Cai, Qiuyin

Abstract Background: The etiology of colorectal cancer is not fully understood. Methods: Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. Results: Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini–Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10−8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60−2.36, P = 2.6 × 10−11; OREUR: 1.01, 95% CI, 0.99−1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer. Conclusions: This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. Impact: The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

AbstractBackground:The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.Methods:Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.Results:We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.Conclusions:We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.Impact:A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.