Li Zhanting, Liu Jianxin, Wang Weiwei, Zhao Yunchun, Yang Dengfeng, Geng Xiaodong
Department of Nephrology, Xi'an International Medical Center Hospital, Xi'an, China.
Physical Examination Section, Qinhuangdao Jungong Hospital, Qinhuangdao, China.
Ann Transl Med. 2020 Sep;8(17):1087. doi: 10.21037/atm-20-5647.
The aim of this study was to find genes with significantly aberrant expression in diabetic nephropathy (DN) and determine their underlying mechanisms.
GSE30528 and GSE1009 were obtained by querying the Gene Expression Omnibus (GEO) database. The difference in target gene expression between normal renal tissues and kidney tissues in patients with DN was screened by using the GEO2R tool. Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database, differentially expressed genes (DEGs) were analysed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Then, the protein-protein interactions (PPIs) of DEGs were analyzed by Cytoscape with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes in this PPI network were recognized by centrality analysis.
There were 110 genes with significant expression differences between normal and DN tissues. The differences in gene expression involved many functions and expression pathways, such as the formation of the extracellular matrix and the construction of the extracellular domain. The correlation analysis and subgroup analysis of 14 hub genes and the clinical characteristics of DN showed that CTGF, ALB, PDPN, FLT1, IGF1, WT1, GJA1, IGFBP2, FGF9, BMP2, FGF1, BMP7, VEGFA, and TGFBR3 may be involved in the progression of DN.
We confirmed the differentially expressed hub genes and other genes which may be the novel biomarker and target candidates in DN.
本研究旨在寻找在糖尿病肾病(DN)中表达显著异常的基因,并确定其潜在机制。
通过查询基因表达综合数据库(GEO)获得GSE30528和GSE1009。使用GEO2R工具筛选正常肾组织与DN患者肾组织中靶基因表达的差异。利用注释、可视化和综合发现数据库(DAVID),通过基因本体论(GO)注释和京都基因与基因组百科全书(KEGG)通路富集分析差异表达基因(DEG)。然后,利用Cytoscape和相互作用基因/蛋白质检索工具(STRING)数据库分析DEG的蛋白质-蛋白质相互作用(PPI),并通过中心性分析识别该PPI网络中的枢纽基因。
正常组织与DN组织之间有110个基因存在显著表达差异。基因表达差异涉及许多功能和表达途径,如细胞外基质的形成和细胞外结构域的构建。对14个枢纽基因与DN临床特征的相关性分析和亚组分析表明,结缔组织生长因子(CTGF)\、白蛋白(ALB)、血小板源性蛋白聚糖(PDPN)、血管内皮生长因子受体1(FLT1)、胰岛素样生长因子1(IGF1)、威尔姆斯瘤1基因(WT1)、缝隙连接蛋白α1(GJA1)、胰岛素样生长因子结合蛋白2(IGFBP2)、成纤维细胞生长因子9(FGF9)、骨形态发生蛋白2(BMP2)、成纤维细胞生长因子1(FGF1)、骨形态发生蛋白7(BMP7)、血管内皮生长因子A(VEGFA)和转化生长因子β受体3(TGFBR3)可能参与DN的进展。
我们确认了差异表达枢纽基因和其他可能成为DN新型生物标志物和候选靶点的基因。