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KMID : 0365220180550010043
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Korean Journal of Public Health
2018 Volume.55 No. 1 p.43 ~ p.55
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Investigation of cardiomyopathic marker genes using gene network analysis
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Seo Moung-Seock
Cho Myeong-Ji
Son Hyeon-Seok
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Abstract
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Objectives : Cardiomyopathy is a heterogeneous disease with structural and functional abnormalities in the heart muscle, which is characterized by a prognosis of heart failure. Recently, several genes related to this have been found. In this study, we aimed to investigate marker genes that can predict the prognosis of heart failure in cardiomyopathies due to genetic factors through network analysis using microarray data.
Methods : GSE1145 data of Gene Expression Omnibus was used as microarray data. 11 of normal, 12 of idiopathic dilated cardiomyopathy, 11 of ischemic cardiomyopathy and 5 of hypertrophic cardiomyopathy were used respectively. The gene network was constructed based on the expression correlation data corresponding to the heart-left ventricle mRNA type of the genotype-tissue expression v5 group, and the centrality analysis was performed using the R program.
Results : In the case of heart failure due to cardiomyopathy, a total of 73 genes were specifically regulated. The network analysis of these genes showed high centrality of 10 genes including C1QTNF7, ECM2 and FAM188A. In the 2-mode network analysis between the above genes and the genes responsible for cardiomyopathy, 26 genes including ACTC1, ACTN2, BAG3 and DES showed a high centrality in DCM. In HCM, 10 genes including ACTC1 and ACTN2 showed a significant high centrality.
Conclusion : Genes with high centrality in 1-mode network analysis are likely to play an important role in the development of cardiac failure as a prognosis for cardiomyopathy and may therefore be a target for research and treatment of heart failure. Genes with high centrality in the 2-mode network analysis may be used as markers to predict heart failure due to myocardial prognosis through routine diagnostic tests.
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KEYWORD
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cardiomyopathy, heart failure, differential expression analysis, network analysis, marker gene
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