(1) Background: This study aimed to identify novel therapeutic targets for prostate cancer (PCa) utilizing expression quantitative trait loci (eQTL) data through Mendelian randomization (MR) analysis, while exploring the potential underlying mechanisms. (2) Methods: We employed MR analysis to evaluate the causal relationships between eQTLs and PCa. Cis-expression quantitative trait loci (cis-eQTL, exposure) data were obtained from the eQTLGen Consortium. GWAS data for prostate cancer were obtained from the UK Biobank Consortium and the FinnGen Consortium, with the UK Biobank Consortium data used for primary discovery and the FinnGen Consortium data used for replication and validation. Additionally, we conducted enrichment analysis, constructed protein interaction networks, predicted potential drugs, and performed molecular docking experiments to elucidate the functional significance and therapeutic reliability of identified targets. (3) Results: Our findings revealed that HOXA9, MPHOSPH6, SLC45A3, PBX2, and HLA-A are positively correlated with PCa risk, whereas PPARGC1A, FLOT2, TKT, CARNS1, GPBAR1, CSF1R, and TRAV21 showed negative associations. Molecular docking analysis demonstrated that GPBAR1 exhibited the highest binding affinity among the top five predicted drugs. (4) Conclusions: This study identified 12 promising drug targets for PCa through MR analysis. Therapeutics developed to target these genes are anticipated to enhance the success rate in clinical trials, thus enabling more efficient development of PCa treatments and potentially lowering overall drug development costs.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

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The ethical review and approval for the original studies are available in the respective publications. This Mendelian randomization (MR) study utilized summary-level statistics only, ensuring that no identifiable private information from the GWAS datasets was accessed or included in the analysis.

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