Discovery and characterization of functional modules and pathogenic genes associated with the risk of coronary artery disease

Abstract

Coronary artery disease (CAD) involves a complex interplay between multiple pathogenic genes that leads to a complex pathogenesis; its diagnosis and treatment remain significantly challenging. Here, we developed an integrated network biology approach to identify disease risk functional modules and pathogenic genes associated with CAD risk. First, we selected 72 known disease genes from the OMIM, GAD, and DO databases as an initial set of seed genes. We retrieved PPI data from HPRD to expand this gene set into a CAD-PPI gene network based on direct interactions and then performed topology analysis for this CAD-PPI gene network. Second, we utilized an MCL algorithm to identify 49 susceptible modules with high modularity. Third, we used functional consistency analysis to further identify 23 risk functional modules. Finally, according to existing cascades of known disease genes in KEGG pathways, we identified 82 pathogenic genes that are either directly or indirectly associated with CAD risk. Based on previous reports, 37 of our identified genes are involved in the development of CAD, whereas the other 45 genes remain to be associated with CAD by experimental evidence. Taken together, our results will provide a better understanding of CAD pathogenesis as well as new insights into its prognosis and treatment.