目的 基于生物信息学构建糖尿病肾病(diabetic nephropathy，DN)竞争性内源RNA(competing endogenous RNA，ceRNA)网络，预测治疗DN潜在有效中药。方法 利用GEO数据库与R语言获取DN差异表达长链非编码RNA(differentially expressed lncRNA，DElncRNA)及差异表达信使RNA(differentially expressed mRNA，DEmRNA)，借助miRcode、TargetScan、miRTarBase、miRDB数据库整合“DElncRNA-微小RNA(microRNA，miRNA)”与“miRNA-DEmRNA”调控关系，通过Cytoscape 3.7.2软件构建ceRNA调控网络，采用Metascape平台分析ceRNA调控网络主要涉及生物过程及信号通路，借助String数据库与Cytoscape 3.7.2软件获取关键基因，并将其映射到Coremine Medical数据库中，预测治疗DN潜在中药，通过TCMSP、PubChem、UniProt、SwissTargetPrediction数据库结合文献收集整理预测中药的核心成分与对应靶点，构建“中药-活性成分-靶基因”网络，采用AutoDockTools、PyMOL对主要活性成分与关键基因进行分子对接。结果 最终获得由9个lncRNA、33个miRNA、106个mRNA所构成的ceRNA网络，主要涉及血小板衍生生长因子受体信号通路、丝裂原活化蛋白激酶(mitogen activated protein kinases，MAPK)级联、对雌二醇的反应等生物过程以及磷脂酰肌醇-3-羟激酶(phosphatidylinositol-3-hydroxykinase，PI3K)/蛋白激酶B(protein kinase B，Akt)、p53、血管内皮生长因子(vascular endothelial growth factor，VEGF)等信号通路。中药预测结果显示，雷公藤、藤黄、黄芪、三七等多味中药与DN密切相关，槲皮素、藤黄酸、异藤黄酸、山柰酚、gambogellic acid与关键基因具有良好的亲和力。结论 ceRNA调控网络的构建有助于对DN发病机制的进一步理解，预测中药可能成为治疗DN潜在药物来源。

Objective To construct a competing endogenous RNA (ceRNA) network for diabetic nephropathy (DN) based on bioinformatics, and predict the potential effective traditional Chinese medicine for treating DN. Methods The GEO database and R language were used to obtain differentially expressed lncRNA (DElncRNA) and differentially expressed mRNA (DEmRNA), respectively. The regulates relationship between “DElncRNa-microRNA (miRNA)” and “miRNA-DEmRNA” was integrated with miRcode, TargetScan, miRTarBase and miRDB databases. Cytoscape 3.7.2 software was used to construct the ceRNA regulatory network. Metascape platform was used to analyze the ceRNA regulatory network mainly involved in biological processes and signaling pathways. Key genes were obtained by String database and Cytoscape 3.7.2 software, and it was mapped into Coremine Medical database to predict the potential traditional Chinese medicine (TCM) in the treatment of DN. The core components and corresponding targets of TCM were collated and predicted through TCMSP, PubChem, UniProt and SwissTargetPrediction database combined with literature collection. The “TCM-active ingredient-target gene” network was constructed, and AutoDockTools and PyMOL were used for molecular docking of main active ingredient and key gene. Results The ceRNA network consisting of nine lncRNA, 33 miRNA and 106 mRNA was finally obtained, which mainly involved in biological processes such as platelet-derived growth factor receptor signaling pathway, mitogen activated protein kinases (MAPK) cascade, response to estradiol and phosphatidylinositol-3-hydroxykinase (PI3K)/ protein kinase B (Akt), p53, vascular endothelial growth factor (VEGF) and other signaling pathways. The prediction results of TCM showed that many TCMs such as Leigongteng (Tripterygium wilfordii), Tenghuang (Garcinia hanbury), Huangqi (Astragali Radix) and Sanqi (Notoginseng Radix et Rhizoma) were closely related to DN, and the main active ingredients, including quercetin, morellic acid, isomorellic acid, kaempferol and gambogellic acid, had good affinity with key genes. Conclusion The construction of ceRNA regulatory network is helpful to further understand the pathogenesis of DN, and it is predicted that TCM may become a potential drug source for treating DN.

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辽宁省“兴辽英才计划”青年拔尖人才项目(XLYC1807145)；辽宁省人社厅百千万人才资助项目(20200512)