Screening and Identification of Hub Genes in the Development of Early Diabetic Kidney Disease Based on Weighted Gene Co-Expression Network Analysis

Abstract

ObjectiveThe study aimed to screen key genes in early diabetic kidney disease (DKD) and predict their biological functions and signaling pathways using bioinformatics analysis of gene chips interrelated to early DKD in the Gene Expression Omnibus database.MethodsGene chip data for early DKD was obtained from the Gene Expression Omnibus expression profile database. We analyzed differentially expressed genes (DEGs) between patients with early DKD and healthy controls using the R language. For the screened DEGs, we predicted the biological functions and relevant signaling pathways by enrichment analysis of Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. Using the STRING database and Cytoscape software, we constructed a protein interaction network to screen hub pathogenic genes. Finally, we performed immunohistochemistry on kidney specimens from the Beijing Hospital to verify the above findings.ResultsA total of 267 differential genes were obtained using GSE142025, namely, 176 upregulated and 91 downregulated genes. GO functional annotation enrichment analysis indicated that the DEGs were mainly involved in immune inflammatory response and cytokine effects. KEGG pathway analysis indicated that C-C receptor interactions and the IL-17 signaling pathway are essential for early DKD. We identified FOS, EGR1, ATF3, and JUN as hub sites of protein interactions using a protein–protein interaction network and module analysis. We performed immunohistochemistry (IHC) on five samples of early DKD and three normal samples from the Beijing Hospital to label the proteins. This demonstrated that FOS, EGR1, ATF3, and JUN in the early DKD group were significantly downregulated.ConclusionThe four hub genes FOS, EGR1, ATF3, and JUN were strongly associated with the infiltration of monocytes, M2 macrophages, and T regulatory cells in early DKD samples. We revealed that the expression of immune response or inflammatory genes was suppressed in early DKD. Meanwhile, the FOS group of low-expression genes showed that the activated biological functions included mRNA methylation, insulin receptor binding, and protein kinase A binding. These genes and pathways may serve as potential targets for treating early DKD.

Identification and Validation of the Pyroptosis-Related Hub Gene Signature and the Associated Regulation Axis in Diabetic Keratopathy

“…Previous studies have demonstrated that the expression of NLRP3, NF- κ B p65, and p-NF- κ B protein and mRNA was significantly enhanced in the diabetic group [ 65 ]. Also, high concentrations of TNF- α and the activated IL-17 signaling pathway have been shown to play an essential role in STZ-induced diabetic corneal epithelium and diabetic nephropathy [ 66 , 67 ]. The diabetic corneas had significantly increased Toll-like receptor 4 expression, which was involved in corneal wound and nerve healing [ 68 ].…”

Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments

“…Wei screened and identified biomarkers in early DKD based on WGCNA, and initially explored the biological functions of candidate markers (30). However, the existing biomarkers are not enough to address the common DKD.…”

“…Han used machine learning to obtain two diagnostic markers of protein kinase cAMP-dependent type II regulatory subunit beta and transforming growth factor beta 1 (PRKAR2B and TGFBI, respectively) ( 29 ) in glomerular injury in diabetic nephropathy. Wei screened and identified biomarkers in early DKD based on WGCNA, and initially explored the biological functions of candidate markers ( 30 ). However, the existing biomarkers are not enough to address the common DKD.…”

Identification of potential biological processes and key genes in diabetes-related stroke through Weighted gene co-expression network analysis

“…Compared to conventional differential expression analysis, it is a more effective technique for detecting gene expression with low variation and abundance, as well as being less prone to information loss [19]. Quite a few researches have demonstrated the value of WGCNA in systematically identifying critical genes and relevant mechanisms about stroke or diabetes [19][20][21][22].…”