncogene proteins or the occurrence of qualitative abnormalities in oncogenes can contribute to neoplastic cellular transformation. The purpose of our study was to identify an unique gene that shows cancer-associated expression, and characterizes its function related to human carcinogenesis. We used the differential display (DD) RT-PCR method using normal cervical, cervical cancer, metastatic cervical tissues, and cervical cancer cell lines to identify genes overexpressed in cervical cancers and identified gremlin 1 which was overexpressed in cervical cancers. We determined expression levels of gremlin 1 using Northern blot analysis and immunohistochemical study in various types of human normal and cancer tissues. To understand the tumorigenesis pathway of identified gremlin 1 protein, we performed a yeast two-hybrid screen, GST pull down assay, and immunoprecipitation to identify gremlin 1 interacting proteins. DDRT-PCR analysis revealed that gremlin 1 was overexpressed in uterine cervical cancer. We also identified a human gremlin 1 that was overexpressed in various human tumors including carcinomas of the lung, ovary, kidney, breast, colon, pancreas, and sarcoma. PIG-2-transfected HEK 293 cells exhibited growth stimulation and increased telomerase activity. Gremlin 1 interacted with homo sapiens tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta polypeptide (14-3-3 eta; YWHAH). YWHAH protein binding site for gremlin 1 was located between residues 61–80 and gremlin 1 binding site for YWHAH was found to be located between residues 1 to 67. Gremlin 1 may play an oncogenic role especially in carcinomas of the uterine cervix, lung, ovary, kidney, breast, colon, pancreas, and sarcoma. Over-expressed gremlin 1 functions by interaction with YWHAH. Therefore, Gremlin 1 and its binding protein YWHAH could be good targets for developing diagnostic and

A kernel regression approach to gene-gene interaction detection for case-control studies.

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Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Interaction between polymorphisms in aspirin metabolic pathways, regular aspirin use and colorectal cancer risk: A case-control study in unselected white European populations.

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Sheth, Harsh; Northwood, Emma; Ulrich, Cornelia M; Scherer, Dominique; Elliott, Faye; Barrett, Jennifer H; Forman, David; Wolf, C Roland; Smith, Gillian; Jackson, Michael S; Santibanez-Koref, Mauro; Haile, Robert; Casey, Graham; Jenkins, Mark; Win, Aung Ko; Hopper, John L; Marchand, Loic Le; Lindor, Noralane M; Thibodeau, Stephen N; Potter, John D; Burn, John; Bishop, D Timothy

2018-01-01

Regular aspirin use is associated with reduced risk of colorectal cancer (CRC). Variation in aspirin's chemoprevention efficacy has been attributed to the presence of single nucleotide polymorphisms (SNPs). We conducted a meta-analysis using two large population-based case-control datasets, the UK-Leeds Colorectal Cancer Study Group and the NIH-Colon Cancer Family Registry, having a combined total of 3325 cases and 2262 controls. The aim was to assess 42 candidate SNPs in 15 genes whose association with colorectal cancer risk was putatively modified by aspirin use, in the literature. Log odds ratios (ORs) and standard errors were estimated for each dataset separately using logistic regression adjusting for age, sex and study site, and dataset-specific results were combined using random effects meta-analysis. Meta-analysis showed association between SNPs rs6983267, rs11694911 and rs2302615 with CRC risk reduction (All Paspirin use and CRC risk (Pinteraction = 0.01 and 0.02, respectively); stratification by aspirin use showed an association for decreased CRC risk for aspirin users having a wild-type genotype (rs2070959 OR = 0.77, 95% CI = 0.68-0.86; rs1105879 OR = 0.77 95% CI = 0.69-0.86) compared to variant allele cariers. The direction of the interaction however is in contrast to that published in studies on colorectal adenomas. Both SNPs showed potential site-specific interaction with aspirin use and colon cancer risk only (Pinteraction = 0.006 and 0.008, respectively), with the direction of association similar to that observed for CRC. Additionally, they showed interaction between any non-steroidal anti-inflammatory drugs (including aspirin) use and CRC risk (Pinteraction = 0.01 for both). All gene x environment (GxE) interactions however were not significant after multiple test correction. Candidate gene investigation indicated no evidence of GxE interaction between genetic variants in genes involved in aspirin pathways, regular aspirin use and colorectal cancer

Drug Interactions in Childhood Cancer

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Haidar, Cyrine; Jeha, Sima

2016-01-01

Children with cancer are increasingly benefiting from novel therapeutic strategies and advances in supportive care, as reflected in improvements in both their survival and quality of life. However, the continuous emergence of new oncology drugs and supportive care agents has also increased the possibility of deleterious drug interactions and healthcare providers need to practice extreme caution when combining medications. In this review, we discuss the most common interactions of chemotherapeutic agents with supportive care drugs such as anticonvulsants, antiemetics, uric acid–lowering agents, acid suppressants, antimicrobials, and pain management medications in pediatric oncology patients. As chemotherapy agents interact not only with medications but also with foods and herbal supplements that patients receive during the course of their treatment, we also briefly review such interactions and provide recommendations to avoid unwanted and potentially fatal interactions in children with cancer. PMID:20869315

Current status of gene therapy for breast cancer: progress and challenges

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McCrudden CM

2014-11-01

Full Text Available Cian M McCrudden, Helen O McCarthySchool of Pharmacy, Queen’s University Belfast, Belfast, UKAbstract: Breast cancer is characterized by a series of genetic mutations and is therefore ideally placed for gene therapy intervention. The aim of gene therapy is to deliver a nucleic acid-based drug to either correct or destroy the cells harboring the genetic aberration. More recently, cancer gene therapy has evolved to also encompass delivery of RNA interference technologies, as well as cancer DNA vaccines. However, the bottleneck in creating such nucleic acid pharmaceuticals lies in the delivery. Deliverability of DNA is limited as it is prone to circulating nucleases; therefore, numerous strategies have been employed to aid with biological transport. This review will discuss some of the viral and nonviral approaches to breast cancer gene therapy, and present the findings of clinical trials of these therapies in breast cancer patients. Also detailed are some of the most recent developments in nonviral approaches to targeting in breast cancer gene therapy, including transcriptional control, and the development of recombinant, multifunctional bio-inspired systems. Lastly, DNA vaccines for breast cancer are documented, with comment on requirements for successful pharmaceutical product development.Keywords: breast cancer, gene therapy, nonviral, clinical trial

Anti-Angiogenic Gene Therapy for Prostate Cancer

Science.gov (United States)

2004-04-01

S. Parvovirus vectors for cancer gene therapy. Expert. Opin. Bid. Ther., 2004, 4: 53-64. Ponnazhagan, S., and Hoover, F. Delivery of DNA to tumor... vaccine with plasmid adjuvants 95h Annual Meeting of the American Society for Cancer Research, Orlando, FL, April 2004. Chaudhuri, T.R., Cao, Z...with recombinant AAV vectors results in sustained expression in a dog model of hemophilia. Gene Ther., 5: 40-49, 1998. 2ś 35. Bohl, D., Bosch, A

