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Gene. 2014 Apr 10;539(1):30-6. doi: 10.1016/j.gene.2014.02.001. Epub 2014 Feb 5.

Landscape of the relationship between type 2 diabetes and coronary heart disease through an integrated gene network analysis.

Author information

  • 1Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China. Electronic address: dongchangzheng@nbu.edu.cn.
  • 2Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.
  • 3Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China; Diabetes Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China.
  • 4The Affiliated Hospital, Ningbo University, Ningbo, Zhejiang, China.
  • 5Institute of Hypertension and Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: dwwang@tjh.tjmu.edu.cn.
  • 6Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, Ningbo, Zhejiang, China; Diabetes Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China. Electronic address: duanshiwei@nbu.edu.cn.

Abstract

Type 2 diabetes (T2D) and coronary artery disease (CAD) are closely related chronic diseases with high prevalence and morbidity. However, a comprehensive comparison of the two diseases is lacking. Recent genome-wide association studies (GWAS) have identified a handful of single nucleotide polymorphisms (SNPs) that are significantly associated with the risk of T2D and CAD. These most significant findings may help interpret the pathogenesis of T2D and CAD. However, tremendous results from these GWAS are ignored. Here we revisited the raw datasets of these GWAS and performed an integrated gene network analysis to unveil the relationship between T2D and CAD by combining multiple datasets including protein-protein interaction (PPI) database, publication libraries, and pathway datasets. Our results showed that majority of genes were involved in the first module (1122 genes in T2D and 895 in CAD). Four pathways were found to be common in both T2D and CAD, including regulation of actin cytoskeleton, calcium signaling pathway, MAPK signaling pathway and focal adhesion (all P<0.00001). MAX which was involved in small cell lung cancer pathway was a hub gene unique to T2D (OR=1.2, P=0.006) but not in CAD. In contrast, three hub genes including PLEKHG5 (T2D: OR=1, P=1; CAD: OR=1.12, P=0.006), TIAM1 (T2D: OR=1, P=1; CAD: OR=1.48, P=0.004) and AKAP13 (T2D: OR=1, P=1; CAD: OR=1.38, P=0.001) were hub genes unique to CAD. Moreover, for some hub genes (such as SMAD3) that were susceptible to both T2D and CAD, their associated polymorphisms were unique to each of the two diseases. Our findings might provide a landscape of the relationship between T2D and CAD.

Copyright © 2014 Elsevier B.V. All rights reserved.

KEYWORDS:

Coronary artery disease; Genome-wide association; Hub; Network analysis; Pathway; Protein–protein interaction; Type 2 diabetes

PMID:
24508273
[PubMed - indexed for MEDLINE]
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