Identification of gene expression profiles in myocardial infarction: a systematic review and meta-analysis

Abstract

Background: Myocardial infarction (MI) is a multifactorial disease with complex pathogenesis, mainly the result of the interplay of genetic and environmental risk factors. The regulation of thrombosis, inflammation and cholesterol and lipid metabolism are the main factors that have been proposed thus far to be involved in the pathogenesis of MI. Traditional risk-estimation tools depend largely on conventional risk factors but there is a need for identification of novel biochemical and genetic markers. The aim of the study is to identify differentially expressed genes that are consistently associated with the incidence myocardial infarction (MI), which could be potentially incorporated into the traditional cardiovascular diseases risk factors models.

Methods: The biomedical literature and gene expression databases, PubMed and GEO, respectively, were searched following the PRISMA guidelines. The key inclusion criteria were gene expression data derived from case-control studies on MI patients from blood samples. Gene expression datasets regarding the effect of medicinal drugs on MI were excluded. The t-test was applied to gene expression data from case-control studies in MI patients.

Results: A total of 162 articles and 174 gene expression datasets were retrieved. Of those a total of 4 gene expression datasets met the inclusion criteria, which contained data on 31,180 loci in 93 MI patients and 89 healthy individuals. Collectively, 626 differentially expressed genes were detected in MI patients as compared to non-affected individuals at an FDR q-value = 0.01. Of those, 88 genes/gene products were interconnected in an interaction network. Totally, 15 genes were identified as hubs of the network.

Conclusions: Functional enrichment analyses revealed that the DEGs and that they are mainly involved in inflammatory/wound healing, RNA processing/transport mechanisms and a yet not fully characterized pathway implicated in RNA transport and nuclear pore proteins. The overlap between the DEGs identified in this study and the genes identified through genetic-association studies is minimal. These data could be useful in future studies on the molecular mechanisms of MI as well as diagnostic and prognostic markers.

Keywords: Biomarkers; Differentially expressed genes; Gene-expression; Meta-analysis; Myocardial infarction; Risk prediction.

Fig. 1

a) Articles screened from Pubmed database b) Datasets screened from Geo Database

Fig. 2

Venn diagram comparing DEG sets identified by the individual studies and by meta-analysis. The results obtained by the meta-analysis (626 DEGs) are compared with DEGs identified by at least one study and DEGs identified by at least two studies

Fig. 3

Gene/protein association network of the 88 MI DEGs displayed in the action view. Lines of different colors indicate predicted modes of action shown in the inset with a confidence interaction cut-off score of 0.7. The network was constructed using STRING