Subtype-Specific Causal Effects of Antidiabetic Drug Targets on Ovarian Cancer: Mendelian Randomization and Colocalization Evidence

Subtype-Specific Causal Effects of Antidiabetic Drug Targets on Ovarian Cancer: Mendelian Randomization and Colocalization Evidence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Subtype-Specific Causal Effects of Antidiabetic Drug Targets on Ovarian Cancer: Mendelian Randomization and Colocalization Evidence Enyu Tang, Jia Zeng, Xinlong Shi, Yani Wang, Yangchun Sun, Lingying Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7173550/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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 (SMR), 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. Results We systematically analyzed the target genes from nine antidiabetic drugs for associations with nine OC phenotypes. Notably, multiple target genes showed consistent and significant associations with specific OC subtypes. For instance, AKR1A1 was linked to low-grade serous OC; HMGCR and KCNJ11 to clear cell OC; ITGAL and AKR1B1 to mucinous OC; and AKR1A1 and ITGAL to endometrioid OC—with these associations supported by at least two MR methods. In contrast, the genetic associations for high-grade serous OC (HGSOC) incidence risk were less robust, as they were only supported by a single MR method. In contrast, the survival outcome of HGSOC demonstrated a more reliable genetic link, with DPP4 consistently implicated by both SMR and colocalization analysis, suggesting a potential role in prognosis rather than initiation. This divergence highlights subtype-specific biological mechanisms, in which antidiabetic drug targets may influence HGSOC progression differently from its development. Conclusion Our study presents the initial systematic findings highlighting the substantial heterogeneity in the relationships between OC and diabetes mellitus across different pathological subtypes by integrating multiple MR approaches. These findings offer a critical theoretical foundation for developing pathology-specific therapeutic targets for OC. ovarian cancer antidiabetic target genes gene expression mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ovarian cancer (OC) continues to be the deadliest gynecological cancer, with significant global health implications [ 1 ]. In 2020 alone, more than 300 thousand new cases of ovarian, fallopian tube, and primary peritoneal cancers were reported worldwide, resulting in 207,252 deaths [ 2 ]. Using the conventional first-line approach of surgery followed by platinum-based chemotherapy, nearly 70% of patients eventually experience recurrence with platinum resistance. Although the introduction of targeted drugs as maintenance therapy, including bevacizumab and poly (ADP-ribose) polymerase inhibitors, has improved overall survival [ 3 ], not all patients benefit from existing targeted drugs. This underscores the critical need to identify novel therapeutic targets to expand the treatment options for patients with OC. Metabolic reprogramming is essential for the initiation and progression of tumors, and numerous metabolites have been explored as potential therapeutic targets [ 4 ]. OC is closely linked to endocrine dysfunction, with prior research indicating that endocrine and metabolic factors significantly contribute to its pathogenesis [ 5 ]. Given this connection, it has been hypothesized that diabetes mellitus (DM) can elevate the risk of OC by disrupting endocrine system homeostasis. However, epidemiological studies exploring the relationship between DM and OC have yielded conflicting findings [ 6 – 7 ]. Several systematic review synthesizing data from multiple studies have provided more robust evidence, confirming a link between diabetes and an increased risk of OC [ 8 – 9 ]. These findings suggest that antidiabetic medications hold promise as potential interventions or adjuvant therapies for OC. Nevertheless, conflicting conclusions from studies on the impact of metformin, a widely used antidiabetic drug, on OC prognosis highlight the complexity and uncertainty of this field [ 10 – 11 ]. This emphasizes the need for innovative research perspectives and methodologies to elucidate the mechanisms linking diabetes and its therapeutic agents to OC development. Additionally, significant heterogeneity among OC subtypes must be carefully considered in such investigations. As a robust approach for causal inference, Mendelian randomization (MR) employs genetic variants as instrumental variables (IVs) to assess the causal impact of exposures on outcomes, effectively addressing the issues of confounding bias. Mendelian randomization (MR) has emerged as a powerful tool for inferring causal relationships between exposures and outcomes. By leveraging genetic variants, such as IVs, MR can assess the causal effect of an exposure on an outcome while minimizing confounding bias. IVs are presumed to influence the outcome exclusively via the exposure, without being affected by other confounding factors [ 12 ]. Thus, MR mimics the rigor of randomized controlled trials [ 13 ] and is particularly advantageous when exposures are difficult or expensive to measure [ 14 ]. Consequently, MR has gained widespread recognition in the fields of medical genetics and epidemiological research. In this study, we employed an integrative approach combining three MR methods involving two-sample MR, SMR, and colocalization analysis to systematically examine the relationship between antidiabetic target genes and OC. Our aim was to further clarify the potential link between diabetes mellitus and OC, while evaluating the therapeutic potential of antidiabetic targets in shaping the development and prognosis of OC across different pathological subtypes. This comprehensive approach provides a robust framework for the identification of novel therapeutic targets for OC treatment. Methods Identification of Antidiabetic Drug Target Genes and Determination of Study Outcomes The methodological structure of this study is summarized in Fig. 1 . The DrugBank pharmacogenetics database ( https://go.drugbank.com/ ) was utilized to identify genes associated with nine antidiabetic drugs: 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 (Table S1 ). Table S2 shows a complete overview of all drug target genes. This study utilized outcome data from a genome-wide association analysis (GWAS) performed by the Ovarian Cancer Association Consortium (OCAC) involving 66,450 European participants, including 25,509 ovarian epithelial cancer patients and 40,941 healthy controls (Table S3). This analysis not only included all ovarian epithelial cancers but also specifically examined different pathological subtypes of OC, such as 13307 High grade serous OC (HGSOC) cases, 1012 Low grade serous OC (LGSOC) cases, 1366 Clear cell OC cases, 2810 Endometrioid OC cases, and 2,566 Mucinous OC cases [ 15 ]. Additionally, 11,311 patients with clinical prognostic information were utilized for GWAS analysis of overall survival, either for HGSOC cases only, All OC cases or All OC with adjustment for pathological type respectively [ 16 ]. Two-sample MR Two-sample MR studies were selected to identify the impact of Hb1Ac alteration by targeting specific genes using antidiabetic drugs on the risk and prognosis of OC. In MR analyses, genetic variation can serve as an IV if it satisfies the following fundamental conditions [ 17 ]: (i) the genetic variation correlates with the exposure; (ii) the variation does not affect the outcome through confounding factors; and (iii) the variation has no direct effect on the outcome but influences it indirectly solely by affecting the exposure (as depicted in the directed acyclic graph in the middle-left of the Fig. 1 ). To meet the requirements, the following is the detailed analysis process. Initially, single nucleotide polymorphisms (SNPs) within the cis-region (spanning ± 500 kb) of the target genes were retrieved from the prior GWAS of HbA1c in the UK Biobank cohort. These SNPs were then analyzed using a clumping window of 100 kb, with a significance threshold of P 0.01, r 2 10 to eliminate weak IVs. Target genes with at least one valid SNP were screened through TwoSampleMR package ( https://mrcieu.github.io/TwoSampleMR/ ). After removing SNPs in palindromic and compatible alleles, the remaining SNPs were used as IVs for the corresponding target genes (Table S4) [ 18 ]. The same method was applied during the replication analysis for Two-sample MR quality control. When two neighboring genes shared IVs due to overlapping cis-regions, they were presented together and separated by a slash, such as ‘VEGFA/SLC29A1’. Subsequently, positive control MR analyses were carried out to further exclude target genes that had no significant association with either blood glucose (met-d-Glucose, ebi-a -GCST90014005) or type 2 diabetes mellitus (T2DM) (ebi-a-GCST006867, finn-b-E4_DM2_STRICT) [ 19 ]. (Tables S3 and S5) For target genes with at least two effective IVs, the multiplicative random effects inverse variance weighted (IVW) was employed as principal analysis, while Wald ratio MR was used for those with only one IV[ 20 – 21 ]. To corroborate the results, we performed multiple sensitivity assessments utilizing various methodologies, specifically the IVW with fixed effects, along with weighted median and mode estimation techniques [ 22 ]. The final risk ratios (OR) and p-value obtained from these four approaches were compared to verify the robustness and reliability of the results. Two-stage methods, such as the IVW method, are the most effective analytical methods when IV assumptions are met [ 23 ], and thus should typically be used as the primary analytical approach. The weighted median estimator maintains robustness despite the inclusion of potentially invalid instrumental variables [ 24 ]. Statistical significance of the weighted mode estimator requires that most comparable causal effects originate from valid instrumental variables [ 25 ]. The heterogeneity of IVs was verified using the I 2 statistics and Q test. Horizontal pleiotropy was evaluated through MR-Egger regression's intercept examination. MR analyses quantified OC-related effects of per-SD decreases in genetically predicted HbA1c through specific drug targets [ 25 ], with additional scaling to per-SD reduction of random glucose (ebi-a-GCST90014005). Positive control analyses provided conversion coefficients (α) between these measures for each target. The effect of a per-SD reduction in random blood glucose induced by antidiabetic drug targeting of specific genes on OC (scaled β) was then calculated by multiplying β by 1/α. We established dual significance thresholds, with P values below 0.05 indicating nominal significance and values under 3×10⁻⁴ representing significance after multiple testing correction (Bonferroni correction: 0.05/18×9 to account for multiple comparisons across all drug target-phenotype combinations) to further rule out the possibility of false-positive results [ 26 ]. If multiple target genes of an antidiabetic drug all had valid IVs, these genes were included in a combined MR analysis to validate the overall drug effect. Comprehensive sensitivity testing and quality control procedures were implemented to confirm the stability of the findings. We implemented Multivariate MR (MVMR) to quantify the causal influence of individual exposures on outcomes by leveraging genetic instruments linked to several potentially confounding exposures. This approach helps avoid bias due to confounding factors, demonstrating that genetic variation does not influence outcomes