Prospective metabolomics study of gastric cancer
In the present proposal, we herein are requesting plasma samples from PLCO to conduct a prospective metabolomics study of gastric cancer. We will collaborate with Metabolon to generate global untargeted metabolomics data using the samples from the Shanghai Women’s Health Study, Shanghai men’s Health Study, Southern Community Cohort Study, and PLCO. Association analysis will be performed within individual study using conditional/unconditional logistic regression, which will be determined by the design of each study. Only metabolites passed QC will be analyzed (e.g. CV < 25%). The individual results of metabolite-gastric cancer association will be combined by meta-analysis to generate ancestry-specific and cross-ancestry estimates. Stratified analysis will be conducted by sex, smoking, years between the enrollment and cancer diagnosis (e.g. < 5 years vs >=5 years), fasting status/time, tea/coffee drinking, site of gastric cancer, etc. Interaction effects will also be evaluated for selective variables mentioned above if evidence for heterogeneity is observed in the stratified analysis. Multiple comparisons will be corrected by taking the Benjamini-Hochberg False Discovery Rate procedure.
1) To identify novel metabolic biomarkers for gastric cancer risk. We will perform assays to profile circulating metabolites in ~1200 cases and 1500 controls using the DiscoveryHD4 platform offered by the Metabolon. We request 150ul of plasma samples from each of the cancer cases and their matched controls from the PLCO study.
2) To perform mediation analyses to evaluate inter-relationships of known lifestyle risk factors, blood metabolites, and cancer risk. We will use the metabolomics data described in Aim 1 and exposure data collected in the parent cohort studies for these analyses. We request relevant exposure data obtained in the PLCO for all PLCO cases and controls included in Aim 1.
3) Explore group specific associations and interaction effects by sex, H. pylori infection, and dietary intakes (tea/coffee intake) if the relevant data is available.
Xiang Shu (Memorial Sloan Kettering Cancer Center)
Xiao-ou Shu (Vanderbilt University Medical Center)