基于数据挖掘和网络药理学技术探讨中医药 治疗脓毒症并发AKI的作用机制

To Explore the Mechanism of Traditional Chinese Medicine in the Treatment of Sepsis Complicated with AKI Based on Data Mining and Network Pharmacology Techniques

摘要: 目的：基于数据挖掘、网络药理学技术对中医药治疗脓毒症急性肾损伤的作用机制进行探讨。方法：收录中国知网、维普、万方等数据库中检索到的中医药治疗SA-AKI的文献作为资料来源，并统计处方组成数据库，运用IBM SPSS Modeler 18.0、IBM SPSS Statistics 27软件进行关联规则和聚类分析，最终筛除一组核心药对；通过TCMSP平台获取药对有效成分，登录SwissTargetPrediction平台预测作用靶点，运用GeneCards数据库筛选SA-AKI疾病相关靶点，并通过venny 2.1.0平台将两者取交集，得到交集靶点；将交集靶点导入STRING平台得出的数据通过Cytoscape 3.9.1软件绘制，建立PPI蛋白网络互作图，然后按BC、CC、DC值三个指标进行拓扑分析，筛选核心靶点；构建通过DAVID数据库对交集靶点进行GO和KEGG富集分析；将核心药物名称、活性成分、SA-AKI疾病靶点构建为Network、Type文件，将其导入Cytoscape 3.9.1软件，并绘制“药物–活性成分–疾病靶点”网络图。结果：共纳入文献45篇，可录入的处方48首，涉及98味中药，使用频次较多的药物有大黄、赤芍、甘草、茯苓、黄芪、丹参等，聚类分析归为三大类；筛选出的药对为大黄和丹参，涉及51个活性成分和698个作用靶点，与626个疾病靶点取交集得到88个交集靶点；5个关键靶点为IL1B、STAT3、PPARG、CASP3、HIF1A；4个核心活性成分为异泽兰黄素、香紫苏醇、丹参新醌D、(6S)-6-(羟甲基)-1,6-二甲基-8,9-二氢-7H-萘并[8,7-g]苯并呋喃-10,11-二酮；GO功能分析得到生物过程(BP) 393项、细胞组成(CC) 45项、分子功能(MF) 134项；KEGG富集分析涉及112条信号通路。结论：大黄和丹参治疗脓毒症AKI具有多组分、多靶点、多途径的协同作用特点，其药效成分与相关靶点和通路通过参与炎症反应、氧化应激反应、免疫失调、代谢紊乱、细胞凋亡等病理过程，以发挥对脓毒症AKI的治疗作用。

Abstract: Objective: Based on data mining and network pharmacology techniques, this study explores the mechanism of traditional Chinese medicine in the treatment of acute kidney injury induced by sepsis. Methods: The literature on the treatment of SA-AKI with traditional Chinese medicine retrieved from databases such as CNKI, VIP, Wanfang, etc., was included as the data sources. IBM SPSS Modeler 18.0 and IBM SPSS Statistics 27 software were used for association rule and cluster analysis, and finally, a group of core drug pairs was screened out. The effective components of the drug pairs were obtained through the TCMSP platform, and the SwissTargetPrediction platform was logged in to predict the action targets. The GeneCards database was used to screen the SA-AKI disease-related targets, and the intersection of the two was obtained through the Venny 2.1.0 platform to get the intersection targets. The data obtained by importing the intersection targets into the STRING platform were drawn using Cytoscape 3.9.1 software to establish a PPI protein network interaction map, and then topological analysis was carried out according to the three indicators of BC, CC, and DC values to screen the core targets. The DAVID database was used to perform GO and KEGG enrichment analyses on the intersection targets. The names of the core drugs, active ingredients, and SA-AKI disease targets were constructed into Network and Type files, which were imported into Cytoscape 3.9.1 software, and a “drug-active ingredient-disease target” network diagram was drawn. Results: A total of 45 studies were included, and 48 prescriptions that could be entered were involved, involving 98 traditional Chinese medicines. The drugs with higher frequencies of use were Rhubarb, Red Peony Root, Licorice, Poria, Astragalus, Salvia miltiorrhiza, etc., and the cluster analysis was classified into three categories. The screened drug pair was Rhubarb and Salvia miltiorrhiza, involving 51 active ingredients and 698 action targets. The intersection with 626 disease targets yielded 88 intersection targets. The 5 key targets were IL1B, STAT3, PPARG, CASP3, and HIF1A. The 4 core active ingredients were Eupatilin, Sclareol, SalvianolicquinoneD, and (6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione. The GO function analysis obtained 393 biological processes (BP), 45 cellular components (CC), and 134 molecular functions (MF). The KEGG enrichment analysis involved 112 signaling pathways. Conclusion: Rhubarb and Salvia miltiorrhiza have the characteristics of multi-component, multi-target, and multi-pathway synergistic effects in the treatment of SA-AKI. Their pharmacodynamic components, related targets, and pathways play a therapeutic role in SA-AKI by participating in pathological processes such as inflammatory response, oxidative stress response, immune disorder, metabolic disorder, and apoptosis.