through confounders [ 27 ]. Therefore, we applied MVMR analysis (Figure S1 ) to calculate the effect of Hb1Ac reduction through targeting specific genes by antidiabetic drugs on different OC phenotypes after adjusting for four confounders: body mass index, systolic blood pressure, smoking status, and alcohol-drinking status. Subsequent replication analysis also examined the association between HbA1c (Within family GWAS consortium, MAGIC), T2DM (DIAGRAM, FinnGen, Sílvia Bonàs-Guarch), and fasting glucose (MAGIC). SNPs within the cis-region (spanning ± 500 kb) of the target genes were obtained from the previous GWAS, and weak IVs were removed following the same screening criteria (Table S3). The causal links between drug targets and OC outcomes were further verified through replicated MR methods. Moreover, the presence of pleiotropic SNPs was investigated through MR-PRESSO outlier detection analysis, serving as a complementary sensitivity evaluation. SMR SMR is a summary data-based MR analysis method employed in expression quantitative trait loci (eQTL) research and GWAS studies. It serves to determine whether the impact magnitude of SNPs on the outcome is influenced by gene expression [ 28 ]. Through SMR analysis, we investigated the causal links between antidiabetic drug target gene expression and OC subtype-specific outcomes, including disease risk and survival, following the analytical approach represented in Fig. 1 's central directed acyclic graph. EQTL refers to a class of genetic loci, mostly SNPs, which can significantly influence the level of gene expression. The eQTLGen consortium encompasses gene expression data from 31,684 patients' blood samples and offers genetic variants linked to the expression of those genes [ 29 ]. This analysis focused on cis-eQTLs located within a 1-Mb range of the respective gene locations were used as IVs to ensure the correlation between SNPs and target genes. We then utilized heterogeneity in dependent instruments (HEIDI) test to identify linkage disequilibrium in pleiotropic relation and, thereby, to detect the presence of heterogeneity in SMR analyses [ 28 ]. To enhance the comprehensiveness of our gene expression investigation, we integrated eQTL data from the most recently released Genotype-Tissue Expression (GTEx) database. Specifically, data from PsychENCODE (n = 1387) and GTEx-ovary (n = 167), derived from brain and ovarian tissues respectively, were used for replication analyses. All of the above-mentioned analysis procedures adopted the preset parameters [ 30 ] ( https://yanglab.westlake.edu.cn/software/smr/#SMR&HEIDIanalysis ). P SMR < 0.05 was regarded as statistically significant for association, followed by the application of a multiple-test correction threshold (Bonferroni correction: 0.05 divided by total gene count used for SMR analysis) to further eliminate the possibility of false-positive results. Colocalization analysis Colocalization analysis is an integrative approach aimed at identifying genetic variants that may induce concurrent phenotypic changes in multiple molecules or other complex features [ 31 ]. Even if a genetic variant in a specific gene region shows associations with both the exposure and the outcome, this does not confirm that the same variant influences the two. These associations could arise from distinct causal variants correlated through linkage disequilibrium [ 32 ]. Colocalization analysis is especially valuable for assessing exposures, such as protein levels and gene expression, especially in MR analyses based on individual gene regions [ 33 ]. Thus, colocalization analysis can offer further support in elucidating the biological mechanism underlying the causal relationship between antidiabetic drug target genes and OC outcomes. This analysis employed a Bayesian model, estimating posterior probabilities for five potential scenarios: 1) H0: No connection to either trait; 2) Associated with trait A exclusively; 3) Associated with trait B exclusively; 4) Separate SNPs linked to trait A and trait B independently; 5) A common SNP associated with both trait A and trait B (middle-right of Fig. 1 ). Analyses were carried out utilizing the R package (coloc.abf algorithm, http://cran.rproject.org/web/packages/coloc ) with default parameters of p1 = 1 × 10⁻⁴, p2 = 1 × 10⁻⁴, and p12 = 1 × 10⁻³. A posterior probability of H4 (PPH4) over 0.75 was considered indicative of a robust colocalization relationship and a PPH4 over 0.6 as evidence of a moderate colocalization association. Finally, we summarized the performance of all genes in the above three MR analyses and listed the genes that were significantly associated with OC in at least two of them (shown at the bottom of the Fig. 1 ). The SMR analyses in this study were carried out using the smr-1.3.1-win software on a Windows system, while all other analyses were executed using R software (version 4.4.1) with packages including TwoSampleMR, coloc.abf, and others. This study relies on previously published data and publicly available databases. Ethical approval and informed consent were secured from the respective institutional review boards. Results Two-Sample MR: Screening Effective Drug Target Antidiabetic drugs were categorized into nine classes based on the DrugBank pharmacogenetics database, and their respective target genes are listed in Tables S1 and S2. A total of 21 target genes were identified as potentially associated with OC outcomes, including genes targeted by sulfonylureas (ABCC8/KCNJ11, ABCB11/LRP2, CPT1A, PPARG, INS, KCNJ1, V-EGFA/SLC29A1), TZDs (PPARG, VEGFA/SLC29A1, ESRRA, RXRB), insulin (ABCB11/LRP2), biguanides (SLC47A1), AGIs (GANC), DPP-4i (HMGCR, ITGAL), GLP-1RA (GLP1R), SGLT2i (SLC5A2, SLC5A1), and other drugs (RAMP1, RAMP2, RAMP3, GCK, AKR1A1) (Table S5). The corresponding SNPs for each target gene and their effects on HbA1c levels are detailed in Table S4. Among these, 18 target genes were selected for subsequent two-sample MR analyses, excluding KCNJ1, ESRRA, and RAMP1, which showed no significant association with blood glucose or T2DM in positive control analyses (Table S5). Two-Sample MR: Investigating Antidiabetic Drug Targets on OC Following a comprehensive analytical approach, which included principal analysis utilizing IVW (multiplicative random effects) or Ward Ratio, sensitivity analyses employing IVW(fixed-effect), weighted median, and weighted mode methods, as well as rigorous quality control screenings (F statistic > 10 and EAF > 0.01; Q test for heterogeneity 0.05; Bonferroni-corrected P < 3×10 − 4 ), we identified 16 significant targets linked to at least one OC outcome except for GANC and ABCB11/LRP2 (Fig. 2 A, Figure S2 -21, Table S6). Notably, VEGFA/SLC29A1 and PPARG for sulfonylureas/TZDs, CPT1A and ABCC8/KCNJ11 for sulfonylureas, RXRB for TZDs, SLC5A2 and SLC5A1 for SGLT2i, GLP1R for GLP-1RA, HMGCR and ITGAL for DPP4i, AKR1A1, RAMP2, RAMP3, and GCK for other drugs demonstrated significant associations with at least one OC phenotype and passed the significance threshold after multiple testing correction (P < 3×10 − 4 ) (Fig. 2 A, Figure S2 ). The impact of representative drug targets, as determined through principal component analysis, on various OC risk and survival outcomes is illustrated in Fig. 2 A. The per-SD reduction in HbA1c through targeting RXRB by TZDs was related to a decreased risk of All OC (OR: 0.528, 95% CI: 0.413 ~ 0.675, P < 3×10 − 4 ) and HGSOC (OR: 0.378, 95% CI: 0.26 ~ 0.549, P < 3×10 − 4 ), indicating a protective effect. Conversely, targeting VEGFA/SLC29A1 with sulfonylureas/TZDs, resulting in a per-SD decrease in HbA1c, was linked to an elevated risk of HGSOC (OR: 2.128, 95% CI: 1.415 ~ 3.199, P < 3×10 − 4 ), and worse survival for All OC (OR: 0.288, 95% CI: 0.138 ~ 0.6, P < 3×10 − 4 ) and HGSOC (OR: 0.197, 95% CI: 0.122 ~ 0.317, P < 3×10 − 4 ). Sulfonylureas/TZDs targeting PPARG were also significantly associated with reduced survival in All OC and an elevated risk of endometrioid (OR: 2.917, 95% CI: 1.596 ~ 5.333, P < 3×10 − 4 ). The effects of sulfonylureas targeting CPT1A on OC risk varied by pathotype, showing a decreased incidence of LGSOC but an increased incidence of endometrioid OC, alongside a negative correlation with survival in HGSOC (all P < 3 × 3×10 − 4 ). GLP-1RA targeting GLP-1R was associated with improved survival in HGSOC. Additionally, other drugs targeting RAMP2 were linked to an elevated risk of All OC, Clear cell OC, Mucinous OC, and Endometrioid OC, but a reduced risk of LGSOC (all P < 3×10 − 4 ). Conversely, targeting AKR1A1 by other drugs was correlated with a higher risk of All OC, HGSOC, LGSOC, and Endometrioid OC, and an increased risk of Mucinous OC, alongside decreased survival in HGSOC and All OC (all P < 3×10 − 4 ). DPP4i targeting HMGCR was significantly linked to a higher risk of clear cell OC and reduced survival of All OC patients(all P < 3×10 − 4 ). Sulfonylureas, TZDs, SGLT2i, and DPP4i, which act on multiple target genes, were also found to have significant associations with at least one of the OC phenotypes, although only the associations observed for DPP4i with OC phenotypes passed the multiple correction threshold (Fig. 2 B, Figure S2 and Table S6). Pooled targets of sulfonylureas showed a significant association with a higher risk of endometrioid OC, an elevated risk of LGSOC, as well as reduced survival in HGSOC and All OC subtypes (all P < 0.05). TZDs targeting SLC29A1, RXRB, and PPARG, had a protective effect on the HGSOC and All OC subtypes, however the effect was reversed for endometrioid OC (all P < 0.05). Moreover, co-targeting SLC5A1 and SLC5A2 by SGLT2i was linked to an elevated risk of All OC and HGSOC (all P < 0.05). In contrast, the pooled targets of DPP4i were linked to a reduced risk of mucinous OC and endometrioid OC (all P < 3×10⁻⁴). Two-Sample MR: Sensitivity Analysis and Quality Control In the MVMR analysis (Figure S1 , Figure S26), the targets of antidiabetic drugs were further validated to be significantly associated with specific OC subtypes, which was consistent with the principal analysis. Among these, RAMP2 and RXRB were related to both the risk and survival of All OC subtypes. Meanwhile, GCK was only correlated with the risk of All OC (P < 0.001). RXRB and GCK were linked to both the risk and survival of HGSOC. Additionally, VEGFA/SLC29A1 (P < 0.001), GLP1R (P < 0.001), and AKR1A1 (P < 0.05) were solely associated with HGSOC survival. Moreover, RAMP2 and GCK were significantly associated with the risk of mucinous OC (all P < 0.001). The risk of clear cell OC was found only associated with RAMP2 (P < 0.001). ITGAL (P < 0.001), AKR1A1 (P < 0.05), and PPARG (P < 0.05) were all associated with the risk of endometrioid OC. In terms of the replication analyses (Table S7, Figure S27-28), the associations of GCK with the survival and risk of HGSOC, GCK with the risk of All OC, LGSOC, and mucinous OC, PPARG with the risk of mucinous OC and endometrioid OC, and the survival of HGSOC and All OC in the MR results were replicated at least once. Table S6 provided the MR findings adjusted for random blood glucose levels. The results of the principal analyses were largely consistent with those obtained from the sensitivity analyses using the other three MR methods (Table S6, Figure S3-25). After the MR-PRESSO analysis (Table S8), the associations of the risk of HGSOC with SGLT2i targeting SLC5A2, as well as TZDs targeting the combined targets of SLC29A1, RXRB, and PPARG, were identified as having potential pleiotropy, although significant in the MR principal analysis. SMR: Antidiabetic Drug Target Gene