基于数据挖掘和网络药理学分析中药治疗房颤用药规律及其作用机制
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R259;R285

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国家重点研发计划项目(2022YFC3500103)


Analysis of Medication Patterns and Mechanism of Chinese Medicine in Treating Atrial Fibrillation Based on Data Mining and Network Pharmacology
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    摘要:

    目的:基于数据挖掘技术分析中药治疗房颤的用药规律,并结合网络药理学分析核心中药组合治 疗房颤的作用机制。方法:在中国知网(CNKI) 等公开数据库中,获取中药治疗房颤的临床随机对照试 验(RCT) 文献,对纳入处方中药进行聚类、关联规则和复杂网络分析,并预测核心中药组合。在TCMSP、 ETCM和BATMAN-TCM数据库获取核心中药组合成分及对应靶点,在GeneCards、OMIM和DisGeNET中获取疾 病靶点,二者取交集后于String网站构建蛋白质互作(PPI) 网络,用Cytoscape筛选核心靶点,对交集靶点于 OmicShare进行基因本体(GO) 功能和京都基因与基因组百科全书(KEGG) 通路富集分析,最后进行分子对 接验证。结果:用药频次分析获得麦冬、丹参、炙甘草、五味子、生地黄等高频药物。关联规则显示补虚药组 合较多,聚类分析得到5类。复杂网络预测核心中药组合为麦冬、五味子、炙甘草、丹参、生地黄、桂枝、人 参。核心中药组合主要活性成分为丹参酮Ⅱa、梓醇、甘草苷、肉桂醛、山奈酚等。筛选获得药物靶点1 049个, 疾病靶点4 657个,交集靶点520个。关键靶点为钙调蛋白3(CALM3)、利钠肽A(NPPA)、丝裂原活化蛋白 激酶1(MAPK)、蛋白激酶B(AKT1)、白细胞介素(IL) -6等。GO分析生物过程主要涉及对含氧化合物的反 应、脂质代谢的调节等。KEGG分析主要富集到晚期糖基化终末产物与其受体(AGE-RAGE)、IL-17、Ras 等 信号通路。分子对接的5 个主要活性成分与5 个核心靶点结合均稳定。结论:中医药治疗房颤常选补益药, 补阴益气,清热凉血为遣方重点。核心中药组合可能通过丹参酮Ⅱa、梓醇、甘草苷、肉桂醛、山奈酚等成分, 作用于CALM3、NPPA、MAPK1、AKT1、IL-6等靶点,调节AGE-RAGE、IL-17、Ras等信号通路,参与调节 炎症反应、抗心肌纤维化、抗心脏重构等过程治疗房颤。

    Abstract:

    Abstract:Objective:To analyze the medication patterns of Chinese medicine in treating atrial fibrillation (AF) using data mining technology and explore the mechanism of core herbal combinations through network pharmacology. Methods: Clinical randomized controlled trial (RCT) literature on Chinese medicine treatment of AF was retrieved from CNKI and other public databases. Cluster analysis,association rule analysis,and complex network analysis were performed on the included prescriptions to identify core herbal combinations. Active ingredients and targets of core herbs were obtained from TCMSP, ETCM, and BATMAN-TCM databases, while disease targets were collected from GeneCards, OMIM, and DisGeNET. Protein-protein interaction (PPI) networks were constructed using the STRING database, and core targets were screened with Cytoscape. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted on overlapping targets using the OmicShare platform, followed by molecular docking verification. Results: High-frequency herbs included Ophiopogonis Radix,Salviae Miltiorrhizae Radix,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Schisandrae Chinensis Fructus,and Rehmanniae Radix. Association rules indicated frequent use of tonifying herbal combinations, and cluster analysis identified five categories. The core herbal combination predicted by complex network analysis was Ophiopogonis Radix,Schisandrae Chinensis Fructus,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Salviae Miltiorrhizae Radix,Rehmanniae Radix,Cinnamomi Ramulus,and Ginseng Radix. Key active ingredients included tanshinone, catalpol, liquiritin cinnamaldehyde, and kaempferol. A total of 1 049 drug targets and 4 657 disease targets were identified, with 520 overlapping targets. Core targets included CALM3, NPPA, MAPK1, AKT1, and IL-6. GO analysis revealed biological processes such as response to oxygen-containing compounds and regulation of lipid metabolism. KEGG analysis highlighted enrichment in AGE-RAGE, IL-17, and Ras signaling pathways. Molecular docking confirmed stable binding between five main active components and five key targets. Conclusion: Chinese medicine treatment of AF frequently uses tonifying herbs, with emphasis on nourishing yin, boosting qi, clearing heat,and cooling blood. The core herbal combination may treat AF by acting on CALM3,NPPA,MAPK1, AKT1,and IL-6 targets through components like tanshinone,catalpol,liquiritin,cinnamaldehyde,and kaempferol, regulating AGE-RAGE, IL-17, and Ras signaling pathways, thereby modulating inflammatory response, antimyocardial fibrosis,and anti-cardiac remodeling.

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张泽宇,夏裕.基于数据挖掘和网络药理学分析中药治疗房颤用药规律及其作用机制[J].新中医,2026,58(5):181-189

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