Mendelian randomization analysis to elucidate the causal relationship between small molecule metabolites and ovarian cancer risk

• DOI: 10.3389/fonc.2023.1291033
• Publication Date: 2023-11-28T08:32:32Z
• Authors: Xin Chang; Shijia Liu; Lu Han
• Keywords: single nucleotide polymorphisms; amino acids; ovarian cancer; Oncology; Mendelian randomization; biomarkers; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; RC254-282; 3. Good health

BackgroundSmall molecule metabolites are potential biomarkers for ovarian cancer. However, the causal relationship between small molecule metabolites and ovarian cancer remains unclear.MethodsSingle nucleotide polymorphisms (SNPs) correlated with 53 distinct small molecule metabolites were identified as instrumental variables (IVs) from comprehensive genome-wide association studies. Aggregate data encompassing 25,509 cases of ovarian cancer and 40,941 controls of European descent were procured from the Ovarian Cancer Association Consortium. To evaluate causative associations, four Mendelian randomization techniques—including inverse-variance weighted, weighted median, maximum likelihood, and MR-Egger regression—were employed.ResultsIn total, 242 SNPs were delineated as IVs for the small molecule metabolites under consideration. A significant association with the overarching risk of ovarian cancer was observed for six distinct metabolites. Hexadecenoylcarnitine and methioninesulfoxide were associated with a 32% and 31% reduced risk, respectively. Fifteen metabolites were linked to subtype ovarian cancers. For instance, both methionine sulfoxide and tetradecanoyl carnitine exhibited an inverse association with the risk of clear cell and high-grade serous ovarian cancers. Conversely, tryptophan demonstrated a 1.72-fold elevated risk for endometrioid ovarian cancer.ConclusionThis study identified several metabolites with putative causal effects on ovarian cancer risk using Mendelian randomization analysis. The findings provide insight into the etiological role of small molecule metabolites and highlight potential early detection biomarkers for ovarian cancer. Subsequent investigations are imperative to corroborate these findings and elucidate the underlying pathophysiological mechanisms.