Expression and OC The eQTLGen (blood) database was utilized for the primary analysis of the SMR (Fig. 3 A). The PsychENCODE (brain) (Fig. 3 B) and GTEx (ovary) (Fig. 3 C) databases were employed for replication analyses. First, in the primary analysis, a per-SD increase of ETFDH expression in the blood was associated not only with the risk of All OC (OR: 0.77, 95% CI: 0.63–0.95, P = 0.013) and HGSOC (OR: 0.74, 95% CI: 0.58–0.94, P = 0.013), but also with the prognosis of HGSOC (OR: 0.71, 95% CI: 0.52–0.97, P = 0.032). This finding implies that there may be a more intricate relationship between ETFDH expression and the progression of OC. Moreover, the expression of DDP4 was negatively correlated with the survival of HGSOC (OR: 0.59, 95% CI: 0.38–0.90, P = 0.015), and the expression of HMGCR was negatively linked to the risk of HGSOC (OR: 0.73, 95% CI: 0.57–0.94, P = 0.016) and All OC (OR: 0.73, 95% CI: 0.57–0.94, P = 0.016). The expression of SLC47A1 was associated with the higher risk of Endometrioid OC (OR: 1.38, 95% CI: 1.01–1.87, P = 0.042), and the expression of ABCA1 was associated with an increased risk of LGSOC (OR: 4.64, 95% CI: 1.76–12.24, P = 0.002). Conversely, HMGCR was negatively linked to the risk of Mucinous OC (OR: 0.36, 95% CI: 0.20–0.66, P = 0.0008), and SLC29A1 was negatively related with the risk of Endometrioid OC (OR: 0.39, 95% CI: 0.19–0.82, P = 0.013). (Fig. 3 A, Table S9) In the replication analyses, AK1B1, CFTR, KCNJ11, and GAA were significantly linked to at least one OC subtype, yet there was no overlap with the principal SMR results. A per-SD increase in AKR1B1 expression in the brain was associated with a significantly increased risk of Mucinous OC (OR: 1.5, 95% CI: 1.14–1.99, P = 0.004). CFTR was negatively associated with the risk of All OC (OR: 0.93, 95% CI: 0.87–1.00, P = 0.043). KCNJ11 was positively associated with the occurrence of All OC (OR: 1.13, 95% CI: 1.0–1.28, P = 0.047) and HGSOC (OR: 1.17, 95% CI: 1.01–1.35, P = 0.039). Additionally, a per-SD increase in GAA expression in the ovary was significantly associated with reduced HGSOC survival (OR: 0.92, 95% CI: 0.85–1.00, P = 0.047). (Fig. 3 B-C, Table S9) Colocalization Analysis: colocalization of the target gene expression with ovarian cancer Colocalization analysis was applied to ascertain whether the connection between the expression of specific genes and OC could be ascribed to the same causal genetic variants (Fig. 4 , Figure S29, TableS10). Figure 4 A shows that GCK, SLC47A1, CCN3, ETFDH, DPP4, and SIGMAR1 all co-localize with at least one of the OC outcomes, with a PPH4 > 0.75. Simultaneously, the expression of GANAB, PRKAA1, HMGCR, ITGAL, CCN3, INSR, AKR1A1, AKR1B1, CPT1A, KCNJ11, TRPM4, and VEGFA moderately co-localize with OC, with PPH4 > 0.6. Figure 4 B and Figure S29 display all the results with PPH4 > 0.75. Among these, both SLC47A1 and CCN3 exhibit a consistent association with Clear cell OC. GCK and ETFDH have been confirmed to have significant colocalization with LGSOC and Mucinous OC, respectively. Additionally, DDP4 is demonstrated to be significantly associated with HGSOC survival, while SIGMAR1 associated with the survival of All OC. However, no antidiabetic target genes were found co-localized to the risk of All OC and HGSOC with PPH4 > 0.6 (Fig. 4 A). Following Two-sample MR, SMR and colocalization analysis, the risk of LGSOC, Clear cell OC, Mucinous OC, Endometrioid OC, and the survival of HGSOC were found consistent associations with specific target genes in at least 2 analyses (Fig. 1 ). AKR1A1 targeted by aldose reductase inhibitors showed a significant association with an elevated risk of LGSOC and Endometrioid OC through Two-sample MR and colocalization analysis. Meanwhile, ITGAL targeted by DPP4i was significantly related to the decreased risk of mucinous OC and endometrioid OC. HMGCR (DPP4i) and KCNJ11 (sulfonylureas) were also positively associated with the risk of clear cell OC by Two-sample MR and colocalization analysis. Moreover, through both SMR and colocalization analysis, the expression of AKR1B1 targeted by Aldose reductase inhibitors and DPP4 targeted by DPP4i were consistently associated with an increased risk of mucinous OC and a reduced survival of HGSOC, respectively. Discussion This study delved into the relationship between antidiabetic drug target genes and the risk and survival of OC through three MR approaches: Two-sample MR, SMR, and colocalization analysis. We aimed to explore the associations between the target genes of antidiabetic drugs and OC risk and survival. Ultimately, we discovered that the incidence of LGSOC, Clear cell OC, Mucinous OC, Endometrioid OC (however not HGSOC), and the survival of HGSOC were all consistently associated with specific antidiabetic target genes in at least 2 MR methods. The significant links observed between specific drug targets and OC imply that metabolic dysregulation contributes to the pathogenesis of OC, suggesting that antidiabetic drugs might hold therapeutic potential as disease-modifying agents. Extensive research has demonstrated a significant connection between diabetes mellitus and the development as well as survival of OC [ 34 – 35 ]. However, these investigations did not separately validate the associations for OCs with different pathological subtypes. Therefore, it remains unclear whether there are varied associations between distinct pathological subtypes of OC and diabetes mellitus. Given the substantial variation in the pathophysiology of different pathological types of OC, we employed MR to independently analyze the GWAS data of different types of OC. All subtypes of OC were found associated with relevant antidiabetic drug target genes in separate MR analyses. However, the association between HGSOC risk and specific genes was only validated by one MR method, while others were found associated with certain genes by two methods, indicating that there may be stronger associations between other pathological types and diabetes mellitus. SMR and colocalization analysis showed that dipeptidyl peptidase 4 (DPP4) was shown strongly correlated with HGSOC survival. It is well-established that DPP4, a member of the prolyl oligopeptidase serine protease family, is overexpressed in OC tissues [ 36 ]. High DPP4 expression is associated with a reduced epithelial phenotype and invasiveness in OC cell. Conversely in another study, DPP4 expression is also linked to enhanced migration and tumorigenic capabilities of cancer cells isolated from abdominal ascites [ 37 – 38 ]. The SMR analysis in this study further demonstrated that elevated DPP4 expression was negatively correlated with OC survival. The per-SD reduction of Hb1Ac, induced by DPP4i targeting HMGCR and ITGAL respectively, was confirmed associated with the risk of specific subtypes of OC outcome through two-sample MR and colocalization analysis. (3S)-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase (HMGCR) catalyzes the transformation of HMG-CoA into mevalonic acid, a rate-limiting reaction in the production of cholesterol and isoprenoids, making it crucial for maintaining cholesterol homeostasis within cells [ 39 ]. Earlier research has demonstrated that higher HMGCR expression correlates with better prognosis in OC. Moreover, platinum-resistant OC patients exhibit lower HMGCR protein expression than their platinum-sensitive counterparts [ 40 ]. Such metabolic reprogramming could help cancer cells meet the elevated energy and cholesterol needs driven by accelerated tumor growth. In this study, we also found that DPP4i-induced inhibition of HMGCR, resulting in a decrease in Hb1Ac, was related to higher risk of clear cell OC. Integrins are heterodimeric transmembrane proteins consisting of alpha and beta subunits, essential for leukocyte trafficking and cell differentiation during inflammatory and cancerous processes [ 41 ]. Integrin Subunit Alpha L (ITGAL) is particularly important in inflammation and the immune response. DPP4i statins, being lipid-soluble drugs, are more likely to penetrate cell membranes and may target HMGCR and ITGAL, thereby exerting antitumor effects [ 42 – 43 ]. Consistently, this study demonstrated through Two-sample MR that the inhibition of ITGAL targeted by DPP4i (such as simvastatin and rosuvastatin) was linked to a reduced risk of Mucinous OC and Endometrioid OC. AKR1A1 and AKR1B1, targeted by aldose reductase inhibitors, were also consistently associated in at least two of the three MR methods. Aldo-keto reductase family member A1 (AKR1A1) mainly facilitates the conversion of aldehydes into their corresponding alcohols, utilizing NADPH as a cofactor[ 44 ]. It is essential for metabolic functions, especially in neutralizing toxic aldehydes and protecting cells from oxidative damage [ 45 ]. It is also known to metabolize anthracyclines like zorubicin and adriamycin into inactive forms, promoting resistance to chemotherapy [ 46 ]. In this study, two-sample MR and colocalization analysis revealed that AKR1A1 inhibition was positively linked to the risk of LGSOC and endometrioid OC. These findings suggest that AKR1A1 may play a role in both the development and drug resistance of OC. Although aldo-keto reductase family member B1(AKR1B1) is also involved in aldehyde metabolism, it has a distinct function in diabetic complications. In contrast to AKR1A1, it facilitates the transformation of glucose into sorbitol, which is then metabolized into fructose [ 47 ]. Fructose metabolism plays a critical role in the tumor proliferation. Previous studies have shown that the deletion of AKR1B1 inhibited the endogenous production of fructose, significantly inhibiting glycolysis and resulting in reduced migration, inhibition of growth, promotion of apoptosis, and induction of cell-cycle arrest in cancer cells [ 48 ]. The expression of AKR1B1 protein was also found higher in the tumor tissues of recurrent HGSOC patients compared to newly diagnosed patients [ 49 ]. In this study, SMR analysis and colocalization analysis indicated that increased expression of AKR1B1 was significantly linked to an higher risk of Mucinous OC. Two-sample MR analysis showed that sulfonylureas targeting KCHJ11, the ATP-sensitive inward rectifier potassium channel 11, were correlated with an elevated risk of Clear cell OC. Meanwhile, colocalization analysis also indicated colocalized between the two. Loss-of-function mutations in KCNJ11 lead to continuous and uncontrolled insulin release, as well as congenital hyperinsulinism [ 50 ]. This phenomenon may facilitate the energy uptake of tumor tissues. Consequently, sulfonylureas might promote tumor development by targeting KCNJ11 and inhibiting its expression. KCNJ11 has been previously discussed in the context of colorectal cancer, oral cavity cancer, pancreatic cancer, etc. [ 51 – 53 ]. However, to the best of our knowledge, its relationship with OC has been scarcely mentioned and requires further exploration. The present study has some limitations. Firstly, MR analyses are predicated on the assumption that IVs are exclusively associated with target genes and influence outcomes solely through these genes. Despite employing multiple sensitivity analyses to bolster the robustness of our MR findings, the possibility of pleiotropy and other biases inherent to MR methods cannot be completely ruled out. Secondly, although European populations predominate in both SMR analysis and colocalization analysis, this may also introduce certain biases. This demographic constraint necessitates cautious interpretation of our study's findings. Lastly, our investigation did not identify any