孟德尔随机化
医学
癌症
代谢组学
代谢组
肿瘤科
遗传关联
代谢物
生物信息学
遗传学
内科学
单核苷酸多态性
生物
基因型
基因
遗传变异
作者
Sulan Chen,Bin Zhang,Song Wang,Ming Yang,Qiaohui Shen,Rui Zhang,Yan Leng
出处
期刊:Medicine
[Wolters Kluwer]
日期:2025-07-18
卷期号:104 (29): e43339-e43339
标识
DOI:10.1097/md.0000000000043339
摘要
Notably, metabolic dysregulation stands as a prominent characteristic of cancer. The identification of biomarkers through blood metabolomics presents a novel approach for the diagnosis and treatment of gastric cancer. We performed a 2-sample Mendelian randomization (MR) analysis to assess the causality from genetically proxied 486 blood metabolites to gastric cancer. In this study, MR analysis was employed to assess the correlation between 486 serum metabolites and gastric cancer. Five different methods, namely inverse-variance weighting, MR-Egger method, weighted median method, simple mode method, and weighted mode method, were utilized for evaluation. Sensitivity analysis was conducted, encompassing heterogeneity testing, horizontal pleiotropy testing, and leave-one-out testing. Furthermore, the study encompassed linkage disequilibrium score (LDSC) genetic association and directionality assessment, metabolic pathway analysis, and reverse MR analysis. The findings revealed the presence of 21 metabolites, comprising 14 known metabolites and 7 unidentified metabolites that potentially play a causal role in gastric cancer. The reverse MR and directional assessment indicated the absence of reverse causality between gastric cancer and the candidate metabolites. Moreover, the LDSC genetic association solely identified a genetic association between gastric cancer and the unknown metabolite X-11315. Additionally, the metabolic pathway analysis identified 3 pathways that may be implicated in the development of the disease. We observed negative correlations between 12 serum metabolites and the risk of gastric cancer, while 9 serum metabolites showed positive correlations. Notably, 3-methyl-2-oxovalerate exhibited promising therapeutic potential, whereas 2-aminobutyrate displayed a higher risk factor. The integration of genomics and metabolomics in our investigation offers novel insights into the underlying mechanisms of gastric cancer, thereby holding significant implications for the screening and prevention of this disease.
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