Gene-Environment Interactions in Severe Mental Illness

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Rudolf eUher

2014-05-01

Full Text Available Severe mental illness is a broad category that includes schizophrenia, bipolar disorder and severe depression. Both genetic disposition and environmental exposures play important roles in the development of severe mental illness. Multiple lines of evidence suggest that the roles of genetic and environmental depend on each other. Gene-environment interactions may underlie the paradox of strong environmental factors for highly heritable disorders, the low estimates of shared environmental influences in twin studies of severe mental illness and the heritability gap between twin and molecular heritability estimates. Sons and daughters of parents with severe mental illness are more vulnerable to the effects of prenatal and postnatal environmental exposures, suggesting that the expression of genetic liability depends on environment. In the last decade, gene-environment interactions involving specific molecular variants in candidate genes have been identified. Replicated findings include an interaction between a polymorphism in the AKT1 gene and cannabis use in the development of psychosis and an interaction between the length polymorphism of the serotonin transporter gene and childhood maltreatment in the development of persistent depressive disorder. Bipolar disorder has been underinvestigated, with only a single study showing an interaction between a functional polymorphism in BDNF and stressful life events triggering bipolar depressive episodes. The first systematic search for gene-environment interactions has found that a polymorphism in CTNNA3 may sensitise the developing brain to the pathogenic effect of cytomegalovirus in utero, leading to schizophrenia in adulthood. Strategies for genome-wide investigations will likely include coordination between epidemiological and genetic research efforts, systematic assessment of multiple environmental factors in large samples, and prioritization of genetic variants.

Comparative modeling and docking studies of p16ink4/Cyclin D1/Rb pathway genes in lung cancer revealed functionally interactive residue of RB1 and its functional partner E2F1

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e Zahra Syeda Naqsh

2013-01-01

Full Text Available Abstract Background Lung cancer is the major cause of mortality worldwide. Major signalling pathways that could play significant role in lung cancer therapy include (1 Growth promoting pathways (Epidermal Growth Factor Receptor/Ras/ PhosphatidylInositol 3-Kinase (2 Growth inhibitory pathways (p53/Rb/P14ARF, STK11 (3 Apoptotic pathways (Bcl-2/Bax/Fas/FasL. Insilico strategy was implemented to solve the mystery behind selected lung cancer pathway by applying comparative modeling and molecular docking studies. Results YASARA [v 12.4.1] was utilized to predict structural models of P16-INK4 and RB1 genes using template 4ELJ-A and 1MX6-B respectively. WHAT CHECK evaluation tool demonstrated overall quality of predicted P16-INK4 and RB1 with Z-score of −0.132 and −0.007 respectively which showed a strong indication of reliable structure prediction. Protein-protein interactions were explored by utilizing STRING server, illustrated that CDK4 and E2F1 showed strong interaction with P16-INK4 and RB1 based on confidence score of 0.999 and 0.999 respectively. In order to facilitate a comprehensive understanding of the complex interactions between candidate genes with their functional interactors, GRAMM-X server was used. Protein-protein docking investigation of P16-INK4 revealed four ionic bonds illustrating Arg47, Arg80,Cys72 and Met1 residues as actively participating in interactions with CDK4 while docking results of RB1 showed four hydrogen bonds involving Glu864, Ser567, Asp36 and Arg861 residues which interact strongly with its respective functional interactor E2F1. Conclusion This research may provide a basis for understanding biological insights of P16-INK4 and RB1 proteins which will be helpful in future to design a suitable drug to inhibit the disease pathogenesis as we have determined the interacting amino acids which can be targeted in order to design a ligand in-vitro to propose a drug for clinical trials. Protein -protein docking of

Hereditary Ovarian Cancer: Not Only BRCA 1 and 2 Genes

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Angela Toss

2015-01-01

Full Text Available More than one-fifth of ovarian tumors have hereditary susceptibility and, in about 65–85% of these cases, the genetic abnormality is a germline mutation in BRCA genes. Nevertheless, several other suppressor genes and oncogenes have been associated with hereditary ovarian cancers, including the mismatch repair (MMR genes in Lynch syndrome, the tumor suppressor gene, TP53, in the Li-Fraumeni syndrome, and several other genes involved in the double-strand breaks repair system, such as CHEK2, RAD51, BRIP1, and PALB2. The study of genetic discriminators and deregulated pathways involved in hereditary ovarian syndromes is relevant for the future development of molecular diagnostic strategies and targeted therapeutic approaches. The recent development and implementation of next-generation sequencing technologies have provided the opportunity to simultaneously analyze multiple cancer susceptibility genes, reduce the delay and costs, and optimize the molecular diagnosis of hereditary tumors. Particularly, the identification of mutations in ovarian cancer susceptibility genes in healthy women may result in a more personalized cancer risk management with tailored clinical and radiological surveillance, chemopreventive approaches, and/or prophylactic surgeries. On the other hand, for ovarian cancer patients, the identification of mutations may provide potential targets for biologic agents and guide treatment decision-making.

Gene expression in colorectal cancer

DEFF Research Database (Denmark)

Birkenkamp-Demtroder, Karin; Christensen, Lise Lotte; Olesen, Sanne Harder

2002-01-01

Understanding molecular alterations in colorectal cancer (CRC) is needed to define new biomarkers and treatment targets. We used oligonucleotide microarrays to monitor gene expression of about 6,800 known genes and 35,000 expressed sequence tags (ESTs) on five pools (four to six samples in each...... pool) of total RNA from left-sided sporadic colorectal carcinomas. We compared normal tissue to carcinoma tissue from Dukes' stages A-D (noninvasive to distant metastasis) and identified 908 known genes and 4,155 ESTs that changed remarkably from normal to tumor tissue. Based on intensive filtering 226...

Endocrine aspects of cancer gene therapy.

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Barzon, Luisa; Boscaro, Marco; Palù, Giorgio

2004-02-01

The field of cancer gene therapy is in continuous expansion, and technology is quickly moving ahead as far as gene targeting and regulation of gene expression are concerned. This review focuses on the endocrine aspects of gene therapy, including the possibility to exploit hormone and hormone receptor functions for regulating therapeutic gene expression, the use of endocrine-specific genes as new therapeutic tools, the effects of viral vector delivery and transgene expression on the endocrine system, and the endocrine response to viral vector delivery. Present ethical concerns of gene therapy and the risk of germ cell transduction are also discussed, along with potential lines of innovation to improve cell and gene targeting.

The potential for tumor suppressor gene therapy in head and neck cancer.

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Birkeland, Andrew C; Ludwig, Megan L; Spector, Matthew E; Brenner, J Chad

2016-01-01

Head and neck squamous cell carcinoma remains a highly morbid and fatal disease. Importantly, genomic sequencing of head and neck cancers has identified frequent mutations in tumor suppressor genes. While targeted therapeutics increasingly are being investigated in head and neck cancer, the majority of these agents are against overactive/overexpressed oncogenes. Therapy to restore lost tumor suppressor gene function remains a key and under-addressed niche in trials for head and neck cancer. Recent advances in gene editing have captured the interest of both the scientific community and the public. As our technology for gene editing and gene expression modulation improves, addressing lost tumor suppressor gene function in head and neck cancers is becoming a reality. This review will summarize new techniques, challenges to implementation, future directions, and ethical ramifications of gene therapy in head and neck cancer.