drug target that exhibited consistent association with a specific OC subtype across all three analytical approaches. Also, a significant number of target genes demonstrated associations with OC in only one MR analysis method. While results from a single method may not suffice to establish causality, they may indicate potential associations that merit further investigation. Future research endeavors could seek to corroborate these findings by expanding sample sizes, utilizing more refined genetic IVs, or integrating additional genetic epidemiological analysis techniques. Conclusion Through the application of two-sample MR, SMR and colocalization analysis, we have discerned that, in terms of disease risk, AKR1A1 with LGSOC, HMGCR and KCNJ11 with clear cell OC, ITGAL and AKR1B1 with mucinous OC, AKR1A1 and ITGAL with endometrioid OC have all demonstrated consistently significant associations across at least two MR approaches. The incidence of HGSOC has been shown to correlate with certain genes in only a single MR analytical approach; however, the survival outcomes for HGSOC have been corroborated associated with DPP4 in both SMR and colocalization analysis. The strong links between specific drug targets and various OC phenotypes highlight the critical role of metabolic dysregulation in OC development. Thus, specific antidiabetic drug targets may, in the future, function as innovative therapeutic targets, opening new pathways for managing OC in light of distinct pathological subtypes. Abbreviations OC: Ovarian cancer; MR: Mendelian randomization; SMR: Summary data-based MR; HGSOC: High-grade serous OC; DM: Diabetes mellitus; IV: Instrumental variable; AGIs: Alpha-glucosidase inhibitors; TZDs: Thiazolidinediones; DPP4i: Dipeptidyl peptidase 4 inhibitors; GLP-1A: Glucagon-like peptide-1 analogues; SGLT2i: Sodium-glucose cotransporter 2 inhibitors; GWAS: Genome-wide association analysis; OCAC: Ovarian Cancer Association Consortium; LGSOC: Low grade serous OC; SNP: Single nucleotide polymorphism; EAF: Effective allele frequency; T2DM: Type 2 diabetes mellitus; IVW: Inverse variance weighted; OR: Odd ratio; SD: Standard deviation; MVMR: Multivariate MR; eQTL: Expression quantitative trait loci; HEIDI: Heterogeneity in dependent instruments; GTEx: Genotype-Tissue Expression; PPH4: Posterior probability of H4; DPP4: Dipeptidyl peptidase 4; HMGCR: (3S)-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase; ITGAL: Integrin Subunit Alpha L; AKR1A1: Aldo-keto reductase family member A1; AKR1B1: Aldo-keto reductase family member B1; KCNJ11: ATP-sensitive inward rectifier potassium channel 11. Declarations Ethics approval and consent to participate This study used previously published data and publicly available databases. Ethical approval and informed consent were obtained from the respective institutional review boards. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the CAMS Innovation Fund for Medical Sciences(CIFMS)(grant number: 2025-12M-KJ-004). Author Contribution TEY and WLY: conception and design. TEY and ZJ: data acquisition. TEY, ZJ, SXL and WYN: data analysis and manuscript writing. WLY and SYC: interpretation and editing of the manuscript. All authors have read and approved the final manuscript. Availability of data and materials Table S3 showed all the databases applied in this study. GWAS summary data for OC are available from OCAC ( http://ocac.ccge.medschl.cam.ac.uk/ ). 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Huang X, Wei X, Qiao S, Zhang X, Li R, Hu S, et al. Low Density Lipoprotein Receptor (LDLR) and 3-Hydroxy-3-Methylglutaryl Coenzyme a Reductase (HMGCR) Expression are Associated with Platinum-Resistance and Prognosis in Ovarian Carcinoma Patients. Cancer Manag Res. 2021;13:9015–9024. Campbell ID, Humphries MJ. Integrin structure, activation, and interactions. Cold Spring Harb Perspect Biol. 2011;3(3):a004994. Vogel TJ, Goodman MT, Li AJ, Jeon CY. Statin treatment is associated with survival in a nationally representative population of elderly women with epithelial ovarian cancer. Gynecol Oncol. 2017;146(2):340–345. Zhu L, Zhang H, Zhang X, Chen R, Xia L. Drug repositioning and ovarian cancer, a study based on Mendelian randomisation analysis. Front Oncol. 2024;14:1376515. O'connor T, Ireland LS, Harrison DJ, Hayes JD. 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Yang Y, Chen B, Zheng C, Zeng H, Zhou J, Chen Y, et al. Association of glucose-lowering drug target and risk of gastrointestinal cancer: a mendelian randomization study. Cell Biosci. 2024;14(1):36. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure.pdf Supplementarytable.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7173550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490496828,"identity":"98a5700e-8de8-4e6b-9ab0-7f1b0bcceeb6","order_by":0,"name":"Enyu Tang","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Enyu","middleName":"","lastName":"Tang","suffix":""},{"id":490496829,"identity":"2ac303c1-5d79-413f-8345-74a3fd3c442c","order_by":1,"name":"Jia Zeng","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Zeng","suffix":""},{"id":490496830,"identity":"4252c4ef-e0d9-4c8a-b2d5-5faceea285d5","order_by":2,"name":"Xinlong Shi","email":"","orcid":"","institution":"Friedrich-Alexander University (FAU) Erlangen-N ü rnberg and Universitätsklinikum Erlangen","correspondingAuthor":false,"prefix":"","firstName":"Xinlong","middleName":"","lastName":"Shi","suffix":""},{"id":490496832,"identity":"bfb2c722-6cd6-44c6-a185-b4ec096dcb59","order_by":3,"name":"Yani Wang","email":"","orcid":"","institution":"General Hospital of Northern Theater Command","correspondingAuthor":false,"prefix":"","firstName":"Yani","middleName":"","lastName":"Wang","suffix":""},{"id":490496834,"identity":"936a084a-a8ca-4b10-953f-894b87fc6f7d","order_by":4,"name":"Yangchun Sun","email":"","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yangchun","middleName":"","lastName":"Sun","suffix":""},{"id":490496836,"identity":"7c9336f9-fb2b-43a4-b722-f652858c4f2c","order_by":5,"name":"Lingying Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFAC5oYDQJIHyDhArBZGmBa2BOK1QBk8BsRpMLiR2Hi44NdhGXP+NR8//qixYeCXPn6B4ecOvFoaDs/sO8xjOePtZmmeY2kMkn05BYy9Z3BrMQNp4e05zGNw4+w2ZsaGwwwGZ3gSmBnbiNJy5hnjT6K18PwAajnfw8bAC9bCfgCvFvszD4G2NKQDbWEzBvmFR7KHh+FgLx4tku3Jhz/z/LG2Nzh/+CEoxOT4edgfPviJRwsYMLY1MzBIJIDZPKAIOkBAAxD8qWNg4IerY39AWMcoGAWjYBSMJAAATGlXU9TvTDUAAAAASUVORK5CYII=","orcid":"","institution":"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Lingying","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-07-21 05:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7173550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7173550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87569653,"identity":"c5263e82-9a7f-4fcf-9509-17a6b3b4f9eb","added_by":"auto","created_at":"2025-07-25 10:16:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210488,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram outlining a structured framework for investigating the relationship between antidiabetic targets and ovarian cancer. It presents a MR analysis framework utilized to evaluate the causal effects of target genes from nine antidiabetic drugs, obtained from DrugBank, on diverse ovarian cancer phenotypes identified through extensive genome-wide association study of ovarian cancer. The three central sections of the flowchart detail the specific mechanisms and sensitivity analyses associated with three distinct MR analysis methods. The bottom section highlights the target genes and ovarian cancer phenotypes that demonstrate consistent significance across at least two of the three MR analyses, underscoring robust associations.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/c1a27a5b8323118464701ca3.png"},{"id":87569654,"identity":"76c90a31-b244-4616-9a64-7d0739b3a0da","added_by":"auto","created_at":"2025-07-25 10:16:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":811487,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots depicting the impact of antidiabetic targets on nine ovarian cancer phenotypes. All analyses were conducted employing the multiplicative random-effects IVW MR approach. The effect estimates, expressed as odds ratios, were calibrated to represent a per-SD reduction in genetically predicted HbA1c levels, reflecting the impact of targeting specific genes with corresponding drugs on ovarian cancer risk and survival. Each plot is annotated with the gene and its related drug class. Figure 2A illustrates the results for individual drug targets, while Figure 2B summarizes the findings for grouped drug targets within a specific drug category. Additional results for individual and combined targets can be found in Figure S2.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/7fc36624221a0221533df1ec.png"},{"id":87570365,"identity":"1331b75d-1718-4484-8d56-583441aefa57","added_by":"auto","created_at":"2025-07-25 10:24:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504168,"visible":true,"origin":"","legend":"\u003cp\u003eSMR analysis of antidiabetic targets in ovarian cancer. Figure 3A presents the relationship between the expression of gene expression in blood (sourced from eQTLGen) and nine ovarian cancer phenotypes. Red regions indicate positive correlations, while green regions denote negative correlations. Statistically significant results are presented with asterisks and odds ratios. * indicates significance level (*P \u0026lt; 0.05, **P \u0026lt; 0.05/32, adjusted for multiple testing). Figures 3B and 3C demonstrate the associations between ovarian cancer phenotypes and the gene expression in the brain (PsychENCODE) and ovary (GTEx-ovary), respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/d57aa5c781c2fb634116d4b8.png"},{"id":87569662,"identity":"8ede94b5-291f-4c2e-9092-29d131801fcc","added_by":"auto","created_at":"2025-07-25 10:16:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":468507,"visible":true,"origin":"","legend":"\u003cp\u003eColocalization analysis of antidiabetic gene expression and ovarian cancer. Figure 4A presents the results of colocalization analysis between putative drug targets and nine ovarian cancer phenotypes. Dark blue shading and asterisks denote robust support for colocalization. **denotes PPH4 \u0026gt; 0.75, while *represents 0.6 \u0026lt; PPH4 \u0026lt; 0.75 in the colocalization analysis. Figure 4B and Figure S29 display all results with PPH4 \u0026gt; 0.75, providing further details on significant colocalization findings.