Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011881 (diabetic nephropathy)
 10,836 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Linkage studies have mapped loci for diabetic nephropathy and associated phenotypes on chromosome 3q. We studied 14 plausible candidate genes in the linkage region because of their potential role in vascular complications. In a large-scale study of patients from Denmark, Finland, and France who have type 1 diabetes, 1,057 case and 1,127 control subjects, as well as 532 trios, were investigated for association with diabetic nephropathy. We analyzed 69 haplotype-tagging single nucleotide polymorphisms and nonsynonymous variants that were identified by sequencing. Polymorphisms in three genes, glucose transporter 2 (SLC2A2), kininogen (KNG1), and adiponectin (ADIPOQ), showed nominal association with diabetic nephropathy in single-point analysis. The T-allele of SLC2A2_16459CT was associated with a decreased risk of diabetic nephropathy (odds ratio 0.79 [95% CI 0.66-0.96], P = 0.016), whereas the T-allele of KNG_7965CT and the A-allele of ADIPOQ_prom2GA were associated with increased risk of nephropathy (1.17 [1.03-1.32], P = 0.016; 1.46 [1.11-1.93], P = 0.006, respectively). Analyses of the transmission disequilibrium test showed similar trends only for ADIPOQ_prom2GA with the overtransmission of the A-allele to patients with diabetic nephropathy (1.52 [0.86-2.66], P = NS) and of the G-allele to patients without diabetic nephropathy (0.50 [0.27-0.92], P = 0.026). The overall significance for this variant (nominal P = 0.011) suggests that ADIPOQ might be involved in the development of diabetic nephropathy.
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PMID:Analysis of 14 candidate genes for diabetic nephropathy on chromosome 3q in European populations: strongest evidence for association with a variant in the promoter region of the adiponectin gene. 1706 57

Diabetic nephropathy (DN) is a primary cause of renal failure. However, studies providing renal gene expression profiles of diabetic tubulointerstitial injury are scarce and its molecular mechanisms still await clarification. To identify vital genes involved in the diabetic tubulointerstitial injury, three microarray data sets from gene expression omnibus (GEO) were downloaded. A total of 127 differentially expressed genes (DEGs) were identified by limma package. Gene set enrichment analysis (GSEA) plots showed that sister chromatid cohesion was the most significant enriched gene set positively correlated with the DN group while retinoid X receptor binding was the most significant enriched gene set positively correlated with the control group. Enriched Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs mostly included extracellular matrix organization, extracellular space, extracellular matrix structural constituent, and Staphylococcus aureus infection. Twenty hub genes from three significant modules were ascertained by Cytoscape. Correlation analysis and subgroup analysis between hub genes and clinical features of DN showed that ALB, ANXA1, APOH, C3, CCL19, COL1A2, COL3A1, COL4A1, COL6A3, CXCL6, DCN, EGF, HRG, KNG1, LUM, SERPINA3, SPARC, SRGN, and TIMP1 may involve in diabetic tubulointerstitial injury. ConnectivityMap analysis indicated the most significant three compounds are 5182598, thapsigargin and 5224221. In conclusion, this study may provide new insights into the molecular mechanisms underlying diabetic tubulointerstitial injury as well as potential targets for diagnosis and therapeutics of DN.
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PMID:Multiple-microarray analysis for identification of hub genes involved in tubulointerstial injury in diabetic nephropathy. 3076 31

The pathogenesis of diabetic nephropathy is not completely understood, and the effects of existing treatments are not satisfactory. Various public platforms already contain extensive data for deeper bioinformatics analysis. From the GSE30529 dataset based on diabetic nephropathy tubular samples, we identified 345 genes through differential expression analysis and weighted gene coexpression correlation network analysis. GO annotations mainly included neutrophil activation, regulation of immune effector process, positive regulation of cytokine production and neutrophil-mediated immunity. KEGG pathways mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and the AGE-RAGE signalling pathway in diabetic complications. Additional datasets were analysed to understand the mechanisms of differential gene expression from an epigenetic perspective. Differentially expressed miRNAs were obtained to construct a miRNA-mRNA network from the miRNA profiles in the GSE57674 dataset. The miR-1237-3p/SH2B3, miR-1238-5p/ZNF652 and miR-766-3p/TGFBI axes may be involved in diabetic nephropathy. The methylation levels of the 345 genes were also tested based on the gene methylation profiles of the GSE121820 dataset. The top 20 hub genes in the PPI network were discerned using the CytoHubba tool. Correlation analysis with GFR showed that SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may be involved in diabetic nephropathy. Eight small molecule compounds were identified as potential therapeutic drugs using Connectivity Map.
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PMID:Identification of C3 as a therapeutic target for diabetic nephropathy by bioinformatics analysis. 3277 79