Analysis of variants in DNA damage signalling genes in bladder cancer

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Bishop D Timothy

2008-07-01

Full Text Available Abstract Background Chemicals from occupational exposure and components of cigarette smoke can cause DNA damage in bladder urothelium. Failure to repair DNA damage by DNA repair proteins may result in mutations leading to genetic instability and the development of bladder cancer. Immunohistochemistry studies have shown DNA damage signal activation in precancerous bladder lesions which is lost on progression, suggesting that the damage signalling mechanism acts as a brake to further tumorigenesis. Single nucleotide polymorphisms (SNPs in DSB signalling genes may alter protein function. We hypothesized that SNPs in DSB signalling genes may modulate predisposition to bladder cancer and influence the effects of environmental exposures. Methods We recruited 771 cases and 800 controls (573 hospital-based and 227 population-based from a previous case-control study and interviewed them regarding their smoking habits and occupational history. DNA was extracted from a peripheral blood sample and genotyping of 24 SNPs in MRE11, NBS1, RAD50, H2AX and ATM was undertaken using an allelic discrimination method (Taqman. Results Smoking and occupational dye exposure were strongly associated with bladder cancer risk. Using logistic regression adjusting for age, sex, smoking and occupational dye exposure, there was a marginal increase in risk of bladder cancer for an MRE11 3'UTR SNP (rs2155209, adjusted odds ratio 1.54 95% CI (1.13–2.08, p = 0.01 for individuals homozygous for the rare allele compared to those carrying the common homozygous or heterozygous genotype. However, in the hospital-based controls, the genotype distribution for this SNP deviated from Hardy-Weinberg equilibrium. None of the other SNPs showed an association with bladder cancer and we did not find any significant interaction between any of these polymorphisms and exposure to smoking or dye exposure. Conclusion Apart from a possible effect for one MRE11 3'UTR SNP, our study does not support

Cancer gene therapy targeting angiogenesis: An updated Review

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Liu, Ching-Chiu; Shen, Zan; Kung, Hsiang-Fu; Lin, Marie CM

2006-01-01

Since the relationship between angiogenesis and tumor growth was established by Folkman in 1971, scientists have made efforts exploring the possibilities in treating cancer by targeting angiogenesis. Inhibition of angiogenesis growth factors and administration of angiogenesis inhibitors are the basics of anti-angiogenesis therapy. Transfer of anti-angiogenesis genes has received attention recently not only because of the advancement of recombinant vectors, but also because of the localized and sustained expression of therapeutic gene product inside the tumor after gene transfer. This review provides the up-to-date information about the strategies and the vectors studied in the field of anti-angiogenesis cancer gene therapy. PMID:17109514

RET is a potential tumor suppressor gene in colorectal cancer

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Luo, Yanxin; Tsuchiya, Karen D.; Park, Dong Il; Fausel, Rebecca; Kanngurn, Samornmas; Welcsh, Piri; Dzieciatkowski, Slavomir; Wang, Jianping; Grady, William M.

2012-01-01

Cancer arises as the consequence of mutations and epigenetic alterations that activate oncogenes and inactivate tumor suppressor genes. Through a genome-wide screen for methylated genes in colon neoplasms, we identified aberrantly methylated RET in colorectal cancer. RET, a transmembrane receptor tyrosine kinase and a receptor for the GDNF-family ligands, was one of the first oncogenes to be identified and has been shown to be an oncogene in thyroid cancer and pheochromocytoma. However, unexpectedly, we found RET is methylated in 27% of colon adenomas and in 63% of colorectal cancers, and now provide evidence that RET has tumor suppressor activity in colon cancer. The aberrant methylation of RET correlates with decreased RET expression, whereas the restoration of RET in colorectal cancer cell lines results in apoptosis. Furthermore, in support of a tumor suppressor function of RET, mutant RET has also been found in primary colorectal cancer. We now show that these mutations inactivate RET, which is consistent with RET being a tumor suppressor gene in the colon. These findings suggest that the aberrant methylation of RET and the mutational inactivation of RET promote colorectal cancer formation and that RET can serve as a tumor suppressor gene in the colon. Moreover, the increased frequency of methylated RET in colon cancers compared to adenomas suggests RET inactivation is involved in the progression of colon adenomas to cancer. PMID:22751117

Polymorphisms in inflammation pathway genes and endometrial cancer risk

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Delahanty, Ryan J.; Xiang, Yong-Bing; Spurdle, Amanda; Beeghly-Fadiel, Alicia; Long, Jirong; Thompson, Deborah; Tomlinson, Ian; Yu, Herbert; Lambrechts, Diether; Dörk, Thilo; Goodman, Marc T.; Zheng, Ying; Salvesen, Helga B.; Bao, Ping-Ping; Amant, Frederic; Beckmann, Matthias W.; Coenegrachts, Lieve; Coosemans, An; Dubrowinskaja, Natalia; Dunning, Alison; Runnebaum, Ingo B.; Easton, Douglas; Ekici, Arif B.; Fasching, Peter A.; Halle, Mari K.; Hein, Alexander; Howarth, Kimberly; Gorman, Maggie; Kaydarova, Dylyara; Krakstad, Camilla; Lose, Felicity; Lu, Lingeng; Lurie, Galina; O’Mara, Tracy; Matsuno, Rayna K.; Pharoah, Paul; Risch, Harvey; Corssen, Madeleine; Trovik, Jone; Turmanov, Nurzhan; Wen, Wanqing; Lu, Wei; Cai, Qiuyin; Zheng, Wei; Shu, Xiao-Ou

2013-01-01

Background Experimental and epidemiological evidence have suggested that chronic inflammation may play a critical role in endometrial carcinogenesis. Methods To investigate this hypothesis, a two-stage study was carried out to evaluate single nucleotide polymorphisms (SNPs) in inflammatory pathway genes in association with endometrial cancer risk. In stage 1, 64 candidate pathway genes were identified and 4,542 directly genotyped or imputed SNPs were analyzed among 832 endometrial cancer cases and 2,049 controls, using data from the Shanghai Endometrial Cancer Genetics Study. Linkage disequilibrium of stage 1 SNPs significantly associated with endometrial cancer (PAsian- and European-ancestry samples. Conclusions These findings lend support to the hypothesis that genetic polymorphisms in genes involved in the inflammatory pathway may contribute to genetic susceptibility to endometrial cancer. Impact Statement This study adds to the growing evidence that inflammation plays an important role in endometrial carcinogenesis. PMID:23221126

Gelsolin-Cu/ZnSOD interaction alters intracellular reactive oxygen species levels to promote cancer cell invasion

KAUST Repository

Tochhawng, Lalchhandami

2016-07-07

The actin-binding protein, gelsolin, is a well known regulator of cancer cell invasion. However, the mechanisms by which gelsolin promotes invasion are not well established. As reactive oxygen species (ROS) have been shown to promote cancer cell invasion, we investigated on the hypothesis that gelsolin-induced changes in ROS levels may mediate the invasive capacity of colon cancer cells. Herein, we show that increased gelsolin enhances the invasive capacity of colon cancer cells, and this is mediated via gelsolin\\'s effects in elevating intracellular superoxide (O2 .-) levels. We also provide evidence for a novel physical interaction between gelsolin and Cu/ZnSOD, that inhibits the enzymatic activity of Cu/ZnSOD, thereby resulting in a sustained elevation of intracellular O2 .-. Using microarray data of human colorectal cancer tissues from Gene Omnibus, we found that gelsolin gene expression positively correlates with urokinase plasminogen activator (uPA), an important matrix-degrading protease invovled in cancer invasion. Consistent with the in vivo evidence, we show that increased levels of O2 .- induced by gelsolin overexpression triggers the secretion of uPA. We further observed reduction in invasion and intracellular O2 .- levels in colon cancer cells, as a consequence of gelsolin knockdown using two different siRNAs. In these cells, concurrent repression of Cu/ ZnSOD restored intracellular O2 .- levels and rescued invasive capacity. Our study therefore identified gelsolin as a novel regulator of intracellular O2 .- in cancer cells via interacting with Cu/ZnSOD and inhibiting its enzymatic activity. Taken together, these findings provide insight into a novel function of gelsolin in promoting tumor invasion by directly impacting the cellular redox milieu.

