目的 利用生物信息学结合网络药理学及分子对接系统性探索芹菜素抗前列腺癌的分子机制。方法 通过ETCM、HERB、HIT 2.0及SwissTargetPrediction数据库预测芹菜素靶点。计算TCGA和GEO数据集差异基因作为前列腺癌预测的疾病靶点，并检索GeneCards和OMIM数据库作为补充。应用STRING数据库和Cytoscape软件构建靶点蛋白相互作用（PPI）网络，并筛选核心靶点。采用Metascape数据库进行富集分析，使用PyMOL、AutoDock Tools、AutoDock Vina软件以及PDB数据库进行分子对接。结果 共预测到236个芹菜素抗前列腺癌作用靶点，主要与激素反应、细胞死亡调控及激酶调节有关，且京都基因与基因组百科全书（KEGG）富集通路与前列腺癌密切相关。筛选得到70个核心靶点，分子对接发现B细胞淋巴瘤因子-2（Bcl-2）、V-Jun肉瘤病毒癌基因同源物（JUN）、低氧诱导因子-1A（HIF-1A）、肿瘤坏死因子（TNF）、ERG、雌激素相关受体α（ESRRA）与芹菜素有良好结合力。结论 芹菜素通过多靶点和多通路实现抗前列腺癌作用。

Objective To explore the molecular mechanism of apigenin against prostate cancer through bioinformatics combined with network pharmacology including molecular docking. Methods To predicted apigenin drug targets were by ETCM, HERB, HIT 2.0 and SwissTargetPrediction databases. Differential genes in TCGA and GEO data sets were calculated as prostate cancer prediction targets, and supplemented by GeneCards and OMIM databases. STRING database and Cytoscape software were used to construct the target protein interaction network and screen the core targets. Metascape platform was used for target enrichment analysis. Softwares such as PyMOL, AutoDock Tools, AutoDock Vina and PDB database were used for molecular docking. Results A total of 236 apigenin against prostate cancer targets were predicted, mainly related to response to hormone, positive regulation of cell death and regulation of kinase activity, and the KEGG enrichment pathways were closely related to prostate cancer. Among the 236 targets, 70 key targets were identified by molecular docking. Bcl-2, JUN, HIF-1A, TNF, ERG, and ESRRA had a good binding ability with apigenin. Conclusion Apigenin exerts anti-prostate cancer effect through multi-target and multi-pathway mechanisms.

R285

广东省医学科学技术研究基金项目（A2020239）