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/22b13383db562885c01d7acc.png"},{"id":89121306,"identity":"9169cc1b-16d9-4ab3-9a42-eab3e1828a97","added_by":"auto","created_at":"2025-08-15 02:16:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2612849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/3df6d412-4917-41c3-9f8c-f87c4357d2c7.pdf"},{"id":87571242,"identity":"3c225357-6b89-48d1-bc8c-cd066e5f7f32","added_by":"auto","created_at":"2025-07-25 10:40:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6467451,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/66367015514db16b3758dd40.pdf"},{"id":87570364,"identity":"c7a419b3-fb06-44d0-b92b-53339f57a46c","added_by":"auto","created_at":"2025-07-25 10:24:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":316647,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7173550/v1/d1a2ea939038335b7dae3da7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Subtype-Specific Causal Effects of Antidiabetic Drug Targets on Ovarian Cancer: Mendelian Randomization and Colocalization Evidence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) continues to be the deadliest gynecological cancer, with significant global health implications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2020 alone, more than 300 thousand new cases of ovarian, fallopian tube, and primary peritoneal cancers were reported worldwide, resulting in 207,252 deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Using the conventional first-line approach of surgery followed by platinum-based chemotherapy, nearly 70% of patients eventually experience recurrence with platinum resistance. Although the introduction of targeted drugs as maintenance therapy, including bevacizumab and poly (ADP-ribose) polymerase inhibitors, has improved overall survival [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], not all patients benefit from existing targeted drugs. This underscores the critical need to identify novel therapeutic targets to expand the treatment options for patients with OC. Metabolic reprogramming is essential for the initiation and progression of tumors, and numerous metabolites have been explored as potential therapeutic targets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOC is closely linked to endocrine dysfunction, with prior research indicating that endocrine and metabolic factors significantly contribute to its pathogenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given this connection, it has been hypothesized that diabetes mellitus (DM) can elevate the risk of OC by disrupting endocrine system homeostasis. However, epidemiological studies exploring the relationship between DM and OC have yielded conflicting findings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several systematic review synthesizing data from multiple studies have provided more robust evidence, confirming a link between diabetes and an increased risk of OC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings suggest that antidiabetic medications hold promise as potential interventions or adjuvant therapies for OC. Nevertheless, conflicting conclusions from studies on the impact of metformin, a widely used antidiabetic drug, on OC prognosis highlight the complexity and uncertainty of this field [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This emphasizes the need for innovative research perspectives and methodologies to elucidate the mechanisms linking diabetes and its therapeutic agents to OC development. Additionally, significant heterogeneity among OC subtypes must be carefully considered in such investigations.\u003c/p\u003e\u003cp\u003eAs a robust approach for causal inference, Mendelian randomization (MR) employs genetic variants as instrumental variables (IVs) to assess the causal impact of exposures on outcomes, effectively addressing the issues of confounding bias. Mendelian randomization (MR) has emerged as a powerful tool for inferring causal relationships between exposures and outcomes. By leveraging genetic variants, such as IVs, MR can assess the causal effect of an exposure on an outcome while minimizing confounding bias. IVs are presumed to influence the outcome exclusively via the exposure, without being affected by other confounding factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, MR mimics the rigor of randomized controlled trials [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and is particularly advantageous when exposures are difficult or expensive to measure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, MR has gained widespread recognition in the fields of medical genetics and epidemiological research.\u003c/p\u003e\u003cp\u003eIn this study, we employed an integrative approach combining three MR methods involving two-sample MR, SMR, and colocalization analysis to systematically examine the relationship between antidiabetic target genes and OC. Our aim was to further clarify the potential link between diabetes mellitus and OC, while evaluating the therapeutic potential of antidiabetic targets in shaping the development and prognosis of OC across different pathological subtypes. This comprehensive approach provides a robust framework for the identification of novel therapeutic targets for OC treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eIdentification of Antidiabetic Drug Target Genes and Determination of Study Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe methodological structure of this study is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The DrugBank pharmacogenetics database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to identify genes associated with nine antidiabetic drugs: 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 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e shows a complete overview of all drug target genes.\u003c/p\u003e\u003cp\u003eThis study utilized outcome data from a genome-wide association analysis (GWAS) performed by the Ovarian Cancer Association Consortium (OCAC) involving 66,450 European participants, including 25,509 ovarian epithelial cancer patients and 40,941 healthy controls (Table S3). This analysis not only included all ovarian epithelial cancers but also specifically examined different pathological subtypes of OC, such as 13307 High grade serous OC (HGSOC) cases, 1012 Low grade serous OC (LGSOC) cases, 1366 Clear cell OC cases, 2810 Endometrioid OC cases, and 2,566 Mucinous OC cases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, 11,311 patients with clinical prognostic information were utilized for GWAS analysis of overall survival, either for HGSOC cases only, All OC cases or All OC with adjustment for pathological type respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo-sample MR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo-sample MR studies were selected to identify the impact of Hb1Ac alteration by targeting specific genes using antidiabetic drugs on the risk and prognosis of OC. In MR analyses, genetic variation can serve as an IV if it satisfies the following fundamental conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]: (i) the genetic variation correlates with the exposure; (ii) the variation does not affect the outcome through confounding factors; and (iii) the variation has no direct effect on the outcome but influences it indirectly solely by affecting the exposure (as depicted in the directed acyclic graph in the middle-left of the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To meet the requirements, the following is the detailed analysis process.\u003c/p\u003e\u003cp\u003eInitially, single nucleotide polymorphisms (SNPs) within the cis-region (spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb) of the target genes were retrieved from the prior GWAS of HbA1c in the UK Biobank cohort. These SNPs were then analyzed using a clumping window of 100 kb, with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, effective allele frequency (EAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.01, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2 and F\u0026thinsp;\u0026gt;\u0026thinsp;10 to eliminate weak IVs. Target genes with at least one valid SNP were screened through TwoSampleMR package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mrcieu.github.io/TwoSampleMR/\u003c/span\u003e\u003cspan address=\"https://mrcieu.github.io/TwoSampleMR/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After removing SNPs in palindromic and compatible alleles, the remaining SNPs were used as IVs for the corresponding target genes (Table S4) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The same method was applied during the replication analysis for Two-sample MR quality control. When two neighboring genes shared IVs due to overlapping cis-regions, they were presented together and separated by a slash, such as \u0026lsquo;VEGFA/SLC29A1\u0026rsquo;. Subsequently, positive control MR analyses were carried out to further exclude target genes that had no significant association with either blood glucose (met-d-Glucose, ebi-a -GCST90014005) or type 2 diabetes mellitus (T2DM) (ebi-a-GCST006867, finn-b-E4_DM2_STRICT) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (Tables S3 and S5)\u003c/p\u003e\u003cp\u003eFor target genes with at least two effective IVs, the multiplicative random effects inverse variance weighted (IVW) was employed as principal analysis, while Wald ratio MR was used for those with only one IV[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To corroborate the results, we performed multiple sensitivity assessments utilizing various methodologies, specifically the IVW with fixed effects, along with weighted median and mode estimation techniques [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The final risk ratios (OR) and p-value obtained from these four approaches were compared to verify the robustness and reliability of the results. Two-stage methods, such as the IVW method, are the most effective analytical methods when IV assumptions are met [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and thus should typically be used as the primary analytical approach. The weighted median estimator maintains robustness despite the inclusion of potentially invalid instrumental variables [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Statistical significance of the weighted mode estimator requires that most comparable causal effects originate from valid instrumental variables [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The heterogeneity of IVs was verified using the I\u003csup\u003e2\u003c/sup\u003e statistics and Q test. Horizontal pleiotropy was evaluated through MR-Egger regression's intercept examination. MR analyses quantified OC-related effects of per-SD decreases in genetically predicted HbA1c through specific drug targets [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], with additional scaling to per-SD reduction of random glucose (ebi-a-GCST90014005). Positive control analyses provided conversion coefficients (α) between these measures for each target. The effect of a per-SD reduction in random blood glucose induced by antidiabetic drug targeting of specific genes on OC (scaled β) was then calculated by multiplying β by 1/α. We established dual significance thresholds, with P values below 0.05 indicating nominal significance and values under 3\u0026times;10⁻⁴ representing significance after multiple testing correction (Bonferroni correction: 0.05/18\u0026times;9 to account for multiple comparisons across all drug target-phenotype combinations) to further rule out the possibility of false-positive results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. If multiple target genes of an antidiabetic drug all had valid IVs, these genes were included in a combined MR analysis to validate the overall drug effect.