Network-Centric Identification of Disease Co-Occurrences: A Systems Biology Approach

Abstract

Complex diseases that occur by perturbations of molecular pathways and genetic factors result in pathophysiology of diseases. Network-centric systems biology approaches play an important role in understanding disease complexity. Diabetes, cardiovascular disease and depression are such complex diseases that have been reported to be comorbid in various epidemiological studies but there are no reports of the genetic and underlying factors which may be responsible for their reported co-occurrences. The present study was undertaken to investigate the molecular factors responsible for co-occurrence of diabetes, depression and cardiovascular disease using in-silico network systems biology approach. Genes common amongst these three diseases were retrieved from DisGeNET, a database of human diseases and their interactions were retrieved from STRING database. The resulting network containing 99 nodes (which represent genes) and 1252 edges (which represent various interactions between nodes) was analyzed using Cytoscape v: 3.7.2 and its various plug-ins i.e. ClusterONE, Cytohubba, ClueGO and Cluepedia. The hub genes identified in the present study namely IL1B, VEGFA, LEP, CAT, CXCL8, PLG, IL6, IL10, PTGS2, TLR4 and AKT1 were found to be enriched in various metabolic pathways and several mechanisms such as inflammation. These genes and their protein products may act as potential biomarkers for early detection of predisposition to diseases and potential therapeutic targets based on the common molecular underpinnings of co-occurrence of diabetes, depression and cardiovascular disease.