Assessing the impact of metabolomic markers on gastric cancer risk: a two-sample Mendelian randomization study.

[BACKGROUND] This study aimed to examine the relationship between genetically predicted metabolite levels and gastric cancer (GC) risk using Mendelian randomization (MR), and to identify the metabolic pathways potentially involved.

[METHODS] We selected genetic instruments for metabolites from 64 genome-wide association studies covering 362,750 participants. A two-sample MR design was applied to evaluate the associations with GC using summary-level data from a combined analysis of the UK Biobank and FinnGen. The primary analysis relied on the inverse-variance weighted method, while the median-weighted and MR-Egger methods were used to account for potential violations of instrumental variable assumptions and provide the estimate even when a subset of instruments was invalid. The MR-Egger intercept test was performed to detect directional pleiotropy. Metabolites showing significant associations with GC were further examined using pathway enrichment analysis to identify relevant metabolic and lipid processes.

[RESULTS] MR analyses identified 25 and 17 metabolites that were positively and inversely associated with GC risk, respectively. Notably, hexanoylcarnitine and cis-4-decenoylcarnitine were strongly associated with increased risk, whereas pregnanediol disulfate, acetylcarnitine, prolyl-hydroxyproline, and X-18914 were associated with reduced risk, with no evidence of heterogeneity or directional pleiotropy. Enrichment analyses highlighted key metabolic pathways, including cysteine and methionine catabolism, beta-oxidation of pristanoyl-CoA (coenzyme A), oxidation of branched-chain fatty acids, and peroxisomal lipid metabolism.

[CONCLUSION] This study identified a set of genetically predicted metabolites associated with GC risk, highlighting the potential utility of metabolite panels and lipid-based biomarkers for risk stratification and early detection. However, further standardization and extensive validation are necessary prior to clinical application.