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Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease.

ABSTRACT: The current study aimed to identify therapeutic gene and microRNA (miRNA) biomarkers for diabetic kidney disease (DKD). The public expression profile GSE30122 was used. Following data preprocessing, the limma package was used to select differentially-expressed genes (DEGs) in DKD glomeruli samples and tubuli samples and they were compared with corresponding controls. Then overlapping DEGs in glomeruli and tubuli were identified and enriched analysis was performed. In addition, protein?protein interaction (PPI) network analysis as well as sub?network analysis was conducted. miRNAs of the overlapping DEGs were investigated using WebGestal. A total of 139 upregulated and 28 downregulated overlapping DEGs were selected, which were primarily associated with pathways involved in extracellular matrix (ECM)?receptor interactions and cytokine?cytokine receptor interactions. CD44, fibronectin 1, C?C motif chemokine ligand 5 and C?X?C motif chemokine receptor 4 were four primary nodes in the PPI network. miRNA (miR)?17?5p, miR?20a and miR?106a were important and nuclear receptor subfamily 4 group A member 3 (NR4A3), protein tyrosine phosphatase, receptor type O (PTPRO) and Kruppel like factor 9 (KLF9) were all predicted as target genes of the three miRNAs in the integrated miRNA?target network. Several genes were identified in DKD, which may be involved in pathways such as ECM?receptor interaction and cytokine?cytokine receptor interaction. Three miRNAs may also be used as biomarkers for therapy of DKD, including miR?17?5p, miR?20a and miR?106a, with the predicted targets of NR4A3, PTPRO and KLF9.

SUBMITTER: Cui C

PROVIDER: S-EPMC5783455 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease.

Cui Chengji C Cui Yabin Y Fu Yanyan Y Ma Sichao S Zhang Shoulin S

Molecular medicine reports 20171128 2

The current study aimed to identify therapeutic gene and microRNA (miRNA) biomarkers for diabetic kidney disease (DKD). The public expression profile GSE30122 was used. Following data preprocessing, the limma package was used to select differentially-expressed genes (DEGs) in DKD glomeruli samples and tubuli samples and they were compared with corresponding controls. Then overlapping DEGs in glomeruli and tubuli were identified and enriched analysis was performed. In addition, protein‑protein in ...[more]

PMID: 29207157

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Project description:BackgroundDiabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis.MethodsThree datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed.ResultsIn total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis.ConclusionsIn this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells' level and provided candidate targets for diagnosis and treatment of DKD.

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Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease.

Publications

Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease.

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