Prostate cancer metastasis-driving genes: hurdles and potential approaches in their identification

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Yan Ting Chiang

2014-08-01

Full Text Available Metastatic prostate cancer is currently incurable. Metastasis is thought to result from changes in the expression of specific metastasis-driving genes in nonmetastatic prostate cancer tissue, leading to a cascade of activated downstream genes that set the metastatic process in motion. Such genes could potentially serve as effective therapeutic targets for improved management of the disease. They could be identified by comparative analysis of gene expression profiles of patient-derived metastatic and nonmetastatic prostate cancer tissues to pinpoint genes showing altered expression, followed by determining whether silencing of such genes can lead to inhibition of metastatic properties. Various hurdles encountered in this approach are discussed, including (i the need for clinically relevant, nonmetastatic and metastatic prostate cancer tissues such as xenografts of patients' prostate cancers developed via subrenal capsule grafting technology and (ii limitations in the currently available methodology for identification of master regulatory genes.

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

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Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

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Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

Novel genetic variants in miR-191 gene and familial ovarian cancer

International Nuclear Information System (INIS)

Shen, Jie; DiCioccio, Richard; Odunsi, Kunle; Lele, Shashikant B; Zhao, Hua

2010-01-01

Half of the familial aggregation of ovarian cancer can't be explained by any known risk genes, suggesting the existence of other genetic risk factors. Some of these unknown factors may not be traditional protein encoding genes. MicroRNA (miRNA) plays a critical role in tumorigenesis, but it is still unknown if variants in miRNA genes lead to predisposition to cancer. Considering the fact that miRNA regulates a number of tumor suppressor genes (TSGs) and oncogenes, genetic variations in miRNA genes could affect the levels of expression of TSGs or oncogenes and, thereby, cancer risk. To test this hypothesis in familial ovarian cancer, we screened for genetic variants in thirty selected miRNA genes, which are predicted to regulate key ovarian cancer genes and are reported to be misexpressed in ovarian tumor tissues, in eighty-three patients with familial ovarian cancer. All of the patients are non-carriers of any known BRCA1/2 or mismatch repair (MMR) gene mutations. Seven novel genetic variants were observed in four primary or precursor miRNA genes. Among them, three rare variants were found in the precursor or primary precursor of the miR-191 gene. In functional assays, the one variant located in the precursor of miR-191 resulted in conformational changes in the predicted secondary structures, and consequently altered the expression of mature miR-191. In further analysis, we found that this particular variant exists in five family members who had ovarian cancer. Our findings suggest that there are novel genetic variants in miRNA genes, and those certain genetic variants in miRNA genes can affect the expression of mature miRNAs and, consequently, might alter the regulation of TSGs or oncogenes. Additionally, the variant might be potentially associated with the development of familial ovarian cancer

Alteration of gene expression and DNA methylation in drug-resistant gastric cancer.

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Maeda, Osamu; Ando, Takafumi; Ohmiya, Naoki; Ishiguro, Kazuhiro; Watanabe, Osamu; Miyahara, Ryoji; Hibi, Yoko; Nagai, Taku; Yamada, Kiyofumi; Goto, Hidemi

2014-04-01

The mechanisms of drug resistance in cancer are not fully elucidated. To study the drug resistance of gastric cancer, we analyzed gene expression and DNA methylation profiles of 5-fluorouracil (5-FU)- and cisplatin (CDDP)-resistant gastric cancer cells and biopsy specimens. Drug-resistant gastric cancer cells were established with culture for >10 months in a medium containing 5-FU or CDDP. Endoscopic biopsy specimens were obtained from gastric cancer patients who underwent chemotherapy with oral fluoropyrimidine S-1 and CDDP. Gene expression and DNA methylation analyses were performed using microarray, and validated using real-time PCR and pyrosequencing, respectively. Out of 17,933 genes, 541 genes commonly increased and 569 genes decreased in both 5-FU- and CDDP-resistant AGS cells. Genes with expression changed by drugs were related to GO term 'extracellular region' and 'p53 signaling pathway' in both 5-FU- and CDDP-treated cells. Expression of 15 genes including KLK13 increased and 12 genes including ETV7 decreased, in both drug-resistant cells and biopsy specimens of two patients after chemotherapy. Out of 10,365 genes evaluated with both expression microarray and methylation microarray, 74 genes were hypermethylated and downregulated, or hypomethylated and upregulated in either 5-FU-resistant or CDDP-resistant cells. Of these genes, expression of 21 genes including FSCN1, CPT1C and NOTCH3, increased from treatment with a demethylating agent. There are alterations of gene expression and DNA methylation in drug-resistant gastric cancer; they may be related to mechanisms of drug resistance and may be useful as biomarkers of gastric cancer drug sensitivity.

Contribution of DNA double-strand break repair gene XRCC3 genotypes to oral cancer susceptibility in Taiwan.

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Tsai, Chia-Wen; Chang, Wen-Shin; Liu, Juhn-Cherng; Tsai, Ming-Hsui; Lin, Cheng-Chieh; Bau, Da-Tian

2014-06-01

The DNA repair gene X-ray repair cross complementing protein 3 (XRCC3) is thought to play a major role in double-strand break repair and in maintaining genomic stability. Very possibly, defective double-strand break repair of cells can lead to carcinogenesis. Therefore, a case-control study was performed to reveal the contribution of XRCC3 genotypes to individual oral cancer susceptibility. In this hospital-based research, the association of XRCC3 rs1799794, rs45603942, rs861530, rs3212057, rs1799796, rs861539, rs28903081 genotypes with oral cancer risk in a Taiwanese population was investigated. In total, 788 patients with oral cancer and 956 age- and gender-matched healthy controls were genotyped. The results showed that there was significant differential distribution among oral cancer and controls in the genotypic (p=0.001428) and allelic (p=0.0013) frequencies of XRCC3 rs861539. As for the other polymorphisms, there was no difference between case and control groups. In gene-lifestyle interaction analysis, we have provided the first evidence showing that there is an obvious joint effect of XRCC3 rs861539 genotype with individual areca chewing habits on oral cancer risk. In conclusion, the T allele of XRCC3 rs861539, which has an interaction with areca chewing habit in oral carcinogenesis, may be an early marker for oral cancer in Taiwanese. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Signal interaction of Hedgehog/GLI and epidermal growth factor receptor signaling in cancer development

International Nuclear Information System (INIS)

Eberl, M.

