Integrated Network and Microarray Analysis to Identify New Biomarkers in Ischemic Heart Disease

A significant proportion of acute myocardial infarction (MI) patients develop heart failure (HF). Early identification of patients at risk of developing HF after MI would be a major breakthrough. An approach combining the power of biological information networks and the precision of microarray analysis was undertaken to identify biomarkers of HF. A set of three new biomarkers of HF was identified and provided significantly improved prediction accuracy than microarray or network analysis alone. These three biomarkers, Vascular Endothelial Growth Factor B, thrombospondin-1 and placental growth factor are all related to angiogenesis, a natural process that favors the revascularization of the heart after MI and prevents the occurrence of HF. Therefore, combined network and microarray analysis allows a systematic and less biased approach to biomarker discovery. The 32 patients of this study were divided in 2 groups corresponding to the extreme quartiles of FE values. The first group are the patients with low Ejection fraction and the second group are the patients with high ejection fraction. Blood sample: 2.5 mL harvested in PAXgene® Blood RNA tubes (PreAnalytix) RNA extraction: PAXgene® Blood RNA kit (Qiagen) We used a Universel Reference RNA (Stratagene) RNA amplification and labelling: kit Amino Allyl MessageAmp II (Ambion) We hybridized 4 microarrays per patient using pangenomic microarrays from the "Réseau National des Génopôles" (Illkirch, France). 2 slides were hybridized with reference RNA labelled Cy3 and patient RNA labelled Cy5, and 2 slides were hybridized with reference RNA labelled Cy5 and patient RNA labelled Cy3. Hybridation : Agilent protocol with few modifications : 750 ng of each labelled RNA were hubriddized at 60°C during 17 hours in an Aglient hybridization oven. After washings, Slides were scanned with a GenePix 4000B scanner (Molecular Devices). Image intensity data were extracted with GenePix Pro 6.0 analysis software. Quantification of Cy3 and Cy5 and selection of good spots were performed using the MAIA software (Novikov E and Barillot E. Software package for automatic microarray image analysis (MAIA). Bioinformatics. 2007 Mar 1;23(5):639-40. The ACUITY software was then used to normalize log ratios Cy3/Cy5 with Lowess non linear normalization, to filter out genes not present in at least 3 slides out of 4, to evaluate the reproducibility of the 4 microarrays of each patient (hierarchical clustering, Self Organizing Maps). Statistical analyses to insure reproducibility was performed using Excel (correlation coefficients, ANOVA). Only slides that passed all reprocubility tests were validated.