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- Jackson, Sarah S., et al.
(författare)
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Anthropometric Risk Factors for Cancers of the Biliary Tract in the Biliary Tract Cancers Pooling Project
- 2019
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Ingår i: Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 0008-5472 .- 1538-7445. ; 79:15, s. 3973-3982
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Tidskriftsartikel (refereegranskat)abstract
- Biliary tract cancers are rare but highly fatal with poorly understood etiology. Identifying potentially modifiable risk factors for these cancers is essential for prevention. Here we estimated the relationship between adiposity and cancer across the biliary tract, including cancers of the gallbladder (GBC), intrahepatic bile ducts (IHBDC), extrahepatic bile ducts (EHBDC), and the ampulla of Vater (AVC). We pooled data from 27 prospective cohorts with over 2.7 million adults. Adiposity was measured using baseline body mass index (BMI), waist circumference, hip circumference, waist-to-hip, and waist-to-height ratios. HRs and 95% confidence intervals (95% CI) were estimated using Cox proportional hazards models adjusted for sex, education, race, smoking, and alcohol consumption with age as the time metric and the baseline hazard stratified by study. During 37,883,648 person-years of follow-up, 1,343 GBC cases, 1,194 EHBDC cases, 784 IHBDC cases, and 623 AVC cases occurred. For each 5 kg/m(2) increase in BMI, there were risk increases for GBC (HR = 1.27; 95% CI, 1.19-1.36), IHBDC (HR = 1.32; 95% CI, 1.21-1.45), and EHBDC (HR = 1.13; 95% CI, 1.03-1.23), but not AVC (HR = 0.99; 95% CI, 0.88-1.11). Increasing waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio were associated with GBC and IHBDC but not EHBDC or AVC. These results indicate that adult adiposity is associated with an increased risk of biliary tract cancer, particularly GBC and IHBDC. Moreover, they provide evidence for recommending weight maintenance programs to reduce the risk of developing these cancers. Significance: These findings identify a correlation between adiposity and biliary tract cancers, indicating that weight management programs may help minimize the risk of these diseases.
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| 2. |
- Walsh, Naomi, et al.
(författare)
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Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer
- 2019
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press. - 0027-8874 .- 1460-2105. ; 111:6
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Tidskriftsartikel (refereegranskat)abstract
- Background: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes.Methods: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.Results: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.Conclusion: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.
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