2012-01-01

The subject of this PhD thesis is based on the cooperation of Hedgehog (HH)/GLI with epidermal growth factor receptor (EGFR) signaling synergistically promoting oncogenic transformation and cancer growth. In previous studies we have demonstrated that the HH/GLI and EGFR signaling pathways interact synergistically resulting not only in selective induction of HH/GLI-EGFR target genes, but also in the onset of oncogenic transformation and tumor formation (Kasper, Schnidar et al. 2006; Schnidar, Eberl et al. 2009). However, the molecular key mediators acting downstream of HH/GLI and EGFR signal cooperation were largely unknown and the in vivo evidence for the therapeutic relevance of HH/GLI and EGFR signal cooperation in HH-associated cancers was lacking. During my PhD thesis I could demonstrate that the integration of EGFR and HH/GLI signaling involves activation of RAS/MEK/ERK and JUN/AP1 signaling in response to EGFR activation. Furthermore I succeeded in identifying genes, including stem cell- (SOX2, SOX9), tumor growth- (JUN, TGFA, FGF19) and metastasis-associated genes (SPP1/osteopontin, CXCR4) that showed synergistic transcriptional activation by HH/GLI-EGFR signal integration. Importantly, I could demonstrate that these genes arrange themselves within a stable interdependent signaling network, which is required for in vivo growth of basal cell carcinoma (BCC) and tumor-initiating pancreatic cancer cells. These data validate EGFR signaling as additional drug target in HH/GLI driven cancers and provide new therapeutic strategies based on combined targeting of cooperative HH/GLI-EGFR signaling and selected downstream target genes (Eberl, Klingler et al. 2012). (author) [de

Detection of Gene Interactions Based on Syntactic Relations

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Mi-Young Kim

2008-01-01

Full Text Available Interactions between proteins and genes are considered essential in the description of biomolecular phenomena, and networks of interactions are applied in a system's biology approach. Recently, many studies have sought to extract information from biomolecular text using natural language processing technology. Previous studies have asserted that linguistic information is useful for improving the detection of gene interactions. In particular, syntactic relations among linguistic information are good for detecting gene interactions. However, previous systems give a reasonably good precision but poor recall. To improve recall without sacrificing precision, this paper proposes a three-phase method for detecting gene interactions based on syntactic relations. In the first phase, we retrieve syntactic encapsulation categories for each candidate agent and target. In the second phase, we construct a verb list that indicates the nature of the interaction between pairs of genes. In the last phase, we determine direction rules to detect which of two genes is the agent or target. Even without biomolecular knowledge, our method performs reasonably well using a small training dataset. While the first phase contributes to improve recall, the second and third phases contribute to improve precision. In the experimental results using ICML 05 Workshop on Learning Language in Logic (LLL05 data, our proposed method gave an F-measure of 67.2% for the test data, significantly outperforming previous methods. We also describe the contribution of each phase to the performance.

Locating a Prostate Cancer Susceptibility Gene on the X Chromosome by Linkage Disequilibrium Mapping Using Three Founder Populations in Quebec and Switzerland

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2006-09-01

Gordon PH, Wang Q, Puisieux, A, Foulkes WD and Trifiro M. Polymorphisms and HNPCC: PMS2 -MLH1 protein interactions diminished by ‘single nucleotide...and PMS2 responsible for hereditary nonpolyposis colorectal cancer (HNPCC). Genes Chromosomes Cancer, 44 (2): 123-38, 2005. 120. Soravia C

Single nucleotide polymorphisms of DNA mismatch repair genes MSH2 and MLH1 confer susceptibility to esophageal cancer.

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Sun, Ming-Zhong; Ju, Hui-Xiang; Zhou, Zhong-Wei; Jin, Hao; Zhu, Rong

2014-01-01

Defects in DNA mismatch repair genes like MSH2 and MLH1 confer increased risk of cancers. Here, single nucleotide polymorphisms (SNPs) in MSH2 and MLH1 were investigated for their potential contribution to the risk of esophageal cancer. This study recruited 614 participants from Affiliated Yancheng Hospital, School of Medicine, Southeast University, of which 289 were patients with esophageal cancer, and the remainder was healthy individuals who served as a control group. Two SNPs, MSH2 c.2063T>G and MLH1 IVS14-19A>G, were genotyped using PCR-RFLP. Statistical analysis was performed using chi-square test and logistic regression analysis. Carriers of the MSH2 c.2063G allele were at significantly higher risk for esophageal cancer compared to individuals with the TT genotype [OR = 3.36, 95% confidence interval (CI): 1.18-11.03]. The MLH1 IVS14-19A>G allele also conferred significantly increased (1.70-fold) for esophageal cancer compared to the AA genotype (OR = 1.70, 95% CI: 1.13-5.06). Further, the variant alleles interacted such that individuals with the susceptible genotypes at both MSH2 and MLH1 had a significantly exacerbated risk for esophageal cancer (OR = 12.38, 95% CI: 3.09-63.11). In brief, SNPs in the DNA mismatch repair genes MSH2 and MLH1 increase the risk of esophageal cancer. Molecular investigations are needed to uncover the mechanism behind their interaction effect.

Novel interactions between vertebrate Hox genes

NARCIS (Netherlands)

Hooiveld, MHW; Morgan, R; Rieden, PID; Houtzager, E; Pannese, M; Damen, K; Boncinelli, E; Durston, AJ

1999-01-01

Understanding why metazoan Hox/HOM-C genes are expressed in spatiotemporal sequences showing colinearity with their genomic sequence is a central challenge in developmental biology. Here, we studied the consequences of ectopically expressing Hox genes to investigate whether Hox-Hox interactions

Nutrigenomics and Cancer

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Ardekani, Ali M.; Jabbari, Sepideh

2009-01-01

Cancer incidence is projected to increase in the future and an effectual preventive strategy is required to face this challenge. Alteration of dietary habits is potentially an effective approach for reducing cancer risk. Assessment of biological effects of a specific food or bioactive component that is linked to cancer and prediction of individual susceptibility as a function of nutrient-nutrient interactions and genetics is an essential element to evaluate the beneficiaries of dietary interventions. In general, the use of biomarkers to evaluate individuals susceptibilities to cancer must be easily accessible and reliable. However, the response of individuals to bioactive food components depends not only on the effective concentration of the bioactive food components, but also on the target tissues. This fact makes the response of individuals to food components vary from one individual to another. Nutrigenomics focuses on the understanding of interactions between genes and diet in an individual and how the response to bioactive food components is influenced by an individual's genes. Nutrients have shown to affect gene expression and to induce changes in DNA and protein molecules. Nutrigenomic approaches provide an opportunity to study how gene expression is regulated by nutrients and how nutrition affects gene variations and epigenetic events. Finding the components involved in interactions between genes and diet in an individual can potentially help identify target molecules important in preventing and/or reducing the symptoms of cancer. PMID:23407612

Inactivation of tumor suppressor genes and cancer therapy: An evolutionary game theory approach.

