Literature DB >> 22435020

Network-centric Analysis of Genetic Predisposition in Diabetic Nephropathy.

A Ntemka, F Iliadis, Na Papanikolaou, D Grekas.   

Abstract

Diabetic nephropathy is a serious, long-term complication of diabetes and the leading cause of end-stage renal disease throughout the world. Although this disease is progressively imposing a heavier burden on the health care system, in many aspects it remains poorly understood. In addition to environmental influences, there is abundant evidence in support of genetic susceptibility to microvascular complications of nephropathy in diabetic patients. Familial clustering of phenotypes such as end-stage renal disease, albuminuria and kidney disease have been reported in large scale population studies throughout the world demonstrating strong contribution of inherited factors. Recent genome-wide linkage scans identified several chromosomal regions that are likely to contain diabetic nephropathy susceptibility genes, and association analyses have evaluated positional candidate genes under linkage peaks. In this review we have extracted from the literature the most promising candidate genes thought to confer susceptibility to diabetic nephropathy and mapped them to affected pathways by using network-centric analysis. Several of the top susceptibility genes have been identified as network hubs and bottlenecks suggesting that they might be important agents in the onset of diabetic nephropathy.

Entities:  

Keywords:  ECM; diabetes; gene ontology; nephropathy; networks; renal structural changes; review

Year:  2011        PMID: 22435020      PMCID: PMC3306029     

Source DB:  PubMed          Journal:  Hippokratia        ISSN: 1108-4189            Impact factor:   0.471


  22 in total

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  6 in total

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Journal:  Indian J Pharmacol       Date:  2014 Nov-Dec       Impact factor: 1.200

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

Authors:  Chengji Cui; Yabin Cui; Yanyan Fu; Sichao Ma; Shoulin Zhang
Journal:  Mol Med Rep       Date:  2017-11-28       Impact factor: 2.952

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Authors:  Rajesh Maheshwari; Ramachandran Balaraman; Ashim Kumar Sen; Disha Shukla; Avinash Seth
Journal:  Ren Fail       Date:  2016-11-13       Impact factor: 2.606

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Journal:  Iran J Public Health       Date:  2012-07-31       Impact factor: 1.429

6.  Intragraft transcriptional profiling of renal transplant patients with tubular dysfunction reveals mechanisms underlying graft injury and recovery.

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Journal:  Hum Genomics       Date:  2016-01-07       Impact factor: 4.639

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