Exploring Druggable Targets Associated With Prostate Cancer Using a Network Pharmacology Approach

Background: Prostate Cancer (PCa) is the second most prevalent cancer among young Nigerian men under 55 years old, after liver cancer. Although androgen deprivation therapy is commonly utilised, it is associated with significant adverse effects. All PCa will eventually become hormone resistant. Computational methods are useful for determining protein targets for small compounds, identifying novel targets for existing medications, or repurposing them. Objective: This study aimed to explore druggable protein targets associated with PCa using a network pharmacology approach. Method: Cytoscape (v3.9.1) with the StringApp (v2.2.0) was used to analyse protein targets related to PCa. A network was imported using the STRING: disease query with a confidence score threshold of 0.7, limited to 50 protein targets. The network included proteins associated with PCa, with a likelihood that the proposed association is strong based on the disease score. Result: The disease query returned 50 prostate cancer-associated proteins, ranked from highest to lowest in terms of disease scores using Cytoscape software. The proteins with the highest disease scores were AR, FOLH1, TMPRSS2, PIK3CA, PIK3CB, PIK3CG, PIK3CD, EGFR, ERBB2, and MTOR. The disease score reflected how frequently the target proteins were mentioned in the downloaded abstract, with AR earning the highest score. Conclusion: This Insilico analysis ranked druggable targets associated with PCa in descending order by disease score. In drug discovery, the emphasis is on nodes with higher disease scores to identify the most promising targets for validation or trial. Therefore, targeting two or more of these proteins together could be a promising treatment approach for PCa.