Integrated protein network and microarray analysis to identify potential biomarkers after myocardial infarction

Abstract

A significant proportion of patients develop left ventricular (LV) dysfunction and heart failure (HF) after acute myocardial infarction (MI). Existing biomarkers of HF provide limited information after MI. To identify new prognostic biomarkers in MI patients, we designed an approach combining protein interaction networks and microarray analysis of blood cells. Blood samples for RNA and protein analysis were taken from 127 acute MI patients. Echocardiography was performed at one month. Assuming that angiogenesis is related to cardiac repair after MI, a protein-protein interaction network of angiogenesis was constructed and analyzed. Among the 556 proteins and 686 interactions of this network, a cluster of 53 proteins highly specialized in regulation of cell growth was identified. Of these 53 proteins, 38 were found differentially expressed by microarrays between low (≤ 40%) and high (>40%) LV ejection fraction (EF) patients (n = 32). Among these 38 genes, prediction analysis identified a set of three genes able to predict significant LV dysfunction (EF ≤ 40%) with an area under the receiver operating characteristic curve (AUC) of 0.82. These three genes—vascular endothelial growth factor B, thrombospondin-1 and placental growth factor—had a stronger predictive value than brain natriuretic peptide and troponin T (AUC of 0.63). Independent validations on protein expression and quantitative PCR datasets confirmed the results. In conclusion, a new strategy is described that allows identifying new potential biomarkers. The three specific biomarkers described here remain to be validated in a larger patient population.