摘要
目的:基于网络药理学分析天麻主要挥发性成分的关键靶点及疾病分析。方法:通过文献筛选贵州天麻主要挥发性成分(相对含量>5%),通过Swiss Target Prediction进行关键挥发性成分靶点筛选,采用R对靶点进行GO和通路分析。通过Cytoscape3.5.1中的CytoHubba中的度(Degree)、最大邻居组件(maximum neighborhood component,MNC)、最大团中心性(maximal clique centrality,MCC)和应力(Stress)四种算法输出Hub基因。基于CTD对Hub靶点进行疾病分析。结果:天麻主要挥发性成分6个(相对含量>5%),共筛选出242个靶点。通过四种算法输出11个Hub靶点(IL6,MAPK3,MAPK1,EGFR,PTGS2,CNR1,PPARG,PIK3CA,ESR1,GRM5,MAPK14)。发现11个Hub靶点主要在肿瘤(食道癌、结直肠癌、上皮性卵巢癌)和精神分裂症等疾病中发挥作用。结论:初步筛选出天麻主要挥发性成分的关键靶点及疾病分析,为动物和细胞实验提供科学依据。
Objective:To analyze the key target of the main volatile components of Gastrodia elata Bl.and the diseases it can treat based on network pharmacology.Methods:The main volatile components(relative content>5%)of Gastrodia elata Bl.from Guizhou were screened by literature.Critical volatile component target was screened out through Swiss Target Prediction,target GO and pathway were analyzed by using R.Hub genes were output by four algorithms of degree,Maximum neighborhood component(MNC),maximal clique centrality(MCC)and stress of cytoHubba by using Cytoscape3.5.1.Disease analysis of Hub targets was carried out based on CTD.Results:There were 6 main volatile components in Gastrodia elata Bl.(relative content>5%),and a total of 242 targets were screened.11 hub targets(IL6,MAPK3,MAPK1,EGFR,PTGS2,CNR1,PPARG,PIK3CA,ESR1,GRM5,MAPK14)were output,which mainly play a role in diseases of tumors(esophageal cancer,colorectal cancer,epithelial ovarian cancer)and schizophrenia.Conclusion:The key targets and disease analysis of main volatile components of Gastrodia elata Bl.were preliminarily selected in this study,which provided scientific basis for animal and cell experiments.
作者
金继华
李专
杨欣
尹艾荟
杨玲燕
刘娇
JIN Jihua;LI Zhuan;YANG Xin;YIN Aihui;YNG Lingyan;LIU Jiao(Guizhou University of Traditional Chinese Medicine, Guizhou 550025, China)
出处
《中医药信息》
2021年第1期17-23,共7页
Information on Traditional Chinese Medicine
基金
贵州省科技计划项目(黔科合基础〔2019〕1028号)
贵州中医药大学大学生创新创业训练计划项目(贵中医大创合字〔2019〕16号)
贵州省教育厅青年科技人才成长项目(黔教合KY字〔2018〕211)。
关键词
天麻
化学成分
网络药理学
Hub靶点
疾病分析
Gastrodia elata Bl.
Chemical constituents
Network pharmacology
Hub targets
Disease analysis