Noninvasive early warning markers for ovarian cancer: a Mendelian randomization study.

OBJECTIVE

Early warning markers of ovarian cancer, especially those that can be detected through noninvasive methods, are very limited; therefore, this study aimed to investigate noninvasive biomarkers for ovarian cancer.

METHODS

We acquired "malignant neoplasm of ovary, excluding all cancers (controls excluding all cancers)" (2,339 cases and 222,078 controls) genome-wide association study summary statistics from FinnGen biobank. Metabolomes, gut microbiomes, immunophenotypes, circulating microRNAs (miRNAs), and 2 proteomes were obtained for Mendelian randomization (MR), and expression quantitative trait loci data were employed for summary-data-based Mendelian randomization (SMR). Specifically, we employed inverse variance weighted as the main method to calculate the MR effect. The robustness of the results was ensured through 3 other MR methods, multivariable MR and sensitivity analyses assessing heterogeneity and potential horizontal pleiotropy. Hub genes were identified using the String database and Cytoscape software. Potential mechanisms of ovarian cancer were identified via pathway enrichment analysis of the identified genes and miRNAs.

RESULTS

Based on MR and SMR analyses, we identified 3 metabolites, 5 immunophenotypes, 1 miRNA, and 5 hub genes, but no gut microbiota, as warning markers for ovarian cancer. Enrichment analysis indicated that pathways such as purine metabolism and the transforming growth factor-β signalling pathway may be involved in mechanisms regulating ovarian cancer.

CONCLUSION

Our results identified noninvasive predictors for ovarian cancer via MR, providing insights into early warning markers of clinical ovarian cancer.