Uncovering the molecular mechanism of Gynostemma pentaphyllum (Thunb.) Makino against breast cancer using network pharmacology and molecular docking

被引:0
作者
Wang, Wen-Xiang [1 ]
He, Xiao-Yan [2 ]
Yi, Dong-Yang [1 ]
Tan, Xiao-Yan [1 ]
Wu, Li-Juan [2 ]
Li, Ning [1 ,3 ]
Feng, Bin-Bin [1 ]
机构
[1] Chongqing Three Gorges Med Coll, Sch Pharm, Chongqing, Peoples R China
[2] Chengdu Univ Tradit Chinese Med, Chengdu, Peoples R China
[3] Chongqing Three Gorges Med Coll, Sch Pharm, Chongqing 404120, Peoples R China
基金
中国国家自然科学基金;
关键词
breast cancer; Gynostemma pentaphyllum (Thunb; ) Makin; mechanism; molecular docking; network pharmacology; RECEPTOR; TARGET; EGFR; PROTEOGLYCANS; QUERCETIN; THERAPY;
D O I
10.1097/MD.0000000000032165
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Because of their strong anti-cancer efficacy with fewer side effects, traditional Chinese medicines (TCM) have attracted considerable attention for their potential application in treating breast cancer (BC). However, knowledge about the underlying systematic mechanisms is scarce. Gynostemma pentaphyllum (Thunb.) Makino (GP), a creeping herb, has been regularly used as a TCM to prevent and treat tumors including BC. Again, mechanisms underlying its anti-BC properties have remained elusive. We used network pharmacology and molecular docking to explore the mechanistic details of GP against BC. The TCM systems pharmacology database and analysis platform and PharmMapper Server database were used to retrieve the chemical constituents and potential targets in GP. In addition, targets related to BC were identified using DrugBank and Therapeutic Target Database. Protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of crucial targets were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and database for annotation, visualization, and integrated discovery databases, whereas the network visualization analysis was performed using Cytoscape 3.8.2. In addition, the molecular docking technique was used to validate network pharmacology-based predictions. A comparison of the predicted targets of GP with those of BC-related drugs revealed 26 potential key targets related to the treatment of BC, among which ALB, EGFR, ESR1, AR, PGR, and HSP90AA1 were considered the major potential targets. Finally, network pharmacology-based prediction results were preliminarily verified by molecular docking experiments. In addition, chemical constituents and potential target proteins were scored, followed by a comparison with the ligands of the protein. We provide a network of pharmacology-based molecular mechanistic insights on the therapeutic action of GP against BC. We believe that our data will serve as a basis to conduct future studies and promote the clinical applications of GP.
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页数:11
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