目的 利用網(wǎng)絡(luò)藥理學結(jié)合TCGA和GEO數(shù)據(jù)集以及分子對接系統(tǒng)性探索黃芪甲苷抗肝癌的分子機制。方法 通過CTD、OMIM及PharmMapper數(shù)據(jù)庫預測黃芪甲苷靶點。計算TCGA和GEO數(shù)據(jù)集差異基因作為肝癌預測靶點并檢索CTD和GeneCards數(shù)據(jù)庫作為補充。用STRING數(shù)據(jù)庫和Cytoscape軟件構(gòu)建靶點蛋白相互作用（PPI）網(wǎng)絡(luò)，并篩選核心靶點?！癱lusterProfiler”R包用來對靶點富集分析。運用AutoDuck和PyMOL軟件進行分子對接。結(jié)果 共預測到201個黃芪甲苷抗肝癌靶點，主要與老化、氧化應激及脂質(zhì)代謝有關(guān)，且京都基因與基因組百科全書（KEGG）富集通路與肝癌密切相關(guān)。201個靶點中有9個關(guān)鍵靶點，分子對接發(fā)現(xiàn)白細胞介素-6（IL-6）、絲裂原活化蛋白激酶3（MAPK3）、白蛋白（ALB）與黃芪甲苷有良好結(jié)合力。結(jié)論 黃芪甲苷通過多靶點和多通路實現(xiàn)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

甘肅省自然科學基金資助項目（21JR7RA402）；蘭州大學第二醫(yī)院萃英科技創(chuàng)新計劃項目（CY2019-BJ02）