目的 利用网络药理学结合TCGA和GEO数据集以及分子对接系统性探索黄芪甲苷抗肝癌的分子机制。方法 通过CTD、OMIM及PharmMapper数据库预测黄芪甲苷靶点。计算TCGA和GEO数据集差异基因作为肝癌预测靶点并检索CTD和GeneCards数据库作为补充。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。“clusterProfiler”R包用来对靶点富集分析。运用AutoDuck和PyMOL软件进行分子对接。结果 共预测到201个黄芪甲苷抗肝癌靶点，主要与老化、氧化应激及脂质代谢有关，且京都基因与基因组百科全书（KEGG）富集通路与肝癌密切相关。201个靶点中有9个关键靶点，分子对接发现白细胞介素-6（IL-6）、丝裂原活化蛋白激酶3（MAPK3）、白蛋白（ALB）与黄芪甲苷有良好结合力。结论 黄芪甲苷通过多靶点和多通路实现抗肝癌作用。

Objective To explore the molecular mechanism of astragaloside IV against liver cancer through network pharmacology combined with TCGA and GEO data sets and molecular docking. Methods Astragaloside IV drug targets were predicted by CTD, OMIM, and PharmMapper databases. Differential genes in TCGA and GEO data sets were calculated as liver cancer prediction targets and supplemented by CTD and GeneCards databases. STRING database and Cytoscape software were used to construct the target protein interaction network and screen the core targets. The "clusterProfiler" R package was used for target enrichment analysis. Softwares such as AutoDuck and PyMOL were used for molecular docking. Results A total of 201 astragaloside IV against liver cancer targets were predicted, mainly related to aging, oxidative stress, and lipid metabolism, and the KEGG enrichment pathway was closely related to liver cancer. Among the 201 targets, nine key targets were identified by molecular docking. IL-6, CASP3, and ALB had a good binding ability with astragaloside IV. Conclusion Astragaloside IV achieves anti- liver cancer effects through multi-target and multi-pathway.

R285；R979.1

甘肃省自然科学基金资助项目（21JR7RA402）；兰州大学第二医院萃英科技创新计划项目（CY2019-BJ02）