Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

中文翻译：

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通过生物信息学分析糖尿病肾病补体系统的作用机制

目标。糖尿病肾病（DN）是全球终末期肾病（ESRD）的主要原因，通过生物信息学分析鉴定新的生物标志物可为未来DN模型和患者的实验验证和大组队列提供研究基础。方法. 从基因表达综合 (GEO) 数据库下载 GSE30528、GSE47183 和 GSE104948 以查找差异表达基因 (DEG)。GEO2R首先筛选正常肾组织和DN肾组织基因表达的差异。然后，通过STRING数据库对DEGs的蛋白质-蛋白质相互作用(PPI)进行分析，通过Cytoscape软件对结果进行整合和可视化，并通过MCODE和拓扑分析选择该PPI网络中的hub基因。进行基因本体论 (GO) 和京都基因和基因组百科全书 (KEGG) 通路富集分析以确定 DEG 参与 DN 进展的分子机制。最后，利用Nephroseq v5在线平台探索枢纽基因与DN临床特征的相关性。结果。共有64个DEG，鉴定出32个hub基因，丰富的hub基因途径涉及补体结合、细胞外基质结构成分、补体级联相关途径和ECM蛋白多糖等多种功能和表达途径。7个补体级联相关hub基因的相关性分析和亚组分析与DN的临床特征表明C1QA、C1QB、C3、CFB、ITGB2、VSIG4和CLU可能参与了DN的发生发展。结论。我们证实补体级联相关中枢基因可能是DN早期诊断和靶向治疗的新型生物标志物。