2017  0,977
2016  0,799
2015  0,662
2014  0,740
2013  0,739
2012  0,637
2011  0,658
2010  0,654
2009  0,570
2008  0,849
2007  0,805
2006  0,330
2005  0,435
2004  0,623
2003  0,567
2002  0,641
2001  0,490
2000  0,477
1999  0,762
1998  0,785
1997  0,507
1996  0,518
1995  0,502
Vol 52(2018) N 6 p. 872-877; DOI 10.1134/S0026893318060146 Full Text

G.J. Osmak1,2*, N.A. Matveeva1,2, B.V. Titov1, O.O. Favorova1,2

The Myocardial Infarction Associated Variant in the MIR196A2 Gene and Presumable Signaling Pathways to Involve miR-196a2 in the Pathological Phenotype

1National Medical Research Center for Cardiology, Moscow, 121552 Russia
2Pirogov Russian National Research Medical University, Moscow, 117997 Russia

Received - 2018-06-07; Accepted - 2018-06-18

The heritable component of susceptibility to myocardial infraction (MI) remains unexplained, possibly due to the minor effects of genes, which are not obviously associated with the disease. These genes may be integrated in miRNA regulated networks associated with myocardial infarction. A systematic review of the literature led us to selecting rs2910164 (MIR146A), rs11614913 (MIR196A2), and rs3746444 (MIR499А) variants to study the association with the MI phenotype. In ethnic Russians, variant rs11614913*C (MIR196A2) was found to be associated with the risk of myocardial infraction (p = 0.023, OR = 1.74) for the first time; this association was validated in an independent cohort. The gene-gene interaction network for experimentally validated miR-196a2 target genes was built and analyzed. One of its four topological clusters contained the majority of miR-196a2 target genes associated with atherosclerosis, coronary artery disease or myocardial infarction and was enriched with the genes regulating the TGFβ and class I MHC signaling pathways, platelet activation/aggregation, and the cell cycle control. This analysis points towards the role of miR-196a2 in the pathological coronary phenotypes and opens up an avenue for further investigations.

myocardial infarction, miRNA, target genes, allelic polymorphism, gene network analysis