Network-based quantitative frameworks to identify pleotropic factors that influence for cardiomyopathy progression

Abstract

This paper presents network-based quantitative frameworks to study the complex relationship of cardiomyopathy (CMP) and risk factors that influence CMP progression in order to identify new CMP biomarkers. We analyzed gene expression microarray data from CMP affected and unaffected (control) tissues, and data from individuals with high body fat, high fat diet and type-II diabetes. We examined differentially expressed genes (DEGs) for each dataset and compared CMP with each factor pairwise to identify common DEG overlaps. In our analysis, 2589 DEGs are identified for CMP of which 1283 genes are over expressed and 1306 genes are under expressed. Protein-protein interaction (PPI) network found 10 core genes, namely SMARCA4, NCOR2 and histone genes HIST1H4K, HIST1H4I, HIST2H4B, HIST1H4H, HIST2H4A, HIST4H4, HIST1H4F and HIST1HL. Ontological and pathway analysis with this information identified significant pathways for CMP progression. The findings were validated using dbGaP (gene SNP-disease linkage) and OMIM databases for gold-standard benchmarking of their significance in disease progression. Thus, our network-based method identified a number of factors, notably histones, that may be pleiotropic influencing factors of the CMP.