In silico identification of common and specific signatures in coronary heart diseases

Abstract

Coronary heart disease (CHD) is on the increase in developing countries, where lifestyle choices such as smoking, bad diet, and no exercise contribute and increase the incidence of high blood pressure and high cholesterol levels to cause CHD. Through utilization of a biomarker-based approach for developing interventions, the aim of the study was to identify differentially expressed genes (DEGs) and their association and impact on various bio-targets. The microarray datasets of both healthy and CHD patients were analyzed to identify the DEGs and their interactions using Gene Ontology, PANTHER, Reactome, and STRING (for the possible associated genes with multiple targets). Our data mining approach suggests that the DEGs were associated with molecular functions, including protein binding (75%) and catalytic activity (56%); biological processes such as cellular process (83%), biological regulation (57%), and metabolic process (44%); and cellular components such as cell (65%) and organelle (58%); as well as other associations including apoptosis, inflammatory, cell development and metabolic pathways. The molecular functions were further analyzed, and protein binding in particular was analyzed using network analysis to determine whether there was a clear association with CHD and disease. The ingenuity pathway analysis revealed pathways related to cell cholesterol biosynthesis, the immune system including cytokinin signaling, in which, the understanding of DEGs is crucial to predict the advancement of preventive strategies. Results of the present study showed that, there is a need to validate the top DEGs to rule out their molecular mechanism in heart failure caused by CHD.