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肾纤维化特征基因筛选及靶向中药预测OA

Identification of key genes in renal fibrosis and prediction of targeted traditional Chinese medicine

中文摘要英文摘要

[目的]基于生物信息学方法筛选肾纤维化(renal fibrosis,RF)特征基因并探寻调控特征基因的中药.[方法]将 GEO 数据库中 RF 相关数据集 GSE76882 作 为 训 练 集,GSE22459 作为验证集,将差异表达基因(differentially expressed genes,DEGs)和加权基因共表达网络分析(weighted gene co-expression network,WGCNA)关键模块基因取交集,对交集基因进行蛋白互作网络构建和富集分析.构建7种机器学习模型,筛选特征基因并构建列线图,通过校准曲线和决策曲线对模型进行评估进而筛选出与RF相关的特征基因.接下来进行免疫浸润分析,探索特征基因与免疫细胞的相关性.基于特征基因在Coremine和TCMSP数据库中预测靶向中药及活性成分并对特征基因与活性成分进行分子对接.[结果]共获得314个RF相关DEGs,其中202个基因上调,112个基因下调.WGCNA分析揭示了与RF最相关的基因模块(blue模块),并筛选出180个交集基因.富集分析表明免疫反应、细胞因子介导的信号通路在RF中起关键作用.机器学习模型筛选出SERPINA3、CXCL10、JCHAIN、LTF和CCL19共5个关键特征基因.免疫浸润分析显示,RF组与对照组的免疫细胞浸润水平存在显著差异,且特征基因与免疫细胞具有相关性.通过中药预测筛选出27种潜在中药,主要具有活血化瘀、补虚等功效,符合中医对RF"虚瘀互结"的病机认识,预测到的中药中丹参、莪术、地龙和川芎有化瘀通络之功;鳖甲、麦冬、石斛、灵芝等有补虚固本之效,与RF病机契合.分子对接显示中药活性成分与RF特征基因具有较好的结合能力.[结论]SERPINA3、CXCL10、JCHAIN、LTF和CCL19是RF的潜在特征基因,丹参、莪术、地龙、川芎、灵芝、麦冬、石斛、鳖甲等是治疗RF的潜在中药.

[Objective]To identify key genes involved in renal fibrosis(RF)using bioinformatics methods and to explore herbs that regulate these genes.[Methods]The GSE76882 dataset from the GEO database was used as the training set and GSE22459 as the validation set.Differentially expressed genes(DEGs)and key modules identified through weighted gene co-expression network analysis(WGCNA)were intersected,and protein-protein interaction networks along with enrichment analyses were conducted.Seven machine learning models were constructed to identify feature genes,and a nomogram was generated.The models were evaluated using calibration and decision curves,leading to the identification of RF-related genes.Immune infiltration analysis was performed to explore correlations between feature genes and immune cells.Based on these genes,potential traditional Chinese medicine(TCM)herbs and active compounds were predicted using Coremine and TCMSP databases,and molecular docking was performed between the genes and the active compounds.[Results]A total of 314 RF-related DEGs were identified,with 202 genes upregulated and 112 genes downregulated.WGCNA identified the blue module as most relevant to RF,yielding 180 intersecting genes.Enrichment analysis revealed that immune responses and cytokine-mediated signaling pathways are critical in RF pathogenesis.Machine learning models identified five key feature genes:SERPINA3,CXCL10,JCHAIN,LTF,and CCL19.Immune infiltration analysis showed significant differences in immune cell infiltration between RF and control groups,and the feature genes correlated with immune cells.A total of 27 potential herbs were predicted,primarily targeting blood circulation,blood stasis,and deficiency,aligning with the TCM understanding of RF as a condition characterized by"deficiency and blood stasis".Among the predicted herbs,Salvia miltiorrhiza,Curcumae rhizoma,Pheretima,and Conioselinum anthriscoides promote blood circulation,while Trionycis carapax,Ophiopogonis radix,Dendrobii caulis,and Ganoderma are tonifying,corresponding to RF's pathogenesis.Molecular docking demonstrated strong binding between active TCM compounds and the key feature genes of RF.[Conclusion]SERPINA3,CXCL10,JCHAIN,LTF,and CCL19 are potential key genes for RF,and Salvia miltiorrhiza,Curcumae rhizoma,Pheretima,Conioselinum anthriscoides,Trionycis carapax,Ophiopogonis radix,Dendrobii caulis,and Ganoderma are potential TCM herbs for the treatment of RF.

康意;靳茜;王雪柘;金鑫燕;李梓荣;周梦琪;李晓文;王耀献;吕杰

北京中医药大学东直门医院,北京 100700||北京中医药大学,北京 100029北京中医药大学,北京 100029北京中医药大学东直门医院,北京 100700||北京中医药大学,北京 100029北京中医药大学东直门医院,北京 100700||北京中医药大学,北京 100029北京中医药大学,北京 100029北京市普仁医院,北京 100062北京中医药大学东直门医院,北京 100700北京中医药大学东直门医院,北京 100700北京中医药大学东直门医院,北京 100700

医药卫生

肾纤维化机器学习特征基因免疫浸润中药预测

renal fibrosismachine learningfeature genesimmune infiltrationtraditional Chinese medicine

《天津中医药大学学报》 2026 (5)

561-577,17

中华中医药学会联合攻关项目(2023DYPLHGG-11)国家中医药管理局中医药传承与创新"百千万"人才工程项目(国中医药人教发[2018]12号).

10.11656/j.issn.1673-9043.2026.05.08

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