Qian Xiaoqing, Zhang Lingle, Xie Feng, Cheng Yingsheng, Cui Daxiang
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Centre for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Front Pharmacol. 2022 Jul 5;13:937439. doi: 10.3389/fphar.2022.937439. eCollection 2022.
The aim of the study was to use a network pharmacological method to examine the mechanism of Guishao-Liujun decoction against gastric cancer (GC). The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and the Traditional Chinese Medicine Integrated Database (TCMID) were used to obtain the chemical composition and targets of all the drugs of Guishao-Liujun decoction, and the targets of GC were screened using GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. The obtained targets were imported into Cytoscape 3.7.2 software by using the R language to take the intersection for a Venn analysis to construct active ingredient target networks, and they were imported into the STRING database to construct protein-protein interaction (PPI) networks, with the BisoGenet plugin in Cytoscape 3.7.2 being used for analyzing network topology. On the potential target of Guishao-Liujun decoction for GC, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the R-language bioconductor platform, and the outcomes were imported into Cytoscape 3.7.2 software to obtain the KEGG network map. The core targets were docked with the active components by the macromolecular docking software application AutoDock Vina. A total of 243 chemical components and 1,448 disease targets including 127 intersecting targets were discovered. AKT1, TP53, and GO functional analysis were mainly associated with ubiquitination and oxidase reduction activity. In GC treatment, the KEGG analysis revealed that Guishao-Liujun decoction mainly acted through the tumor necrosis factor (TNF), interleukin 17 (IL-17), and cancer-related signaling pathways, with the best binding performance with TP53, as indicated by the outcomes of macromolecular docking. In the treatment of GC, Guishao-Liujun decoction works with a variety of components and targets, establishing the groundwork for further research into its mechanism of action.
本研究旨在运用网络药理学方法探讨归芍六君汤治疗胃癌（GC）的作用机制。利用中药系统药理学数据库与分析平台（TCMSP）以及中医综合数据库（TCMID）获取归芍六君汤各味药物的化学成分及靶点，并通过GeneCards和人类孟德尔遗传在线数据库（OMIM）筛选GC的靶点。将获取的靶点运用R语言导入Cytoscape 3.7.2软件进行交集运算，进行韦恩分析以构建活性成分靶点网络，并导入STRING数据库构建蛋白质-蛋白质相互作用（PPI）网络，使用Cytoscape 3.7.2中的BisoGenet插件分析网络拓扑结构。针对归芍六君汤治疗GC的潜在靶点，利用R语言生物导体平台进行基因本体（GO）富集分析和京都基因与基因组百科全书（KEGG）富集分析，并将结果导入Cytoscape 3.7.2软件获取KEGG网络图。通过大分子对接软件AutoDock Vina将核心靶点与活性成分进行对接。共发现243种化学成分和1448个疾病靶点，其中包括127个交集靶点。AKT1、TP53和GO功能分析主要与泛素化和氧化酶还原活性相关。在GC治疗中，KEGG分析显示归芍六君汤主要通过肿瘤坏死因子（TNF）、白细胞介素17（IL-17）和癌症相关信号通路发挥作用，大分子对接结果表明其与TP53的结合性能最佳。在GC治疗中，归芍六君汤通过多种成分和靶点发挥作用，为进一步研究其作用机制奠定了基础。