Science.gov (United States)

Khadem, Heydar; Kebriaei, Hamed; Veisi, Zahra

2017-06-01

Inactivation of alleles in tumor suppressor genes (TSG) is one of the important issues resulting in evolution of cancerous cells. In this paper, the evolution of healthy, one and two missed allele cells is modeled using the concept of evolutionary game theory and replicator dynamics. The proposed model also takes into account the interaction rates of the cells as designing parameters of the system. Different combinations of the equilibrium points of the parameterized nonlinear system is studied and categorized into some cases. In each case, the interaction rates' values are suggested in a way that the equilibrium points of the replicator dynamics are located on an appropriate region of the state space. Based on the suggested interaction rates, it is proved that the system doesn't have any undesirable interior equilibrium point as well. Therefore, the system will converge to the desirable region, where there is a scanty level of cancerous cells. In addition, the proposed conditions for interaction rates guarantee that, when a trajectory of the system reaches the boundaries, then it will stay there forever which is a desirable property since the equilibrium points have been already located on the boundaries, appropriately. The simulation results show the effectiveness of the suggestions in the elimination of the cancerous cells in different scenarios. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparing the DNA hypermethylome with gene mutations in human colorectal cancer.

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Kornel E Schuebel

2007-09-01

Full Text Available We have developed a transcriptome-wide approach to identify genes affected by promoter CpG island DNA hypermethylation and transcriptional silencing in colorectal cancer. By screening cell lines and validating tumor-specific hypermethylation in a panel of primary human colorectal cancer samples, we estimate that nearly 5% or more of all known genes may be promoter methylated in an individual tumor. When directly compared to gene mutations, we find larger numbers of genes hypermethylated in individual tumors, and a higher frequency of hypermethylation within individual genes harboring either genetic or epigenetic changes. Thus, to enumerate the full spectrum of alterations in the human cancer genome, and to facilitate the most efficacious grouping of tumors to identify cancer biomarkers and tailor therapeutic approaches, both genetic and epigenetic screens should be undertaken.

Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis

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Zhou X

2018-05-01

characterize the results of WGCNA.Results: Two gene modules (Gmagenta and Ggreen and one miRNA module were associated with the pathological stage. Six hub genes (COL1A2, THBS2, BGN, COL1A1, TAGLN and DACT3 were related to prognosis and validated to be associated with the pathological stage. Five hub miRNAs were identified to be related to prognosis (hsa-miR-125b-5p, hsa-miR-145-5p, hsa-let-7c-5p, hsa-miR-218-5p and hsa-miR-125b-2-3p. A total of 18 hub genes and seven hub miRNAs were predominantly expressed in tumor stroma. Proteoglycans in cancer, focal adhesion, extracellular matrix (ECM–receptor interaction and so on were common pathways of the three modules. Hsa-let-7c-5p was located at the core of miRNA–gene network.Conclusion: These findings help to advance the understanding of tumor stroma in the progression of CAC and provide prognostic biomarkers as well as therapeutic targets. Keywords: colon adenocarcinoma, weighted gene co-expression network analysis, differentially expressed genes, differentially expressed miRNA, tumor stroma

Combining many interaction networks to predict gene function and analyze gene lists.

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Mostafavi, Sara; Morris, Quaid

2012-05-01

In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gene-based testing of interactions in association studies of quantitative traits.

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Li Ma

Full Text Available Various methods have been developed for identifying gene-gene interactions in genome-wide association studies (GWAS. However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene-gene interaction (GGG tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein-protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

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Jiang Wei

2008-08-01

Full Text Available Abstract Background With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. Results The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8 (p ≈ 0, desmin (DES (p = 2.71 × 10-6 and enolase 1 (ENO1 (p = 4.19 × 10-5], while two novel hub genes [RNA binding motif protein 9 (RBM9 (p = 1.50 × 10-4 and ribosomal protein L30 (RPL30 (p = 1.50 × 10-4] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO based analysis of the colon cancer-specific gene network and

Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.

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Jiang, Wei; Li, Xia; Rao, Shaoqi; Wang, Lihong; Du, Lei; Li, Chuanxing; Wu, Chao; Wang, Hongzhi; Wang, Yadong; Yang, Baofeng

2008-08-10

With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p approximately 0), desmin (DES) (p = 2.71 x 10(-6)) and enolase 1 (ENO1) (p = 4.19 x 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 x 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 x 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that

ADAMTS14 Gene Polymorphism and Environmental Risk in the Development of Oral Cancer.

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Shih-Chi Su

Full Text Available Oral cancer is a common malignancy that is shown to be causally associated with hereditary and acquired factors. ADAMTS14 is a member of the ADAMTS (a disintegrin-like and metalloproteinase domain with thrombospondin motifs metalloproteinase family that plays an important role in extracellular matrix (ECM assembly and degradation. Elevation or deficiency of certain ADAMTS proteinases has been known to be implicated in a wide range of pathological processes including atherosclerosis, arthritis, and cancer. The present study aimed to explore the impact of ADAMTS14 gene polymorphisms, combined with environmental risks on the susceptibility to oral tumorigenesis.Four single-nucleotide polymorphisms (SNPs of the ADAMTS14 gene, including rs10823607, rs12774070, rs4747096, and rs61573157 were evaluated from 1200 normal controls and 850 patients with oral cancer. We failed to detect a significant association of four individual SNPs with oral cancer between case and control group. However, while considering behavioral exposure of environmental carcinogens, the presence of four ADAMTS14 SNPs, combined with betel nut chewing and/or smoking, profoundly leveraged the risk of oral cancer. Moreover, we observed a significant association of rs12774070, which is predicted to alter the expression and function of ADAMTS14 by in silico and bioinformatics analyses, with poor tumor cell differentiation (AOR: 0.59; 95% CI: 0.38-0.92; p = 0.02 in patients who chewed betel nuts.These results implicate the interaction between ADAMTS14 gene polymorphisms and environmental mutagens as a risk factor of oral tumorigenesis and suggest a correlation of rs12774070 with the degree of oral tumor cell differentiation.

Inherited variation in circadian rhythm genes and risks of prostate cancer and three other cancer sites in combined cancer consortia.

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Gu, Fangyi; Zhang, Han; Hyland, Paula L; Berndt, Sonja; Gapstur, Susan M; Wheeler, William; Ellipse Consortium, The; Amos, Christopher I; Bezieau, Stephane; Bickeböller, Heike; Brenner, Hermann; Brennan, Paul; Chang-Claude, Jenny; Conti, David V; Doherty, Jennifer Anne; Gruber, Stephen B; Harrison, Tabitha A; Hayes, Richard B; Hoffmeister, Michael; Houlston, Richard S; Hung, Rayjean J; Jenkins, Mark A; Kraft, Peter; Lawrenson, Kate; McKay, James; Markt, Sarah; Mucci, Lorelei; Phelan, Catherine M; Qu, Conghui; Risch, Angela; Rossing, Mary Anne; Wichmann, H-Erich; Shi, Jianxin; Schernhammer, Eva; Yu, Kai; Landi, Maria Teresa; Caporaso, Neil E

2017-11-01

Circadian disruption has been linked to carcinogenesis in animal models, but the evidence in humans is inconclusive. Genetic variation in circadian rhythm genes provides a tool to investigate such associations. We examined associations of genetic variation in nine core circadian rhythm genes and six melatonin pathway genes with risk of colorectal, lung, ovarian and prostate cancers using data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) network. The major results for prostate cancer were replicated in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial, and for colorectal cancer in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). The total number of cancer cases and controls was 15,838/18,159 for colorectal, 14,818/14,227 for prostate, 12,537/17,285 for lung and 4,369/9,123 for ovary. For each cancer site, we conducted gene-based and pathway-based analyses by applying the summary-based Adaptive Rank Truncated Product method (sARTP) on the summary association statistics for each SNP within the candidate gene regions. Aggregate genetic variation in circadian rhythm and melatonin pathways were significantly associated with the risk of prostate cancer in data combining GAME-ON and PLCO, after Bonferroni correction (p pathway  circadian rhythm pathway in GAME-ON (p pathway  = 0.021); this association was not confirmed in GECCO (p pathway  = 0.76) or the combined data (p pathway  = 0.17). No significant association was observed for ovarian and lung cancer. These findings support a potential role for circadian rhythm and melatonin pathways in prostate carcinogenesis. Further functional studies are needed to better understand the underlying biologic mechanisms. © 2017 UICC.