\u003c/p\u003e\u003cp\u003eComprehensive sensitivity testing and quality control procedures were implemented to confirm the stability of the findings. We implemented Multivariate MR (MVMR) to quantify the causal influence of individual exposures on outcomes by leveraging genetic instruments linked to several potentially confounding exposures. This approach helps avoid bias due to confounding factors, demonstrating that genetic variation does not influence outcomes through confounders [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, we applied MVMR analysis (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) to calculate the effect of Hb1Ac reduction through targeting specific genes by antidiabetic drugs on different OC phenotypes after adjusting for four confounders: body mass index, systolic blood pressure, smoking status, and alcohol-drinking status. Subsequent replication analysis also examined the association between HbA1c (Within family GWAS consortium, MAGIC), T2DM (DIAGRAM, FinnGen, S\u0026iacute;lvia Bon\u0026agrave;s-Guarch), and fasting glucose (MAGIC). SNPs within the cis-region (spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb) of the target genes were obtained from the previous GWAS, and weak IVs were removed following the same screening criteria (Table S3). The causal links between drug targets and OC outcomes were further verified through replicated MR methods. Moreover, the presence of pleiotropic SNPs was investigated through MR-PRESSO outlier detection analysis, serving as a complementary sensitivity evaluation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSMR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSMR is a summary data-based MR analysis method employed in expression quantitative trait loci (eQTL) research and GWAS studies. It serves to determine whether the impact magnitude of SNPs on the outcome is influenced by gene expression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Through SMR analysis, we investigated the causal links between antidiabetic drug target gene expression and OC subtype-specific outcomes, including disease risk and survival, following the analytical approach represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e's central directed acyclic graph. EQTL refers to a class of genetic loci, mostly SNPs, which can significantly influence the level of gene expression. The eQTLGen consortium encompasses gene expression data from 31,684 patients' blood samples and offers genetic variants linked to the expression of those genes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This analysis focused on cis-eQTLs located within a 1-Mb range of the respective gene locations were used as IVs to ensure the correlation between SNPs and target genes. We then utilized heterogeneity in dependent instruments (HEIDI) test to identify linkage disequilibrium in pleiotropic relation and, thereby, to detect the presence of heterogeneity in SMR analyses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo enhance the comprehensiveness of our gene expression investigation, we integrated eQTL data from the most recently released Genotype-Tissue Expression (GTEx) database. Specifically, data from PsychENCODE (n\u0026thinsp;=\u0026thinsp;1387) and GTEx-ovary (n\u0026thinsp;=\u0026thinsp;167), derived from brain and ovarian tissues respectively, were used for replication analyses. All of the above-mentioned analysis procedures adopted the preset parameters [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/#SMR\u0026amp;HEIDIanalysis\u003c/span\u003e\u003cspan address=\"https://yanglab.westlake.edu.cn/software/smr/#SMR\u0026amp;HEIDIanalysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). P\u003csub\u003eSMR\u003c/sub\u003e \u0026lt; 0.05 was regarded as statistically significant for association, followed by the application of a multiple-test correction threshold (Bonferroni correction: 0.05 divided by total gene count used for SMR analysis) to further eliminate the possibility of false-positive results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColocalization analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eColocalization analysis is an integrative approach aimed at identifying genetic variants that may induce concurrent phenotypic changes in multiple molecules or other complex features [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Even if a genetic variant in a specific gene region shows associations with both the exposure and the outcome, this does not confirm that the same variant influences the two. These associations could arise from distinct causal variants correlated through linkage disequilibrium [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Colocalization analysis is especially valuable for assessing exposures, such as protein levels and gene expression, especially in MR analyses based on individual gene regions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, colocalization analysis can offer further support in elucidating the biological mechanism underlying the causal relationship between antidiabetic drug target genes and OC outcomes. This analysis employed a Bayesian model, estimating posterior probabilities for five potential scenarios: 1) H0: No connection to either trait; 2) Associated with trait A exclusively; 3) Associated with trait B exclusively; 4) Separate SNPs linked to trait A and trait B independently; 5) A common SNP associated with both trait A and trait B (middle-right of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Analyses were carried out utilizing the R package (coloc.abf algorithm, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.rproject.org/web/packages/coloc\u003c/span\u003e\u003cspan address=\"http://cran.rproject.org/web/packages/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters of p1\u0026thinsp;=\u0026thinsp;1 \u0026times; 10⁻⁴, p2\u0026thinsp;=\u0026thinsp;1 \u0026times; 10⁻⁴, and p12\u0026thinsp;=\u0026thinsp;1 \u0026times; 10⁻\u0026sup3;. A posterior probability of H4 (PPH4) over 0.75 was considered indicative of a robust colocalization relationship and a PPH4 over 0.6 as evidence of a moderate colocalization association.\u003c/p\u003e\u003cp\u003eFinally, we summarized the performance of all genes in the above three MR analyses and listed the genes that were significantly associated with OC in at least two of them (shown at the bottom of the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The SMR analyses in this study were carried out using the smr-1.3.1-win software on a Windows system, while all other analyses were executed using R software (version 4.4.1) with packages including TwoSampleMR, coloc.abf, and others. This study relies on previously published data and publicly available databases. Ethical approval and informed consent were secured from the respective institutional review boards.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTwo-Sample MR: Screening Effective Drug Target\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAntidiabetic drugs were categorized into nine classes based on the DrugBank pharmacogenetics database, and their respective target genes are listed in Tables S1 and S2. A total of 21 target genes were identified as potentially associated with OC outcomes, including genes targeted by sulfonylureas (ABCC8/KCNJ11, ABCB11/LRP2, CPT1A, PPARG, INS, KCNJ1, V-EGFA/SLC29A1), TZDs (PPARG, VEGFA/SLC29A1, ESRRA, RXRB), insulin (ABCB11/LRP2), biguanides (SLC47A1), AGIs (GANC), DPP-4i (HMGCR, ITGAL), GLP-1RA (GLP1R), SGLT2i (SLC5A2, SLC5A1), and other drugs (RAMP1, RAMP2, RAMP3, GCK, AKR1A1) (Table S5). The corresponding SNPs for each target gene and their effects on HbA1c levels are detailed in Table S4. Among these, 18 target genes were selected for subsequent two-sample MR analyses, excluding KCNJ1, ESRRA, and RAMP1, which showed no significant association with blood glucose or T2DM in positive control analyses (Table S5).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo-Sample MR: Investigating Antidiabetic Drug Targets on OC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing a comprehensive analytical approach, which included principal analysis utilizing IVW (multiplicative random effects) or Ward Ratio, sensitivity analyses employing IVW(fixed-effect), weighted median, and weighted mode methods, as well as rigorous quality control screenings (F statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 and EAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01; Q test for heterogeneity\u0026thinsp;\u0026lt;\u0026thinsp;50%; MR-Egger intercept P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Bonferroni-corrected P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), we identified 16 significant targets linked to at least one OC outcome except for GANC and ABCB11/LRP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-21, Table S6). Notably, VEGFA/SLC29A1 and PPARG for sulfonylureas/TZDs, CPT1A and ABCC8/KCNJ11 for sulfonylureas, RXRB for TZDs, SLC5A2 and SLC5A1 for SGLT2i, GLP1R for GLP-1RA, HMGCR and ITGAL for DPP4i, AKR1A1, RAMP2, RAMP3, and GCK for other drugs demonstrated significant associations with at least one OC phenotype and passed the significance threshold after multiple testing correction (P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe impact of representative drug targets, as determined through principal component analysis, on various OC risk and survival outcomes is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The per-SD reduction in HbA1c through targeting RXRB by TZDs was related to a decreased risk of All OC (OR: 0.528, 95% CI: 0.413\u0026thinsp;~\u0026thinsp;0.675, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and HGSOC (OR: 0.378, 95% CI: 0.26\u0026thinsp;~\u0026thinsp;0.549, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), indicating a protective effect. Conversely, targeting VEGFA/SLC29A1 with sulfonylureas/TZDs, resulting in a per-SD decrease in HbA1c, was linked to an elevated risk of HGSOC (OR: 2.128, 95% CI: 1.415\u0026thinsp;~\u0026thinsp;3.199, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and worse survival for All OC (OR: 0.288, 95% CI: 0.138\u0026thinsp;~\u0026thinsp;0.6, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and HGSOC (OR: 0.197, 95% CI: 0.122\u0026thinsp;~\u0026thinsp;0.317, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Sulfonylureas/TZDs targeting PPARG were also significantly associated with reduced survival in All OC and an elevated risk of endometrioid (OR: 2.917, 95% CI: 1.596\u0026thinsp;~\u0026thinsp;5.333, P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). The effects of sulfonylureas targeting CPT1A on OC risk varied by pathotype, showing a decreased incidence of LGSOC but an increased incidence of endometrioid OC, alongside a negative correlation with survival in HGSOC (all P\u0026thinsp;\u0026lt;\u0026thinsp;3 \u0026times; 3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). GLP-1RA targeting GLP-1R was associated with improved survival in HGSOC. Additionally, other drugs targeting RAMP2 were linked to an elevated risk of All OC, Clear cell OC, Mucinous OC, and Endometrioid OC, but a reduced risk of LGSOC (all P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Conversely, targeting AKR1A1 by other drugs was correlated with a higher risk of All OC, HGSOC, LGSOC, and Endometrioid OC, and an increased risk of Mucinous OC, alongside decreased survival in HGSOC and All OC (all P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). DPP4i targeting HMGCR was significantly linked to a higher risk of clear cell OC and reduced survival of All OC patients(all P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eSulfonylureas, TZDs, SGLT2i, and DPP4i, which act on multiple target genes, were also found to have significant associations with at least one of the OC phenotypes, although only the associations observed for DPP4i with OC phenotypes passed the multiple correction threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table S6). Pooled targets of sulfonylureas showed a significant association with a higher risk of endometrioid OC, an elevated risk of LGSOC, as well as reduced survival in HGSOC and All OC subtypes (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TZDs targeting SLC29A1, RXRB, and PPARG, had a protective effect on the HGSOC and All OC subtypes, however the effect was reversed for endometrioid OC (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, co-targeting SLC5A1 and SLC5A2 by SGLT2i was linked to an elevated risk of All OC and HGSOC (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, the pooled targets of DPP4i were linked to a reduced risk of mucinous OC and endometrioid OC (all P\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026times;10⁻⁴).