Integration of partial least squares and Monte Carlo gene expression analysis in coronary artery disease

Coronary artery disease (CAD) is the most common type of cardiovascular disease and leading cause of mortality worldwide. Microarray technology for gene expression analysis has facilitated the identification of the molecular mechanism that underlies the pathogenesis of CAD. Previous studies have primarily used variance or regression analysis, without considering array specific factors. Thus, the aim of the present study was to investigate the mechanism of CAD using partial least squares (PLS)-based analysis, which was integrated with the Monte Carlo technique. Microarray analysis was performed with a data set of 110 CAD patients and 111 controls obtained from the Gene Expression Omnibus database. A total of 390 dysregulated genes were acquired. Significantly increased representations of dysregulated genes in Gene Ontology items, including transforming growth factor β-activated receptor activity and acyl-CoA oxidase activity, were identified. Network analysis revealed three hub genes with a degree of >10, including ESR1, ITGA4 and ARRB2. The results of the present study provide novel information on the gene expression signatures of CAD patients and offer further theoretical support for future therapeutic study.