Progress in nonviral gene therapy for breast cancer and what comes next?

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Bottai, Giulia; Truffi, Marta; Corsi, Fabio; Santarpia, Libero

2017-05-01

The possibility of correcting defective genes and modulating gene expression through gene therapy has emerged as a promising treatment strategy for breast cancer. Furthermore, the relevance of tumor immune microenvironment in supporting the oncogenic process has paved the way for novel immunomodulatory applications of gene therapy. Areas covered: In this review, the authors describe the most relevant delivery systems, focusing on nonviral vectors, along with the description of the major approaches used to modify target cells, including gene transfer, RNA interference (RNAi), and epigenetic regulation. Furthermore, they highlight innovative therapeutic strategies and the application of gene therapy in clinical trials for breast cancer. Expert opinion: Gene therapy has the potential to impact breast cancer research. Further efforts are required to increase the clinical application of RNAi-based therapeutics, especially in combination with conventional treatments. Innovative strategies, including genome editing and stem cell-based systems, may contribute to translate gene therapy into clinical practice. Immune-based approaches have emerged as an attractive therapeutic opportunity for selected breast cancer patients. However, several challenges need to be addressed before considering gene therapy as an actual option for the treatment of breast cancer.

A Catalog of Genes Homozygously Deleted in Human Lung Cancer and the Candidacy of PTPRD as a Tumor Suppressor Gene

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Kohno, Takashi; Otsuka, Ayaka; Girard, Luc; Sato, Masanori; Iwakawa, Reika; Ogiwara, Hideaki; Sanchez-Cespedes, Montse; Minna, John D.; Yokota, Jun

2010-01-01

A total of 176 genes homozygously deleted in human lung cancer were identified by DNA array-based whole genome scanning of 52 lung cancer cell lines and subsequent genomic PCR in 74 cell lines, including the 52 cell lines scanned. One or more exons of these genes were homozygously deleted in one (1%) to 20 (27%) cell lines. These genes included known tumor suppressor genes, e.g., CDKN2A/p16, RB1, and SMAD4, and candidate tumor suppressor genes whose hemizygous or homozygous deletions were reported in several types of human cancers, such as FHIT, KEAP1, and LRP1B/LRP-DIP. CDKN2A/p16 and p14ARF located in 9p21 were most frequently deleted (20/74, 27%). The PTPRD gene was most frequently deleted (8/74, 11%) among genes mapping to regions other than 9p21. Somatic mutations, including a nonsense mutation, of the PTPRD gene were detected in 8/74 (11%) of cell lines and 4/95 (4%) of surgical specimens of lung cancer. Reduced PTPRD expression was observed in the majority (>80%) of cell lines and surgical specimens of lung cancer. Therefore, PTPRD is a candidate tumor suppressor gene in lung cancer. Microarray-based expression profiling of 19 lung cancer cell lines also indicated that some of the 176 genes, such as KANK and ADAMTS1, are preferentially inactivated by epigenetic alterations. Genetic/epigenetic as well as functional studies of these 176 genes will increase our understanding of molecular mechanisms behind lung carcinogenesis. PMID:20073072

Prostate Cancer Epigenetics: A Review on Gene Regulation

OpenAIRE

Diaw, Lena; Woodson, Karen; Gillespie, John W.

2007-01-01

Prostate cancer is the most common cancer in men in western countries, and its incidence is increasing steadily worldwide. Molecular changes including both genetic and epigenetic events underlying the development and progression of this disease are still not well understood. Epigenetic events are involved in gene regulation and occur through different mechanisms such as DNA methylation and histone modifi cations. Both DNA methylation and histone modifi cations affect gene regulation and play ...

Leveraging gene-environment interactions and endotypes for asthma gene discovery

DEFF Research Database (Denmark)

Bønnelykke, Klaus; Ober, Carole

2016-01-01

, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs......Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also...... heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes...

Correlation between the methylation of APC gene promoter and colon cancer.

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Li, Bing-Qiang; Liu, Peng-Peng; Zhang, Cai-Hua

2017-08-01

The present study was planned to explore the correlation between the methylation of APC (adenomatous polyposis coli) and colon carcinogenesis. Colon cancer tissues and tumor-adjacent normal tissues of 60 colon cancer patients (who received surgical operation in our hospital from January 2012 to December 2014) were collected. SW1116 cells in human colon cancer tissues were selected for culturing. 5-aza-2c-deoxycytidine (5-aza-dC) was utilized as an inhibitor of the methylation for APC gene. Methylation specific PCR (MSP) was utilized for detection of APC methylation in SW1116 cells. The MTT and Transwell assays were performed to detect the effect of the methylation of APC gene on the proliferation and invasive abilities of SW1116 cells. The correlation between the methylation of APC gene and pathological parameters of colon cancer patients was analyzed. MSP results revealed that 41 cases (68.33%) showed methylation of APC gene in colon cancer tissues. No methylation of APC gene was found in tumor-adjacent normal tissues. 5-aza-dC was able to inhibit the methylation of APC gene in SW1116 cells. APC gene methylation was correlated with tumor size, differentiation degree, lymph node metastasis and Dukes staging. In conclusion, the levels of the methylation of APC in colon cancer tissues and SW1116 cells are relatively high. The methylation of APC promoted the proliferation and invasion abilities of SW1116 cells. Furthermore, methylation is correlated with a variety of clinicopathological features of colon cancer patients.

Identification of valid reference genes for gene expression studies of human stomach cancer by reverse transcription-qPCR

International Nuclear Information System (INIS)

Rho, Hyun-Wook; Lee, Byoung-Chan; Choi, Eun-Seok; Choi, Il-Ju; Lee, Yeon-Su; Goh, Sung-Ho

