基于网络药理学的头花蓼防治结石潜在成分分析
Analysis of Potential Components of Stone Prevention and Control Based on Network Pharmacology

目的：在网络药理学的基础上预测苗药头花蓼防治肾结石机制原理。方法：文献查询头花蓼作用成分录入TCMSP库筛选具体活性和靶标；Uniport数据库检索KS (肾结石)相关靶标基因；用维恩分析交集出药–疾病的交集基因；使用Cytoscape3.9.1构图软件表示出药物–成分对应关系、成分–疾病对应关系；使用STRING数据库构建交集基因PPI互作网络，导入Cytoscape利用BC排序靶点；将交集基因作GO、KEGG富集通路分析。结果：整理得到头花蓼可能治疗肾结石的6个活性成分，6个成分对应了100个基因靶点，与结石患病基因3949个基因交集出77个，PPI结果按照度值大小排名前10位的靶点分别为AKT1、TP53、PTGS2、TNF、EGF、MAPK1、EGFR、CASP3、MMP9、CCND1；AGE-RAGE信号通路、IL-17信号通路是头花蓼防治结石的主要通路。结论：头花蓼防治肾结石网络药理学预测结果在AGE-RAGE信号通路、IL-17信号通路，AKT1、TP53、PTGS2、TNF、EGF、MAPK1、EGFR、CASP3、MMP9、CCND1为关键基因靶点。
Objective: To predict the mechanism principle of preventing kidney stones based on network pharmacology. Methods: To select the specific activities and targets; Uniport database for KS (kid-ney stones); analyze drug-disease intersection genes; Cytoscape3.9.1 drug-component composition software; construct PPI interaction network using the STRING database, and import Cytoscape for BC sorting targets; use intersection genes as GO and KEGG enrichment pathway analysis. Results: Six active components, 6 components correspond to 100 gene targets, and 77 intersections with 3949 genes. The top 10 targets of PPI were AKT 1, TP 53, PTGS 2, TNF, EGF, MAPK 1, EGFR, CASP 3, MMP 9, CCND 1; AGE-RAGE signaling pathway and IL-17 signaling pathway are the main pathways for prevention of stones. Conclusion: The results of network pharmacology are found in AGE-RAGE signaling, IL-17 signaling, AKT 1, TP 53, PTGS 2, TNF, EGF, MAPK 1, EGFR, CASP 3, MMP 9, and CCND 1.