Identification of prostate cancer potential therapeutic targets and virtual drug screening through combined Mendelian randomization study, Bayesian colocalization and molecular docking.

BACKGROUND

Prostate cancer (PCa) faces significant global challenges, including rising incidence and drug resistance. Our research aims to identify novel protein targets for innovative treatments, evaluate potential adverse effects, and find compounds with strong binding affinity.

METHODS

We conducted a proteomic Mendelian randomization (MR) study to assess the causal relationship between plasma proteins and PCa risk. Plasma protein data were obtained from the UK Biobank Pharmaceutical Proteomics Project, which includes GWAS data for 2940 plasma proteins. The PCa GWAS data were sourced from the PRACTICAL Consortium me-ta-analysis, encompassing 79,148 patients and 61,106 controls. Plasma proteins with positive MR results were further analyzed for Bayesian colocalization, druggability, and stratification. We performed a phenome-wide association study (PheWAS) on first and second tier proteins and screened first tier proteins against TS Biochem's library of 2070 anti- PCa com-pounds using molecular docking.

RESULTS

The MR study revealed five plasma proteins as protective factors and nine as risk factors for PCa (P < 0.05). Subsequent analyses identified SCP2 (OR: 2.10, 95% CI:1.45-3.03, P = 0.027) and C5 (OR: 1.23, 95% CI:1.10-1.37, P = 0.037) as promising drug targets. Molecular docking showed beta-Amyrone exhibited strong binding energy with both SCP2 and C5 (binding energies of - 9.2 kcal/mol and - 8.9 kcal/mol, respectively), suggesting its potential as a dual-target inhibitor.

CONCLUSION

Our study identified several protein biomarkers associated with PCa risk and screened potential compounds using molecular docking. These findings provide new insights and promising targets for PCa drug development, with beta-Amyrone showing strong potential as a dual-target inhibitor against both C5 and SCP2.