2010-01-01

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a powerful method for the analysis of gene expression. Target gene expression levels are usually normalized to a consistently expressed reference gene also known as internal standard, in the same sample. However, much effort has not been expended thus far in the search for reference genes suitable for the study of stomach cancer using RT-qPCR, although selection of optimal reference genes is critical for interpretation of results. We assessed the suitability of six possible reference genes, beta-actin (ACTB), glyceraldehydes-3-phosphate dehydrogenase (GAPDH), hypoxanthine phosphoribosyl transferase 1 (HPRT1), beta-2-microglobulin (B2M), ribosomal subunit L29 (RPL29) and 18S ribosomal RNA (18S rRNA) in 20 normal and tumor stomach tissue pairs of stomach cancer patients and 6 stomach cancer cell lines, by RT-qPCR. Employing expression stability analyses using NormFinder and geNorm algorithms we determined the order of performance of these reference genes and their variation values. This RT-qPCR study showed that there are statistically significant (p < 0.05) differences in the expression levels of HPRT1 and 18S rRNA in 'normal-' versus 'tumor stomach tissues'. The stability analyses by geNorm suggest B2M-GAPDH, as best reference gene combination for 'stomach cancer cell lines'; RPL29-HPRT1, for 'all stomach tissues'; and ACTB-18S rRNA, for 'all stomach cell lines and tissues'. NormFinder also identified B2M as the best reference gene for 'stomach cancer cell lines', RPL29-B2M for 'all stomach tissues', and 18S rRNA-ACTB for 'all stomach cell lines and tissues'. The comparisons of normalized expression of the target gene, GPNMB, showed different interpretation of target gene expression depend on best single reference gene or combination. This study validated RPL29 and RPL29-B2M as the best single reference

Identification of valid reference genes for gene expression studies of human stomach cancer by reverse transcription-qPCR

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Lee Yeon-Su

2010-05-01

Full Text Available Abstract Background Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR is a powerful method for the analysis of gene expression. Target gene expression levels are usually normalized to a consistently expressed reference gene also known as internal standard, in the same sample. However, much effort has not been expended thus far in the search for reference genes suitable for the study of stomach cancer using RT-qPCR, although selection of optimal reference genes is critical for interpretation of results. Methods We assessed the suitability of six possible reference genes, beta-actin (ACTB, glyceraldehydes-3-phosphate dehydrogenase (GAPDH, hypoxanthine phosphoribosyl transferase 1 (HPRT1, beta-2-microglobulin (B2M, ribosomal subunit L29 (RPL29 and 18S ribosomal RNA (18S rRNA in 20 normal and tumor stomach tissue pairs of stomach cancer patients and 6 stomach cancer cell lines, by RT-qPCR. Employing expression stability analyses using NormFinder and geNorm algorithms we determined the order of performance of these reference genes and their variation values. Results This RT-qPCR study showed that there are statistically significant (p Conclusion This study validated RPL29 and RPL29-B2M as the best single reference genes and combination, for RT-qPCR analysis of 'all stomach tissues', and B2M and B2M-GAPDH as the best single reference gene and combination, for 'stomach cancer cell lines'. Use of these validated reference genes should provide more exact interpretation of differential gene expressions at transcription level in stomach cancer.

RKIP Suppresses Breast Cancer Metastasis to the Bone by Regulating Stroma-Associated Genes

International Nuclear Information System (INIS)

Bevilacqua, E.; Frankenberger, C.A.; Rosner, M.R.

2012-01-01

In the past decade cancer research has recognized the importance of tumor stroma interactions for the progression of primary tumors to an aggressive and invasive phenotype and for colonization of new organs in the context of metastasis. The dialogue between tumor cells and the surrounding stroma is a complex and dynamic phenomenon, as many cell types and soluble factors are involved. While the function of many of the players involved in this cross talk have been studied, the regulatory mechanisms and signaling pathways that control their expression have not been investigated in depth. By using a novel, interdisciplinary approach applied to the mechanism of action of the metastasis suppressor, Raf kinase inhibitory protein (RKIP), we identified a signaling pathway that suppresses invasion and metastasis through regulation of stroma-associated genes. Conceptually, the approach we developed uses a master regulator and expression arrays from breast cancer patients to formulate hypotheses based on clinical data. Experimental validation is followed by further bioinformatics analysis to establish the clinical significance of discoveries. Using RKIP as an example we show here that this multi-step approach can be used to identify gene regulatory mechanisms that affect tumor-stroma interactions that in turn influence metastasis to the bone or other organs

Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways

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Q. Wang

Full Text Available Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC based on the functional dependency among pathways. Protein-protein interaction (PPI information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN, where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.

Association testing to detect gene-gene interactions on sex chromosomes in trio data

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Yeonok eLee

2013-11-01

Full Text Available Autism Spectrum Disorder (ASD occurs more often among males than females in a 4:1 ratio. Among theories used to explain the causes of ASD, the X chromosome and the Y chromosome theories attribute ASD to X-linked mutation and the male-limited gene expressions on the Y chromosome, respectively. Despite the rationale of the theory, studies have failed to attribute the sex-biased ratio to the significant linkage or association on the regions of interest on X chromosome. We further study the gender biased ratio by examining the possible interaction effects between two genes in the sex chromosomes. We propose a logistic regression model with mixed effects to detect gene-gene interactions on sex chromosomes. We investigated the power and type I error rates of the approach for a range of minor allele frequencies and varying linkage disequilibrium between markers and QTLs. We also evaluated the robustness of the model to population stratification. We applied the model to a trio-family data set with an ASD affected male child to study gene-gene interactions on sex chromosomes.

Gene set-based module discovery in the breast cancer transcriptome

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Zhang Michael Q

2009-02-01

Full Text Available Abstract Background Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data. Results In order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2 is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells. Conclusion These results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.

Risk of metachronous colon cancer following surgery for rectal cancer in mismatch repair gene mutation carriers.

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Win, Aung Ko; Parry, Susan; Parry, Bryan; Kalady, Matthew F; Macrae, Finlay A; Ahnen, Dennis J; Young, Graeme P; Lipton, Lara; Winship, Ingrid; Boussioutas, Alex; Young, Joanne P; Buchanan, Daniel D; Arnold, Julie; Le Marchand, Loïc; Newcomb, Polly A; Haile, Robert W; Lindor, Noralane M; Gallinger, Steven; Hopper, John L; Jenkins, Mark A

2013-06-01

Despite regular surveillance colonoscopy, the metachronous colorectal cancer risk for mismatch repair (MMR) gene mutation carriers after segmental resection for colon cancer is high and total or subtotal colectomy is the preferred option. However, if the index cancer is in the rectum, management decisions are complicated by considerations of impaired bowel function. We aimed to estimate the risk of metachronous colon cancer for MMR gene mutation carriers who underwent a proctectomy for index rectal cancer. This retrospective cohort study comprised 79 carriers of germline mutation in a MMR gene (18 MLH1, 55 MSH2, 4 MSH6, and 2 PMS2) from the Colon Cancer Family Registry who had had a proctectomy for index rectal cancer. Cumulative risks of metachronous colon cancer were calculated using the Kaplan-Meier method. During median 9 years (range 1-32 years) of observation since the first diagnosis of rectal cancer, 21 carriers (27 %) were diagnosed with metachronous colon cancer (incidence 24.25, 95 % confidence interval [CI] 15.81-37.19 per 1,000 person-years). Cumulative risk of metachronous colon cancer was 19 % (95 % CI 9-31 %) at 10 years, 47 (95 % CI 31-68 %) at 20 years, and 69 % (95 % CI 45-89 %) at 30 years after surgical resection. The frequency of surveillance colonoscopy was 1 colonoscopy per 1.16 years (95 % CI 1.01-1.31 years). The AJCC stages of the metachronous cancers, where available, were 72 % stage I, 22 % stage II, and 6 % stage III. Given the high metachronous colon cancer risk for MMR gene mutation carriers diagnosed with an index rectal cancer, proctocolectomy may need to be considered.

A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

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Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

2016-07-01

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All