Identification of Novel Target Genes Linked to Type 2 Diabetes: An in Silico Approach

Abstract

Introduction: Type 2 diabetes is a progressive metabolic disorder that causes morbidity and mortality all around the world. Though several drugs are available to manage diabetes, none of them effectively lower diabetes without causing adverse effects. As a result, finding novel genes associated with diabetes can lead to the development of novel approaches to therapy.

Objectives: The purpose of this study is to find potential genes linked to type 2 diabetes using in silico methods.

Methods: The Gene Expression Omnibus (GEO) analysis approach was employed to find new genes in the current work. GSE12634's expression was taken from the GEO database. The DEGs were identified using the GEO2R on the web application. Both gene ontology (GO) word enrichment analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were performed. The PPI network for differentially expressed genes (DEGs) was established by using the Cytoscape tool to identify significant functional pathways and gene candidates.

Results: GEO2R was used to filter a total of 250 DEGs, of which 65 were upregulated and 185 were downregulated. According to GO analysis, DEGs had significantly higher levels of extracellular matrix organization, extracellular structural organization, extracellular matrix-containing collagen, contractile fiber, and membrane raft. As for the KEGG pathway analysis reveals that DEGs were considerably abundant in the pathways related to diabetic complications such as AGE-RAGE, P13K-AKT, Tyrosine metabolism, Glycolysis/Gluconeogenesis, Glycosaminoglycan degradation, Pyruvate metabolism, and RAPI signaling. Six gene candidates were discovered (MMP2, CXCL2, CDH2, GSN, PPARG, DPP4, and SPP1) based on protein-protein interaction (PPI) network research.

Conclusions: In conclusion, our findings suggest that the genes CIR, CXCL12, CD44, SGCA, AGTR1, and TPM1 may be targets for diabetes condition, as they provide a number of bioinformatics investigations of gene pathways.