摘要
基于数据挖掘分析国医大师治疗水肿验案的组方特点及核心方,并利用网络药理学阐释核心方治疗水肿的作用机制。通过中国知网(CNKI)收集并整理3届国医大师治疗水肿有效病案的期刊报道,利用中医传承计算平台3.0构建数据库,并通过关联规则和k均值聚类等方法分析病案处方,利用TCMSP和TCMID数据库检索成分并筛选靶点,在DrugBank和GeneCards数据库查找水肿(edema)相关的疾病靶点,通过STRING 11.0构建蛋白质-蛋白质相互作用(PPI)网络筛选出核心靶点,利用FunRich 3.1.3软件富集核心方的表达部位,利用Metascape数据库对交集靶点进行京都基因与基因组百科全书(KEGG)富集分析,运用Cytoscape 3.6.0软件进行"中药-活性成分-核心靶点-通路"网络可视化展示。结果发现治疗水肿的国医大师验案315则,关联规则和聚类分析获得5个核心方。核心方1:茯苓、白术、黄芪、泽泻、甘草、党参,包含166种化学成分,涉及1 125个靶点。核心方2:黄芪、丹参、茯苓、川芎、赤芍、当归,包括化学成分138种,涉及1 112个靶点。核心方3:茯苓、丹参、黄芪、白术、泽泻、薏苡仁,涉及化学成分126种,1 121个靶点。核心方4:茯苓、连翘、白术、白茅根、蝉蜕、薏苡仁,涉及化学成分58种,820个靶点。核心方5:茯苓、白术、黄芪、泽泻、鳖甲、山药,涉及化学成分68种,919个靶点。5个核心方的核心靶点均涉及AKT1、ALB、CASP3、MAPK3、EGFR、SRC、MAPK1、TNF,且5个核心方治疗水肿潜在靶点大多在胃、膀胱、肺、肾中较高表达。KEGG通路富集于炎症和细胞周期等相关通路,其中炎症相关通路涉及最多,推测其可能通过健脾、利水、活血和益气等作用发挥消炎、调节免疫系统等治疗水肿,该研究为国医大师经验总结和新药研发提供参考。
The core prescriptions and formulation characteristics in the treatment of edema by traditional Chinese medicine(TCM) masters were analyzed through data mining and their mechanisms were explored by network pharmacology.We collected journal reports on the treatment of edema by TCM masters in three sessions from China National Knowledge Infrastructure(CNKI) and constructed a database by Traditional Chinese Medicine Inheritance Support System 3.0.The prescriptions in the case studies were analyzed by association rules and k-means clustering.The chemical components and targets of Chinese medicines in core prescriptions were collected through TCMSP and TCMID.Edema-related targets were collected from DrugBank and GeneCards.The protein-protein interaction(PPI) network was constructed by STRING and the core targets were screened out.FunRich 3.1.3 was used to enrich the expression sites of core prescriptions.Metascape was used to perform Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis of intersection targets.Cytoscape 3.6.0 was used to visualize the "Chinese medicine-active ingredient-core target-pathway" network.The results showed that 315 pieces of medical records in the treatment of edema by TCM masters were obtained and five core prescriptions were analyzed by association rules and k-means clustering.Core prescription 1 contained Poria,Atractylodis Macrocephalae Rhizoma,Astragali Radix,Alismatis Rhizoma,Glycyrrhizae Radix et Rhizoma,and Codonopsis Radix,involving 166 chemical components and 1 125 targets.Core prescription 2 contained Astragali Radix,Salviae Miltiorrhizae Radix et Rhizoma,Poria,Chuanxiong Rhizoma,Paeoniae Radix Rubra,and Angelicae Sinensis Radix,involving 138 chemical components and 1 112 targets.Core prescription 3 contained Poria,Salviae Miltiorrhizae Radix et Rhizoma,Astragali Radix,Atractylodis Macrocephalae Rhizoma,Alismatis Rhizoma,and Coicis Semen,involving 126 chemical components and 1 121 targets.Core prescription 4 contained Poria,Forsythiae Fructus,Atractylodis Macrocephala
作者
孟祥飞
张丰荣
王波
唐仕欢
MENG Xiang-fei;ZHANG Feng-rong;WANG Bo;TANG Shi-huan(Wangjing Hospital,China Academy of Chinese Medical Sciences,Beijing 100102,China;Institute of Chinese Materia Medica,China Academy of Chinese Medical Sciences,Beijing 100700,China)
出处
《中国中药杂志》
CAS
CSCD
北大核心
2022年第3期764-775,共12页
China Journal of Chinese Materia Medica
基金
国家重点研发计划项目(2018YFC1704101)
中国中医科学院科技创新工程项目(CI2021A03705)
国家“重大新药创制”科技重大专项(2019ZX09201005,2019ZX09721001-005-002)
山东省重点研发计划项目(2017CXGC1301)。
关键词
国医大师
水肿
核心方
数据挖掘
网络药理学
traditional Chinese medicine master
edema
core prescriptions
data mining
network pharmacology
