Abstract
BACKGROUND: Ovarian cancer (OC), characterized by a high mortality rate and limited treatment options, underscores the urgent need to identify novel therapeutic targets to advance individualized precision therapy. Exploring the potential of antidiabetic drug target genes as therapeutic candidates may expand the treatment repertoire of diverse OC subtypes. METHODS: Leveraging datasets involving the Ovarian Cancer Association Consortium, the eQTLGen consortium, and the Genotype-Tissue Expression database, we implemented an integrated analytical framework combining two-sample Mendelian randomization (MR), summary data-based MR, as well as colocalization analysis to assess the association between target genes of antidiabetic drugs with the risk and survival of different ovarian cancer subtypes. Positive control analysis, replication analysis, MR-Egger regression, Bonferroni correction, and MR-PRESSO outlier test were employed to further validate the robustness of the associations. RESULTS: We systematically analyzed the associations of nine OC phenotypes with the target genes from nine antidiabetic drugs, including sulfonylureas, metformin, alpha-glucosidase inhibitors (AGIs), thiazolidinediones (TZDs), dipeptidyl peptidase 4 inhibitors (DPP4i), glucagon-like peptide-1 analogues (GLP-1A), insulin, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and other drugs. Notably, multiple target genes showed consistent and significant associations with specific OC risk: AKR1A1 with Low grade serous OC; HMGCR and KCNJ11 with clear cell OC; ITGAL and AKR1B1 with mucinous OC; and AKR1A1 and ITGAL with endometrioid OC. Although high grade serous OC risk was linked to certain genes in only one method, its survival was associated with DPP4 in two approaches. CONCLUSION: This study reveals marked subtype-specific heterogeneity in the genetic relationships between antidiabetic targets and ovarian cancer (OC), pointing to a direction for future translational research into drug repurposing for subtype-specific applications. These findings support a metabolic basis in OC progression and may inform the development of tailored therapeutic strategies based on pathological subtypes.