Dataset Information

Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk.

ABSTRACT:

Background

Several cancer-associated loci identified from genome-wide association studies (GWAS) have been associated with risks of multiple cancer sites, suggesting pleiotropic effects. We investigated whether GWAS-identified risk variants for other common cancers are associated with risk of esophageal adenocarcinoma (EA) or its precursor, Barrett's esophagus.

Methods

We examined the associations between risks of EA and Barrett's esophagus and 387 SNPs that have been associated with risks of other cancers, by using genotype imputation data on 2,163 control participants and 3,885 (1,501 EA and 2,384 Barrett's esophagus) case patients from the Barrett's and Esophageal Adenocarcinoma Genetic Susceptibility Study, and investigated effect modification by smoking history, body mass index (BMI), and reflux/heartburn.

Results

After correcting for multiple testing, none of the tested 387 SNPs were statistically significantly associated with risk of EA or Barrett's esophagus. No evidence of effect modification by smoking, BMI, or reflux/heartburn was observed.

Conclusions

Genetic risk variants for common cancers identified from GWAS appear not to be associated with risks of EA or Barrett's esophagus.

Impact

To our knowledge, this is the first investigation of pleiotropic genetic associations with risks of EA and Barrett's esophagus.

SUBMITTER: Lee E

PROVIDER: S-EPMC4648999 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk.

Lee Eunjung E Stram Daniel O DO Ek Weronica E WE Onstad Lynn E LE MacGregor Stuart S Gharahkhani Puya P Ye Weimin W Lagergren Jesper J Shaheen Nicholas J NJ Murray Liam J LJ Hardie Laura J LJ Gammon Marilie D MD Chow Wong-Ho WH Risch Harvey A HA Corley Douglas A DA Levine David M DM Whiteman David C DC Bernstein Leslie L Bird Nigel C NC Vaughan Thomas L TL Wu Anna H AH

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20150912 11

<h4>Background</h4>Several cancer-associated loci identified from genome-wide association studies (GWAS) have been associated with risks of multiple cancer sites, suggesting pleiotropic effects. We investigated whether GWAS-identified risk variants for other common cancers are associated with risk of esophageal adenocarcinoma (EA) or its precursor, Barrett's esophagus.<h4>Methods</h4>We examined the associations between risks of EA and Barrett's esophagus and 387 SNPs that have been associated w ...[more]

PMID: 26364162

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Project description:Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package "clusterProfiler." SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction (P fdr < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90-0.97, P = 7.60 × 10-4), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03-1.11, P = 1.00 × 10-3), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86-0.96, P = 7.10 × 10-4), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87-0.94, P = 1.00 × 10-7), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90-0.96, P = 1.20 × 10-4), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03-1.11, P = 4.30 × 10-4). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.

Project description:PurposeEsophageal adenocarcinoma (EAC) is the most common type of esophageal cancer in Western countries. It is usually detected at an advanced stage and has a poor prognosis. The aim of this study was to identify key genes and miRNAs in EAC.MethodsThe mRNA microarray data sets GSE1420, GSE26886, and GSE92396 and miRNA data set GSE16456 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were obtained using R software. Functional enrichment analysis was performed using the DAVID database. A protein-protein interaction (PPI) network and functional modules were established using the STRING database and visualized by Cytoscape. The targets of the DEMs were predicted using the miRecords database, and overlapping genes between DEGs and targets were identified. The prognosis-related overlapping genes were identified using Kaplan-Meier analysis and Cox proportional hazard analysis based on The Cancer Genome Atlas (TCGA) database. The differential expression of these prognosis-related genes was validated using the expression matrix in the TCGA database.ResultsSeven hundred and fifteen DEGs were obtained, consisting of 313 upregulated and 402 downregulated genes. The PPI network consisted of 281 nodes; 683 edges were constructed and 3 functional modules were established. Forty-four overlapping genes and 56 miRNA- mRNA pairs were identified. Five genes, FAM46A, RAB15, SLC20A1, IL1A, and ACSL1, were associated with overall survival or relapse-free survival. FAM46A and IL1A were found to be independent prognostic indicators for overall survival, and FAM46A, RAB15, and SLC20A1 were considered independent prognostic indicators for relapse-free survival. Among them, the overexpression of RAB15 and SLC20A1 and lower expression of ACSL1 were also identified in EAC tissues based on the expression matrix in the TCGA database.ConclusionThese prognosis-related genes and differentially expressed miRNA have provided potential biomarkers for EAC diagnosis and treatment.

Dataset Information

Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk.

Background

Methods

Results

Conclusions

Impact

Publications

Pleiotropic analysis of cancer risk loci on esophageal adenocarcinoma risk.

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