Modern College of Humanities and Science of Shanxi Normal University, Linfen, PR China.
School of Life Science, Shanxi Normal University, Linfen, PR China.
Drug Dev Ind Pharm. 2022 May;48(5):189-197. doi: 10.1080/03639045.2022.2088785. Epub 2022 Aug 5.
The network pharmacology approach and molecular docking were employed to explore the mechanism of (PF) against prostate cancer (PCa).
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).
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.
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.
采用网络药理学方法和分子对接技术探讨（PF）抗前列腺癌（PCa）的作用机制。
通过中药系统药理学数据库和分析平台（TCMSP）筛选 PF 的活性成分及其潜在作用靶点，从 UniProt 数据库中获取靶点的基因名称。从 GeneCards 和 DisGeNET 数据库中获取与 PCa 相关的基因集。通过在线软件 Draw Venn Diagram 将药物靶点和疾病靶点进行合并。将得到的基因列表导入 R 软件（v3.6.3）进行 GO 和 KEGG 功能富集分析。利用 STRING 数据库构建蛋白质-蛋白质相互作用（PPI）网络。利用 Cytoscape 中的 cytoHubba 插件识别核心基因。进一步使用 AutoDock Vina 软件（v1.5.6）对关键靶点进行分子对接分析。
PF 共筛选出 6 个活性成分，对应 167 个潜在作用靶点，与 PCa 相关的靶点有 1395 个，与药物和疾病相关的靶点有 113 个。通过 Cytoscape 软件构建“药物-成分-疾病-靶点”网络图，预测 PF 治疗 PCa 的作用靶点主要涉及与氧化应激反应、细胞因子活性、癌症通路、PCa 通路和肿瘤坏死因子（TNF）信号通路相关的复杂治疗效果。PPI 网络中的核心基因有 TNF、JUN、IL6、IL1B、CXCL8、RELA、CCL2、TP53、IL10 和 FOS。分子对接结果显示，6 个活性成分与核心靶点具有较好的结合亲和力。
本研究结果表明，PF 可能通过调节相关靶基因，影响癌症通路、TNF 信号通路和乙型肝炎信号通路，对 PCa 发挥一定的治疗作用。
