Genetically predicted 1,400 blood metabolites related to the risk of ovarian cancer: A Mendelian randomization study

Introduction Ovarian cancer, classified as a malignant tumor, represents a major threat to women's health. The factors contributing to its development are very diverse, among the notable characteristics of cancer are metabolic disorders, but evidence linking them causally to ovarian cancer remains insufficient. The aim is to identify potential biomarkers for early screening and targeted therapeutic strategy. Material and methods This study employed a GWAS and applied a two-sample MR analysis. Causality was primarily assessed using random IVW. Cross-validation was conducted with MR-Egger, weighted median, and weighted mode approaches. The MR-Egger intercept and Cochran’s Q test were adopted to assess heterogeneity and pleiotropy. Pathway enrichment analysis was performed using MetaboAnalyst 6.0. Results Our research identifies key metabolites as potential biomarkers for early screening and personalized therapy in ovarian cancer. By using MR, we establish causal links between ovarian cancer subtypes and plasma metabolites, offering valuable insights for clinical applications. After FDR correction, screening for one metabolite, 5-acetylamino-6-amino-3-methyluracil levels (AAMU). Also, significant metabolites were enriched to caffeine metabolism(p<0.05) as the most significant metabolic pathway in ovarian cancer. Conclusions We integrated genomic and metabolomic analyses to reveal causal associations of metabolites with ovarian cancer and its subtypes. Certain metabolites were indicated as prospective biomarkers for ovarian cancer