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. Plain Language Summary: Why was this study done? Ovarian cancer is a serious disease with limited therapeutic options. This study aimed to identify potential new treatment strategies by investigating existing medications. Given established links between diabetes and cancer, we examined whether genes targeted by common diabetes drugs influence the risk of developing different subtypes of ovarian cancer or affect patient survival. What did the researchers do and find? Using two sample Mendelian randomization, summary data-based Mendelian randomization, and colocalization analyses, we evaluated genetic data from large-scale studies to assess relationships between diabetes drug target genes and ovarian cancer subtypes. Our results demonstrate that these associations are highly subtype-specific. For example: Genes targeted by DPP4i (HMGCR) and sulfonylureas (KCNJ11) were linked to the risk of clear cell ovarian cancer. Genes targeted by other drugs were linked to the risk of mucinous (ITGAL, AKR1B1) and endometrioid (AKR1A1, ITGAL) ovarian cancers. DPP4 was associated with the survival of patients with high-grade serous ovarian cancer, which is the most common subtype in ovarian cancer. What do these results mean? These findings suggest that certain diabetes medications merit further investigation for their potential relevance to the risk and prognosis of specific ovarian cancer subtypes. This lays a conceptual foundation for future studies exploring treatment personalization based on precise molecular subtyping, which may ultimately contribute to more tailored clinical strategies. Keywords: ovarian cancer, antidiabetic target genes, gene expression, mendelian randomization
Tang et al. (Sun,) studied this question.