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Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis.

ABSTRACT: MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods.Gene expression profiling data related to DN (GSE1009) was obtained from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) between DN patients and normal individuals were screened using GEO2R, followed by a series of bioinformatics analyses, including identifying key genes, conducting pathway enrichment analysis, predicting and identifying key miRNAs, and establishing regulatory relationships between key miRNAs and their target genes.A total of 600 DEGs associated with DN were identified. An additional 7 key DEGs, including 6 downregulated genes, such as vascular endothelial growth factor ? (VEGFA) and COL4A5, and 1 upregulated gene (CCL19), were identified in another dataset (GSE30528) from glomeruli samples. Pathway analysis showed that the down- and upregulated DEGs were enriched in 14 and 6 pathways, respectively, with 7 key genes mainly involved in extracellular matrix-receptor interaction, PI3K/Akt signaling, focal adhesion, and Rap1 signaling. The relationships between miRNAs and target genes were constructed, showing that miR-29 targeted COL4A and VEGFA, miR-200 targeted VEGFA, miR-25 targeted ITGAV, and miR-27 targeted EGFR.MiR-29 and miR-200 may play important roles in DN. VEGFA and COL4A5 were targeted by miR-29 and VEGFA by miR-200, which may mediate multiple signaling pathways leading to the pathogenesis and development of DN.

SUBMITTER: Yang F

PROVIDER: S-EPMC6635158 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis.

Yang Fengying F Cui Zhenhai Z Deng Hongjun H Wang Ying Y Chen Yang Y Li Huiqing H Yuan Li L

Medicine 20190701 27

MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods.Gene expression profiling data related to DN (GSE1009) was obtained from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) between DN patients and normal individuals were screened using GEO2R, followed by a series o ...[more]

PMID: 31277135

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Project description:BackgroundMicroRNA (miRNA) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (DN), but its mechanism of action is still unclear.ObjectiveWith the help of the GEO database, bioinformatics methods are used to explore the miRNA-mRNA regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action.MethodsThe DN-related miRNA microarray dataset (GSE51674) and mRNA expression dataset (GSE30122) are downloaded through the GEO database, online analysis tool GEO2R is used for data differential expression analysis, TargetScan, miRTarBase, and miRDB databases are used to predict potential downstream target genes regulated by differentially expressed miRNAs, and intersection with differential genes is used to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair is clarified, and the miRNA-mRNA regulatory network is constructed using Cytoscape. DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. By GeneMANIA prediction of miRNA target genes and coexpressed genes, the protein interaction network is constructed. Results and Conclusions. A total of 67 differentially expressed miRNAs were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed mRNAs were screened, of which 93 were upregulated and 355 were downregulated. Using TargetScan, miRTarBase, and miRDB databases to predict downstream targets of differentially expressed miRNAs, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 96 candidate target genes. Finally, 44 miRNA-mRNA relationship pairs consisting of 12 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out; further analysis showed that miRNA regulatory network genes may participate in the occurrence and development of diabetic nephropathy through PI3K/Akt, ECM-receptor interaction pathway, and RAS signaling pathway.

Project description:The aim of the present study was to identify genes under the effect of transforming growth factor-β (TGF-β1), high glucose (HG) and glucosamine (GlcN) in MES-13 mesangial cells and elucidate the molecular mechanisms of diabetic nephropathy (DN). The gene expression datasets GSE2557 and GSE2558 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were independently screened using the GEO2R online tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. The hub genes were identified by the NetworkAnalyzer plugin. Overlapping genes were subjected to molecular docking analysis using SystemsDock. A total of 202 upregulated and 158 downregulated DEGs from the HG-treated groups, 138 upregulated and 103 downregulated DEGs from the GlcN-treated groups, and 81 upregulated and 44 downregulated DEGs from the TGF-β1-treated groups were identified. The majority of the DEGs were independently enriched in 'nucleosome assembly', 'chromatin silencing' and 'xenobiotic glucuronidation'. In addition, KEGG pathways were significantly enriched in 'systemic lupus erythematosus', 'protein processing in endoplasmic reticulum' and 'aldarate metabolism pathway', and 'TNF signaling pathway' intersected in the TGF-β1-treated and HG-treated groups. In total, eight hub genes, Jun, prostaglandin-endoperoxide synthase 2 (Ptgs2), fibronectin 1 (Fn1), cyclin-dependent kinase (Cdk)2, Fos, heat shock protein family A (Hsp70) member 5 (Hspa5), Hsp90b1 and homo sapiens hypoxia upregulated 1 (Hyou1), and three overlapping genes, Ras homolog gene family, member B (RHOB), complement factor H (CFH) and Krüppel-like factor 15 (KLF15), were selected. Valsartan with RHOB, and fosinopril with CFH and KLF15 had preferential binding activity. In conclusion, Jun, Ptgs2, Fn1, Cdk2, Fos, Hspa5, Hsp90b1, Hyou1, RHOB, CFH and KLF15 may be potential therapeutic targets for mesangial cells associated with DN, which may provide insight into DN treatment strategies.

Project description:Diabetic nephropathy (DN) is the most important cause of end-stage renal disease with a poorer prognosis and high economic burdens of medical treatments. It is of great research value and clinical significance to explore potential gene targets of renal tubulointerstitial lesions in DN. To properly identify key genes associated with tubulointerstitial injury of DN, we initially performed a weighted gene coexpression network analysis of the dataset to screen out two nonconserved gene modules (dark orange and dark red). The regulation of oxidative stress-induced intrinsic apoptotic signaling pathway, PI3K-Akt signaling pathway, p38MAPK cascade, and Th1 and Th2 cell differentiation were primarily included in Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of these two modules. Next, 199 differentially expressed genes (DEGs) were identified via the limma package. Then, the GO annotation and KEGG pathways of the DEGs were primarily enriched in extracellular matrix (ECM) organization, epithelial cell migration, cell adhesion molecules (CAMs), NF-kappa B signaling pathway, and ECM-receptor interaction. Gene set enrichment analysis showed that in the DN group, the interaction of ECM-receptor, CAMs, the interaction of cytokine-cytokine receptor, and complement and coagulation cascade pathways were significantly activated. Eleven key genes, including ALB, ANXA1, ANXA2, C3, CCL2, CLU, EGF, FOS, PLG, TIMP1, and VCAM1, were selected by constructing a protein-protein interaction network, and expression validation, ECM-related pathways, and glomerular filtration rate correlation analysis were performed in the validated dataset. The upregulated expression of hub genes ANXA2 and FOS was verified by real-time quantitative PCR in HK-2 cells treated with high glucose. This study revealed potential regulatory mechanisms of renal tubulointerstitial damage and highlighted the crucial role of extracellular matrix in DN, which may promote the identification of new biomarkers and therapeutic targets.

Dataset Information

Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis.

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Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis.

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