RNA测序与网络药理学联合分析糖尿病肾病病理机制及津力达颗粒治疗潜力  

Pathological mechanism of diabetic kidney disease and the therapeutic potential of Jinlida Granules by RNA sequencing integrated with network pharmacology

作　　者：苏雪华 朱亚欣 龚苹 关楚仪 李秀铭[2] 魏华 SU Xuehua;ZHU Yaxin;GONG Ping;GUAN Chuyi;LI Xiuming;WEI Hua(The Second Clinical College of Guangzhou University of Chinese Medicine,Guangdong Guangzhou 510504,China;Guangdong Provincial Hospital of Chinese Medicine,Guangdong Guangzhou 510120,China;The Second Affiliated Hospital of Guangzhou University of Chinese Medicine,State Key Laboratory of Dampness Syndrome of Chinese Medicine,Guangdong Guangzhou 510120,China)

机构地区：[1]广州中医药大学第二临床医学院,广东广州510504 [2]广东省中医院,广东广州510120 [3]广州中医药大学第二附属医院,省部共建中医湿证国家重点实验室,广东广州510120

出　　处：《中国医院药学杂志》2025年第5期561-568,共8页Chinese Journal of Hospital Pharmacy

基　　金：山东第一医科大学教育发展基金资助项目(编号:2023-10)。

摘　　要：目的:探讨糖尿病肾病的病理机制及津力达颗粒的治疗潜力。方法:通过R4.4.2软件分析GSE30122数据集来获取差异表达基因,结合GeneCards等数据库筛选出与糖尿病肾病相关的靶点并提取交集基因,使用CytoScape 3.10.2软件构建PPI网络、识别枢纽基因并进行生物信息富集分析。结合Lasso回归等机器学习方法构建生物标志物预测模型,并使用GSE104954数据集进行外部验证。利用TCMSP等数据库获取津力达颗粒的活性成分及干预靶点,并通过Autodock⁃Tools1.5.7软件进行分子对接来评估核心复合物的相互作用。结果:获取糖尿病肾病差异表达基因535个,从数据库筛选出与糖尿病肾病相关的靶点基因1534个,交集基因121个,识别39个枢纽基因,机器学习分析显示CX3CR1、CCL2、SPARC是糖尿病肾病的关键生物标志物,CX3CR1、CCL2、DKDGS对诊断具有较高预测能力。GO和KEGG分析显示,主要涉及炎症免疫反应和纤维化等相关通路。津力达颗粒的干预靶点21个,主要涉及IL-17和TNF信号通路、NOD样受体信号通路、AGE-RAGE等信号通路,且与津力达关键活性成分结合力较强。结论:糖尿病肾病主要涉及炎症免疫、纤维化等病理机制,CX3CR1、CCL2、SPARC是糖尿病肾病发病机制的关键生物标志物,而津力达颗粒通过靶控多个炎症免疫、纤维化相关的通路来发挥治疗作用。OBJECTIVE To discuss the pathological mechanism of diabetic kidney disease(DKD)and the therapeutic poten⁃tial of Jinlida Granules.METHODS The GSE30122 dataset was analyzed by R4.4.2 software to obtain the differentially expressed genes(DEGs).Targets of DKD were screened by GeneCards and other online databases and the intersecting genes were extracted.CytoScape 3.10.2 software was used to construct a protein-protein interaction(PPI)network.Key hub genes were identified,and the gene set enrichment analysis was conducted.The biomarker prediction model was constructed by machine learning methods(e.g.,Lasso regression)integrated with an external validation using the GSE104954 dataset.The active ingre⁃dients and intervention targets of Jinlida granules were obtained from the Traditional Chinese Medicine Systems Pharmacology(TCMSP)database and other databases,and molecular docking was performed by AutodockTools 1.5.7 software to evaluate the core complex interactions.RESULTS A total of 535 DEGs of DKD and 1534 target genes related to DKD were obtained,finally yielding 121 intersecting genes,and 39 hub genes.Machine learning analysis showed that CX3C chemokine receptor 1(CX3CR1),C-C motif ligand 2(CCL2),secreted protein acidic and rich in cysteine(SPARC)were the key biomarkers for DKD and CX3CR1,CCL2 and Diabetic Kidney Disease Gene Set(DKDGS)had higher predictive abilities for DKD.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses showed that inflammatory immune response and fibrosis were mainly enriched in hub genes.A total of 21 targets of Jinlida granules were identified,which were mainly enriched in the interleukin(IL)-17 and tumor necrosis factor(TNF)signaling pathways,NOD-like receptor signal⁃ing pathways,and advanced glycation end products-receptor of advanced glycation end products(AGE-RAGE)signaling path⁃ways,showing a strong bind to the key active ingredients of Jinlida granules.CONCLUSION Inflammatory immunity and fibrosis are the dominant pathological mechanisms of

关 键 词：糖尿病肾病 RNA测序 机器学习 网络药理学 分子对接 津力达颗粒 

分 类 号：R979.9[医药卫生—药品]