目的 基于网络药理学及生物信息学探讨四虫片治疗下肢动脉硬化闭塞症膝下病变的潜在作用机制。 方法 通过中国知网、PubMed、Coremine、TCMSP 等数据库检索、筛选四虫片药物有效成分及其靶基因。通 过高通量基因表达数据库下载 GSE100927 数据集，筛选膝下病变的差异表达基因（DEGs）。将 DEGs 与加权基 因共表达网络分析（WGCNA）获得的基因模块取交集，获得膝下病变相关基因；然后与四虫片药物有效成分靶 基因取交集，获得四虫片干预膝下病变的靶基因；运用 Cytoscape 3.10.1 软件构建药物-有效成分-疾病-靶基 因网络。以靶基因为对象，运用最小绝对收缩和选择算子（LASSO）回归、随机森林（RF）、支持向量机-递归特 征消除（SVM-RFE）及 Boruta 算法四种机器学习方法筛选四虫片干预膝下病变的核心靶基因，并进行 GO 功能 及 KEGG 通路富集分析。使用 AutoDock Vina 1.2.3 软件对四虫片干预膝下病变的有效成分与核心靶点进行分 子对接，筛选出结合最稳定的对接构象。结果 筛选获得四虫片药物有效成分 119 个，靶基因 487 个；膝下病 变的 DEGs 295 个，其中差异表达上调基因 211 个，差异表达下调基因 84 个；DEGs 与 WGCNA 筛选的基因模 块取交集，获得膝下病变相关基因 210 个。将四虫片靶基因与膝下病变相关基因取交集，获得四虫片干预膝下 病变的靶点 9 个。通过四种机器学习方法获得四虫片干预膝下病变的核心基因 6 个：PCDH12、COL1A1、 CD68、PLCB2、CD36、DPP4。核心基因显著富集于血小板活化、脂质与动脉粥样硬化、脂肪的消化和吸收、 胆固醇代谢等信号通路。PLCB2 与有效成分的结合能较低，而 COL1A1、CD68、PLCB2、CD36、DPP4 等靶点 与有效成分均能够有效结合，且亲和力较好。结论 四虫片可能通过 Palmitate、Palmitic acid 等活性成分，作 用于 COL1A1、PCDH12 等核心靶点，调控血小板活化、脂质与动脉粥样硬化等关键通路，发挥治疗膝下病变 的潜在作用。

Objective To investigate the potential mechanism of Sichong Tablets in treating below-the-knee lesions of lower extremity arteriosclerosis obliterans （LEASO） using network pharmacology and bioinformatics. Methods Active components and target genes of Sichong Tablets were retrieved and screened from databases including CNKI，PubMed， Coremine， and TCMSP. The GSE100927 dataset was downloaded from the GEO database to identify differentially expressed genes （DEGs） in BTK lesions. BTK-related genes were obtained by intersecting DEGs with gene modules identified through weighted gene co-expression network analysis （WGCNA）. These were further intersected with target genes of Sichong Tablets to identify therapeutic targets. A drug-active component-disease-target gene network was constructed using Cytoscape 3.10.1. Four machine learning methods—least absolute shrinkage and selection operator （LASSO） regression，random forest （RF），support vector machine-recursive feature elimination （SVM-RFE），and Boruta algorithm—were applied to screen core genes. GO functional and KEGG pathway enrichment analyses were performed. Molecular docking between active components of Sichong Tablets and core genes was conducted using AutoDock Vina 1.2.3 to identify the conformation with the highest binding affinity. Results A total of 119 active components and 487 target genes of Sichong Tablets were identified. Among 295 DEGs in BTK lesions，211 were up- regulated and 84 were down-regulated. Intersection of DEGs and WGCNA modules yielded 210 BTK-related genes， which were further intersected with Sichong Tablets targets to obtain 9 therapeutic targets. Six core genes were identified through machine learning：PCDH12，COL1A1，CD68，PLCB2，CD36，and DPP4. These genes were significantly enriched in signaling pathways such as platelet activation，lipid and atherosclerosis，fat digestion and absorption，and cholesterol metabolism. PLCB2 exhibited lower binding energy with active components， while COL1A1， CD68， PLCB2，CD36，and DPP4 effectively bound to active components with good affinity. Conclusion Sichong Tablets may exert therapeutic effects on BTK lesions through active components such as Palmitate and Palmitic acid，acting on core targets including COL1A1 and PCDH12， and regulating key pathways like platelet activation and lipid and atherosclerosis.

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国家自然科学基金青年基金项目（82104860）；山东省中医药科技发展计划项目（2019-0559）；济南市卫生健康委员会科技计划项目 （2019-1-23）；山东省医药卫生科技发展计划项目（2018WS478）；济南市临床医学科技创新计划项目（202134013）；济南市医疗卫生行业高层次 人才专项经费资助项目（202412）；张红星全国名老中医药专家传承工作室建设项目（国中医药人教函〔2022〕75 号）。