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTwo-Sample MR: Sensitivity Analysis and Quality Control\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the MVMR analysis (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure S26), the targets of antidiabetic drugs were further validated to be significantly associated with specific OC subtypes, which was consistent with the principal analysis. Among these, RAMP2 and RXRB were related to both the risk and survival of All OC subtypes. Meanwhile, GCK was only correlated with the risk of All OC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RXRB and GCK were linked to both the risk and survival of HGSOC. Additionally, VEGFA/SLC29A1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GLP1R (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and AKR1A1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were solely associated with HGSOC survival. Moreover, RAMP2 and GCK were significantly associated with the risk of mucinous OC (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of clear cell OC was found only associated with RAMP2 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ITGAL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AKR1A1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and PPARG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were all associated with the risk of endometrioid OC.\u003c/p\u003e\u003cp\u003eIn terms of the replication analyses (Table S7, Figure S27-28), the associations of GCK with the survival and risk of HGSOC, GCK with the risk of All OC, LGSOC, and mucinous OC, PPARG with the risk of mucinous OC and endometrioid OC, and the survival of HGSOC and All OC in the MR results were replicated at least once. Table S6 provided the MR findings adjusted for random blood glucose levels. The results of the principal analyses were largely consistent with those obtained from the sensitivity analyses using the other three MR methods (Table S6, Figure S3-25). After the MR-PRESSO analysis (Table S8), the associations of the risk of HGSOC with SGLT2i targeting SLC5A2, as well as TZDs targeting the combined targets of SLC29A1, RXRB, and PPARG, were identified as having potential pleiotropy, although significant in the MR principal analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSMR: Antidiabetic Drug Target Gene Expression and OC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe eQTLGen (blood) database was utilized for the primary analysis of the SMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The PsychENCODE (brain) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and GTEx (ovary) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) databases were employed for replication analyses. First, in the primary analysis, a per-SD increase of ETFDH expression in the blood was associated not only with the risk of All OC (OR: 0.77, 95% CI: 0.63\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.013) and HGSOC (OR: 0.74, 95% CI: 0.58\u0026ndash;0.94, P\u0026thinsp;=\u0026thinsp;0.013), but also with the prognosis of HGSOC (OR: 0.71, 95% CI: 0.52\u0026ndash;0.97, P\u0026thinsp;=\u0026thinsp;0.032). This finding implies that there may be a more intricate relationship between ETFDH expression and the progression of OC. Moreover, the expression of DDP4 was negatively correlated with the survival of HGSOC (OR: 0.59, 95% CI: 0.38\u0026ndash;0.90, P\u0026thinsp;=\u0026thinsp;0.015), and the expression of HMGCR was negatively linked to the risk of HGSOC (OR: 0.73, 95% CI: 0.57\u0026ndash;0.94, P\u0026thinsp;=\u0026thinsp;0.016) and All OC (OR: 0.73, 95% CI: 0.57\u0026ndash;0.94, P\u0026thinsp;=\u0026thinsp;0.016). The expression of SLC47A1 was associated with the higher risk of Endometrioid OC (OR: 1.38, 95% CI: 1.01\u0026ndash;1.87, P\u0026thinsp;=\u0026thinsp;0.042), and the expression of ABCA1 was associated with an increased risk of LGSOC (OR: 4.64, 95% CI: 1.76\u0026ndash;12.24, P\u0026thinsp;=\u0026thinsp;0.002). Conversely, HMGCR was negatively linked to the risk of Mucinous OC (OR: 0.36, 95% CI: 0.20\u0026ndash;0.66, P\u0026thinsp;=\u0026thinsp;0.0008), and SLC29A1 was negatively related with the risk of Endometrioid OC (OR: 0.39, 95% CI: 0.19\u0026ndash;0.82, P\u0026thinsp;=\u0026thinsp;0.013). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table S9)\u003c/p\u003e\u003cp\u003eIn the replication analyses, AK1B1, CFTR, KCNJ11, and GAA were significantly linked to at least one OC subtype, yet there was no overlap with the principal SMR results. A per-SD increase in AKR1B1 expression in the brain was associated with a significantly increased risk of Mucinous OC (OR: 1.5, 95% CI: 1.14\u0026ndash;1.99, P\u0026thinsp;=\u0026thinsp;0.004). CFTR was negatively associated with the risk of All OC (OR: 0.93, 95% CI: 0.87\u0026ndash;1.00, P\u0026thinsp;=\u0026thinsp;0.043). KCNJ11 was positively associated with the occurrence of All OC (OR: 1.13, 95% CI: 1.0\u0026ndash;1.28, P\u0026thinsp;=\u0026thinsp;0.047) and HGSOC (OR: 1.17, 95% CI: 1.01\u0026ndash;1.35, P\u0026thinsp;=\u0026thinsp;0.039). Additionally, a per-SD increase in GAA expression in the ovary was significantly associated with reduced HGSOC survival (OR: 0.92, 95% CI: 0.85\u0026ndash;1.00, P\u0026thinsp;=\u0026thinsp;0.047). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C, Table S9)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eColocalization Analysis: colocalization of the target gene expression with ovarian cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eColocalization analysis was applied to ascertain whether the connection between the expression of specific genes and OC could be ascribed to the same causal genetic variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure S29, TableS10). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA shows that GCK, SLC47A1, CCN3, ETFDH, DPP4, and SIGMAR1 all co-localize with at least one of the OC outcomes, with a PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.75. Simultaneously, the expression of GANAB, PRKAA1, HMGCR, ITGAL, CCN3, INSR, AKR1A1, AKR1B1, CPT1A, KCNJ11, TRPM4, and VEGFA moderately co-localize with OC, with PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.6. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Figure S29 display all the results with PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.75. Among these, both SLC47A1 and CCN3 exhibit a consistent association with Clear cell OC. GCK and ETFDH have been confirmed to have significant colocalization with LGSOC and Mucinous OC, respectively. Additionally, DDP4 is demonstrated to be significantly associated with HGSOC survival, while SIGMAR1 associated with the survival of All OC. However, no antidiabetic target genes were found co-localized to the risk of All OC and HGSOC with PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eFollowing Two-sample MR, SMR and colocalization analysis, the risk of LGSOC, Clear cell OC, Mucinous OC, Endometrioid OC, and the survival of HGSOC were found consistent associations with specific target genes in at least 2 analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). AKR1A1 targeted by aldose reductase inhibitors showed a significant association with an elevated risk of LGSOC and Endometrioid OC through Two-sample MR and colocalization analysis. Meanwhile, ITGAL targeted by DPP4i was significantly related to the decreased risk of mucinous OC and endometrioid OC. HMGCR (DPP4i) and KCNJ11 (sulfonylureas) were also positively associated with the risk of clear cell OC by Two-sample MR and colocalization analysis. Moreover, through both SMR and colocalization analysis, the expression of AKR1B1 targeted by Aldose reductase inhibitors and DPP4 targeted by DPP4i were consistently associated with an increased risk of mucinous OC and a reduced survival of HGSOC, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study delved into the relationship between antidiabetic drug target genes and the risk and survival of OC through three MR approaches: Two-sample MR, SMR, and colocalization analysis. We aimed to explore the associations between the target genes of antidiabetic drugs and OC risk and survival. Ultimately, we discovered that the incidence of LGSOC, Clear cell OC, Mucinous OC, Endometrioid OC (however not HGSOC), and the survival of HGSOC were all consistently associated with specific antidiabetic target genes in at least 2 MR methods. The significant links observed between specific drug targets and OC imply that metabolic dysregulation contributes to the pathogenesis of OC, suggesting that antidiabetic drugs might hold therapeutic potential as disease-modifying agents.\u003c/p\u003e\u003cp\u003eExtensive research has demonstrated a significant connection between diabetes mellitus and the development as well as survival of OC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, these investigations did not separately validate the associations for OCs with different pathological subtypes. Therefore, it remains unclear whether there are varied associations between distinct pathological subtypes of OC and diabetes mellitus. Given the substantial variation in the pathophysiology of different pathological types of OC, we employed MR to independently analyze the GWAS data of different types of OC. All subtypes of OC were found associated with relevant antidiabetic drug target genes in separate MR analyses. However, the association between HGSOC risk and specific genes was only validated by one MR method, while others were found associated with certain genes by two methods, indicating that there may be stronger associations between other pathological types and diabetes mellitus. SMR and colocalization analysis showed that dipeptidyl peptidase 4 (DPP4) was shown strongly correlated with HGSOC survival. It is well-established that DPP4, a member of the prolyl oligopeptidase serine protease family, is overexpressed in OC tissues [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. High DPP4 expression is associated with a reduced epithelial phenotype and invasiveness in OC cell. Conversely in another study, DPP4 expression is also linked to enhanced migration and tumorigenic capabilities of cancer cells isolated from abdominal ascites [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The SMR analysis in this study further demonstrated that elevated DPP4 expression was negatively correlated with OC survival.