Interactome Based Computational Approach to Identify Association Between Cardiomyopathy and Cardiovascular Disease

Cardiovascular diseases (CVD), including heart failure (HF), represent a major global health concern, with significant indisposition and mortality rates. Cardiomyopathy is a myocardial disease that impedes the heart's ability to efficiently pump blood throughout the body, ultimately leading to heart failure. For the identification of critical genes and proteins linking different diseases, computational biology tools and "omics"play a significant role. Therefore, the present study was undertaken to identify underlying molecular factors responsible for cardiomyopathy to decipher its molecular association with CVD and heart failure using an integrative network system biology approach. Microarray and RNA-seq datasets for cardiomyopathy were retrieved from the Gene Expression Omnibus database and 51 common DEGs were identified. Subsequently, a Protein-Protein Interaction (PPI) network was constructed using STRING, followed by its analysis using various Cytoscape plug-ins. Nine hub genes, namely LPA, APOA2, ABCA1, LCAT, APOB, APOA4, CLU, APOC3, and APOA1 were identified that were found to be involved in cholesterol metabolism, fat digestion and absorption, lipid metabolism, and atherosclerosis pathways. Therefore, the proteins identified in the present study belonging to the APO family and their associated proteins may prove as useful biomarkers for cardiomyopathy and therapeutic targets to prevent CVD and heart failure.