Investigating Gene and Pathway Association in Type 2 Diabetes, Obesity, and Diabetic Nephropathy: A Network Systems Biology Approach

OBJECTIVE: The present study aims to identify underlying molecular factors and mechanisms responsible for type 2 diabetes (T2D) to unravel its molecular association with obesity and diabetic nephropathy (DN) using an in silico network systems biology-based integrative approach. MATERIALS AND METHODS: Microarray datasets for T2D, obesity, and DN were retrieved from the Gene Expression Omnibus (GEO) database through GEO query package of R programming language followed by the identification of common differentially expressed genes (DEGs) in the 3 diseases. A protein-protein interaction (PPI) network was constructed using STRING, and the network topology was analyzed using Cytoscape plugins, CytoHubba and CytoCluster, followed by gene set enrichment analysis using Enrichr-KG. RESULTS: The microarray datasets with accession numbers GSE20966, GSE9624, and GSE1009 for T2D, obesity, and DN, respectively, were pre-processed followed by identification of up-regulated and downregulated genes. These DEGs resulted in identification of 13 common DEGs amongst the diseases. The PPI network constructed using STRING contained 93 nodes and 866 edges followed by identification of 4 hub genes namely AKR1C3, CYP19A1, AKR1D1, and HSD17B3 using Cytoscape plug-ins, CytoHubba and CytoCluster. These 4 hub genes were found to be predominantly involved in steroid hormone biosynthesis pathway. CONCLUSIONS: This study reveals that steroid hormones exert a substantial influence on metabolic pathways and play a crucial role in the onset and progression of metabolic disorder such as T2D and its comorbidites. Identification of the molecular factors and mechanisms underlying complex diseases can aid in the design of therapeutic interventions targeting comorbidities.