Potential mechanisms of in treating prostate cancer based on network pharmacology and molecular docking.

OBJECTIVE

The network pharmacology approach and molecular docking were employed to explore the mechanism of (PF) against prostate cancer (PCa).

METHODS

The active compounds and their corresponding putative targets of PF were identified by the Traditional Chinese Medicine Systems Pharmacology (TCMSP), the gene names of the targets were obtained from the UniProt database. The collection of genes associated with PCa was obtained from GeneCards and DisGeNET database. We merged the drug targets and disease targets by online software, Draw Venn Diagram. The resulting gene list was imported into R software (v3.6.3) for GO and KEGG function enrichment analysis. The STRING database was utilized for protein-protein interaction (PPI) network construction. The cytoHubba plugin of Cytoscape was used to identify core genes. Further, molecular docking analysis of the hub targets was carried out using AutoDock Vina software (v1.5.6).

RESULTS

A total of six active components were screened by PF, with 167 corresponding putative targets, 1395 related targets for PCa, and 113 targets for drugs and diseases. The 'drug-component-disease-target' network was constructed by Cytoscape software and the target genes mainly involved in the complex treating effects associated with response to oxidative stress, cytokine activity, pathways in cancer, PCa pathway, and tumor necrosis factor (TNF) signaling pathway. Core genes in the PPI network were TNF, JUN, IL6, IL1B, CXCL8, RELA, CCL2, TP53, IL10, and FOS. The molecular docking results reveal the better binding affinity of six active components to the core targets.

CONCLUSION

The results of this study indicated that PF may be have a certain anti-PCa effect by regulating related target genes, affecting pathways in cancer, TNF signaling pathway, and hepatitis B signaling pathway.