\u003c/p\u003e\u003cp\u003eThe per-SD reduction of Hb1Ac, induced by DPP4i targeting HMGCR and ITGAL respectively, was confirmed associated with the risk of specific subtypes of OC outcome through two-sample MR and colocalization analysis. (3S)-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase (HMGCR) catalyzes the transformation of HMG-CoA into mevalonic acid, a rate-limiting reaction in the production of cholesterol and isoprenoids, making it crucial for maintaining cholesterol homeostasis within cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Earlier research has demonstrated that higher HMGCR expression correlates with better prognosis in OC. Moreover, platinum-resistant OC patients exhibit lower HMGCR protein expression than their platinum-sensitive counterparts [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Such metabolic reprogramming could help cancer cells meet the elevated energy and cholesterol needs driven by accelerated tumor growth. In this study, we also found that DPP4i-induced inhibition of HMGCR, resulting in a decrease in Hb1Ac, was related to higher risk of clear cell OC. Integrins are heterodimeric transmembrane proteins consisting of alpha and beta subunits, essential for leukocyte trafficking and cell differentiation during inflammatory and cancerous processes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Integrin Subunit Alpha L (ITGAL) is particularly important in inflammation and the immune response. DPP4i statins, being lipid-soluble drugs, are more likely to penetrate cell membranes and may target HMGCR and ITGAL, thereby exerting antitumor effects [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Consistently, this study demonstrated through Two-sample MR that the inhibition of ITGAL targeted by DPP4i (such as simvastatin and rosuvastatin) was linked to a reduced risk of Mucinous OC and Endometrioid OC.\u003c/p\u003e\u003cp\u003eAKR1A1 and AKR1B1, targeted by aldose reductase inhibitors, were also consistently associated in at least two of the three MR methods. Aldo-keto reductase family member A1 (AKR1A1) mainly facilitates the conversion of aldehydes into their corresponding alcohols, utilizing NADPH as a cofactor[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It is essential for metabolic functions, especially in neutralizing toxic aldehydes and protecting cells from oxidative damage [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. It is also known to metabolize anthracyclines like zorubicin and adriamycin into inactive forms, promoting resistance to chemotherapy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, two-sample MR and colocalization analysis revealed that AKR1A1 inhibition was positively linked to the risk of LGSOC and endometrioid OC. These findings suggest that AKR1A1 may play a role in both the development and drug resistance of OC. Although aldo-keto reductase family member B1(AKR1B1) is also involved in aldehyde metabolism, it has a distinct function in diabetic complications. In contrast to AKR1A1, it facilitates the transformation of glucose into sorbitol, which is then metabolized into fructose [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Fructose metabolism plays a critical role in the tumor proliferation. Previous studies have shown that the deletion of AKR1B1 inhibited the endogenous production of fructose, significantly inhibiting glycolysis and resulting in reduced migration, inhibition of growth, promotion of apoptosis, and induction of cell-cycle arrest in cancer cells [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The expression of AKR1B1 protein was also found higher in the tumor tissues of recurrent HGSOC patients compared to newly diagnosed patients [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In this study, SMR analysis and colocalization analysis indicated that increased expression of AKR1B1 was significantly linked to an higher risk of Mucinous OC.\u003c/p\u003e\u003cp\u003eTwo-sample MR analysis showed that sulfonylureas targeting KCHJ11, the ATP-sensitive inward rectifier potassium channel 11, were correlated with an elevated risk of Clear cell OC. Meanwhile, colocalization analysis also indicated colocalized between the two. Loss-of-function mutations in KCNJ11 lead to continuous and uncontrolled insulin release, as well as congenital hyperinsulinism [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This phenomenon may facilitate the energy uptake of tumor tissues. Consequently, sulfonylureas might promote tumor development by targeting KCNJ11 and inhibiting its expression. KCNJ11 has been previously discussed in the context of colorectal cancer, oral cavity cancer, pancreatic cancer, etc. [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, to the best of our knowledge, its relationship with OC has been scarcely mentioned and requires further exploration.\u003c/p\u003e\u003cp\u003eThe present study has some limitations. Firstly, MR analyses are predicated on the assumption that IVs are exclusively associated with target genes and influence outcomes solely through these genes. Despite employing multiple sensitivity analyses to bolster the robustness of our MR findings, the possibility of pleiotropy and other biases inherent to MR methods cannot be completely ruled out. Secondly, although European populations predominate in both SMR analysis and colocalization analysis, this may also introduce certain biases. This demographic constraint necessitates cautious interpretation of our study's findings. Lastly, our investigation did not identify any drug target that exhibited consistent association with a specific OC subtype across all three analytical approaches. Also, a significant number of target genes demonstrated associations with OC in only one MR analysis method. While results from a single method may not suffice to establish causality, they may indicate potential associations that merit further investigation. Future research endeavors could seek to corroborate these findings by expanding sample sizes, utilizing more refined genetic IVs, or integrating additional genetic epidemiological analysis techniques.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThrough the application of two-sample MR, SMR and colocalization analysis, we have discerned that, in terms of disease risk, AKR1A1 with LGSOC, HMGCR and KCNJ11 with clear cell OC, ITGAL and AKR1B1 with mucinous OC, AKR1A1 and ITGAL with endometrioid OC have all demonstrated consistently significant associations across at least two MR approaches. The incidence of HGSOC has been shown to correlate with certain genes in only a single MR analytical approach; however, the survival outcomes for HGSOC have been corroborated associated with DPP4 in both SMR and colocalization analysis. The strong links between specific drug targets and various OC phenotypes highlight the critical role of metabolic dysregulation in OC development. Thus, specific antidiabetic drug targets may, in the future, function as innovative therapeutic targets, opening new pathways for managing OC in light of distinct pathological subtypes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOC: Ovarian cancer; MR: Mendelian randomization; SMR: Summary data-based MR; HGSOC: High-grade serous OC; DM: Diabetes mellitus; IV: Instrumental variable; AGIs: Alpha-glucosidase inhibitors; TZDs: Thiazolidinediones; DPP4i: Dipeptidyl peptidase 4 inhibitors; GLP-1A: Glucagon-like peptide-1 analogues; SGLT2i: Sodium-glucose cotransporter 2 inhibitors; GWAS: Genome-wide association analysis; OCAC: Ovarian Cancer Association Consortium; LGSOC: Low grade serous OC; SNP: Single nucleotide polymorphism; EAF: Effective allele frequency; T2DM: Type 2 diabetes mellitus; IVW: Inverse variance weighted; OR: Odd ratio; SD: Standard deviation; MVMR: Multivariate MR; eQTL: Expression quantitative trait loci; HEIDI: Heterogeneity in dependent instruments; GTEx: Genotype-Tissue Expression; PPH4: Posterior probability of H4; DPP4: Dipeptidyl peptidase 4; HMGCR: (3S)-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase; ITGAL: Integrin Subunit Alpha L; AKR1A1: Aldo-keto reductase family member A1; AKR1B1: Aldo-keto reductase family member B1; KCNJ11: ATP-sensitive inward rectifier potassium channel 11.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study used previously published data and publicly available databases. Ethical approval and informed consent were obtained from the respective institutional review boards.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the CAMS Innovation Fund for Medical Sciences(CIFMS)(grant number: 2025-12M-KJ-004).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eTEY and WLY: conception and design. TEY and ZJ: data acquisition. TEY, ZJ, SXL and WYN: data analysis and manuscript writing. WLY and SYC: interpretation and editing of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eTable S3 showed all the databases applied in this study. GWAS summary data for OC are available from OCAC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ocac.ccge.medschl.cam.ac.uk/\u003c/span\u003e\u003c/span\u003e). GWAS summary statistics of HbA1c, type 2 diabetes, fasting glucose, glucose, body mass index, systolic blood pressure, smoking status and alcohol drinking status are available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/\u003c/span\u003e\u003c/span\u003e. Data from eQTLGen consortium and the GTEx database can be found in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTorre LA, Trabert B, DeSantis CE, Miller KD, Samimi G, Runowicz CD, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018;68(4):284\u0026ndash;296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Int J Surg. 2025 Feb 5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, Chen B, Zheng C, Zeng H, Zhou J, Chen Y, et al. Association of glucose-lowering drug target and risk of gastrointestinal cancer: a mendelian randomization study. Cell Biosci. 2024;14(1):36.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, antidiabetic target genes, gene expression, mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-7173550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7173550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOvarian 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eLeveraging 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 (SMR), 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe systematically analyzed the target genes from nine antidiabetic drugs for associations with nine OC phenotypes. Notably, multiple target genes showed consistent and significant associations with specific OC subtypes. For instance, AKR1A1 was linked to low-grade serous OC; HMGCR and KCNJ11 to clear cell OC; ITGAL and AKR1B1 to mucinous OC; and AKR1A1 and ITGAL to endometrioid OC\u0026mdash;with these associations supported by at least two MR methods. In contrast, the genetic associations for high-grade serous OC (HGSOC) incidence risk were less robust, as they were only supported by a single MR method. In contrast, the survival outcome of HGSOC demonstrated a more reliable genetic link, with DPP4 consistently implicated by both SMR and colocalization analysis, suggesting a potential role in prognosis rather than initiation. This divergence highlights subtype-specific biological mechanisms, in which antidiabetic drug targets may influence HGSOC progression differently from its development.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur study presents the initial systematic findings highlighting the substantial heterogeneity in the relationships between OC and diabetes mellitus across different pathological subtypes by integrating multiple MR approaches. These findings offer a critical theoretical foundation for developing pathology-specific therapeutic targets for OC.\u003c/p\u003e","manuscriptTitle":"Subtype-Specific Causal Effects of Antidiabetic Drug Targets on Ovarian Cancer: Mendelian Randomization and Colocalization Evidence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 10:16:45","doi":"10.21203/rs.3.rs-7173550/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5f195ba-6ce3-4412-9f79-38229ccb015e","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-15T02:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 10:16:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7173550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7173550","identity":"rs-7173550","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}