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BMC Cardiovasc Disord. 2017 Jun 30;17(1):173. doi: 10.1186/s12872-017-0609-z.

Identifying differential miR and gene consensus patterns in peripheral blood of patients with cardiovascular diseases from literature data.

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Vilnius University, Faculty of Medicine, Vilnius, Lithuania.
Vilnius University Hospital Santariškių Klinikos, Vilnius, Lithuania.
Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Institute for Automation Engineering (IFAT), Laboratory for Systems Theory and Automatic Control, Otto-von-Guericke University Magdeburg, 39106, Magdeburg, Germany.



Numerous recent studies suggest the potential of circulating MicroRNAs (miRs) in peripheral blood samples as diagnostic or prognostic markers for coronary artery disease (CAD), acute coronary syndrome (ACS) and heart failure (HF). However, literature often remains inconclusive regarding as to which markers are most indicative for which of the above diseases. This shortcoming is mainly due to the lack of a systematic analyses and absence of information on the functional pathophysiological role of these miRs and their target genes.


We here provide an-easy-to-use scoring approach to investigate the likelihood of regulation of several miRs and their target genes from literature by identifying consensus patterns of regulation. We therefore have screened over 1000 articles that study mRNA markers in cardiovascular and metabolic diseases, and devised a scoring algorithm to identify consensus means for miRs and genes regulation across several studies. We then aimed to identify differential markers between CAD, ACS and HF.


We first identified miRs (miR-122, -126, -223, -138 and -370) as commonly regulated within a group of metabolic disease, while investigating cardiac-related pathologies (CAD, ACS, HF) revealed a decisive role of miR-1, -499, -208b, and -133a. Looking at differential markers between cardiovascular disease revealed miR-1, miR-208a and miR-133a to distinguish ACS and CAD to HF. Relating differentially expressed miRs to their putative gene targets using MirTarBase, we further identified HCN2/4 and LASP1 as potential markers of CAD and ACS, but not in HF. Likewise, BLC-2 was found oppositely regulated between CAD and HF. Interestingly, while studying overlap in target genes between CAD, ACS and HF only revealed little similarities, mapping these genes to gene ontology terms revealed a surprising similarity between CAD and ACS compared to HF.


We conclude that our analysis using gene and miR scores allows the extraction of meaningful markers and the elucidation of differential pathological functions between cardiac diseases and provides a novel approach for literature screening for miR and gene consensus patterns. The analysis is easy to use and extendable upon further emergent literature as we provide an Excel sheet for this analysis to the community.

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