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Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis.

ABSTRACT: Background:Diabetic nephropathy (DN) is the leading cause of ESRD. Emerging evidence indicated that proteinuria may not be the determinant of renal survival in DN. The aim of the current study was to provide molecular signatures apart from proteinuria in DN by an integrative bioinformatics approach. Method:Affymetrix microarray datasets from microdissected glomerular and tubulointerstitial compartments of DN, healthy controls, and proteinuric disease controls including minimal change disease and membranous nephropathy were extracted from open-access database. Differentially expressed genes (DEGs) in DN versus both healthy and proteinuric controls were identified by limma package, and further defined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Hub genes were checked by protein-protein interaction networks. Results:A total of 566 glomerular and 581 tubulointerstitial DEGs were identified in DN, which were commonly differentially expressed compared to normal controls and proteinuric disease controls. The upregulated DEGs in both compartments were significantly enriched in GO biological process associated with fibrosis, inflammation, and platelet dysfunction, and largely located in extracellular space, including matrix and extracellular vesicles. Pathway analysis highlighted immune system regulation. Hub genes of the upregulated DEGs negatively correlated with estimated glomerular filtration rate (eGFR). While the downregulated DEGs and their hub genes in tubulointerstitium were enriched in pathways associated with lipid metabolism and oxidation, which positively correlated with eGFR. Conclusions:Our study identified pathways including fibrosis, inflammation, lipid metabolism, and oxidative stress contributing to the progression of DN independent of proteinuria. These genes may serve as biomarkers and therapeutic targets.

SUBMITTER: Cai F

PROVIDER: S-EPMC7336202 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis.

Cai Fanghao F Zhou Xujie X Jia Yan Y Yao Weijian W Lv Jicheng J Liu Gang G Yang Li L

BioMed research international 20200626

<h4>Background</h4>Diabetic nephropathy (DN) is the leading cause of ESRD. Emerging evidence indicated that proteinuria may not be the determinant of renal survival in DN. The aim of the current study was to provide molecular signatures apart from proteinuria in DN by an integrative bioinformatics approach.<h4>Method</h4>Affymetrix microarray datasets from microdissected glomerular and tubulointerstitial compartments of DN, healthy controls, and proteinuric disease controls including minimal cha ...[more]

PMID: 32685522

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Project description:BackgroundDiabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus (DM). A growing body of research has demonstrated that the inflammatory state plays a critical role in the incidence and development of DN. Pyroptosis is a new way of programmed cell death, which has the particularity of natural immune inflammation. The inhibition of inflammatory cytokine expression and regulation of pathways related to pyroptosis may be a novel strategy for DN treatment. The aim of this study is to identify pyroptosis-related genes and potential drugs for DN.MethodsDN differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE96804. Dataset GSE30528 and GSE142025 were downloaded to verify pyroptosis-related differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a pyroptosis-related gene predictive model. A consensus clustering analysis was performed to identify pyroptosis-related DN subtypes. Subsequently, Gene Set Variation Analysis (GSVA), Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the differences between DN clusters. A protein-protein interaction (PPI) network was used to select hub genes and DGIdb database was utilized to screen potential therapeutic drugs/compounds targeting hub genes.ResultsA total of 24 differentially expressed pyroptosis-related genes were identified in DN. A 16 gene predictive model was conducted via LASSO regression analysis. According to the expression level of these 16 genes, DN cases were divided into two subtypes, and the subtypes are mainly associated with inflammation, activation of immune response and cell metabolism. In addition, we identified 10 hub genes among these subtypes, and predicted 65 potential DN therapeutics that target key genes.ConclusionWe identified two pyroptosis-related DN clusters and 65 potential therapeutical agents/compounds for DN, which might shed a light on the treatment of DN.

Dataset Information

Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis.

Publications

Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis.

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