Connecting islet-specific hub genes and pathways in type 2 diabetes mellitus through the bioinformatics lens

• Published in: Human gene (Amsterdam, Netherlands) Vol. 36; p. 201177
• Main Authors: Yadav, Saurabh Singh, Roham, Pratiksha H., Roy, Sujata, Sharma, Shilpy
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2023
• Subjects: Differential gene expression; GEO2R; Hub genes; Microarray datasets; Pancreatic islets; GEO; GEO2R; Hub genes; Pancreatic islets; Differential gene expression; Microarray datasets
• ISSN: 2773-0441; 2773-0441
• DOI: 10.1016/j.humgen.2023.201177

Type 2 diabetes mellitus (T2DM) is a non-communicable, metabolic disorder in which the loss of function and/or mass of pancreatic β-cells occurs. The incidence of T2DM is increasing worldwide and needs more efficient biomarkers for diagnosis along with better therapeutic interventions with fewer side effects. Transcriptomics analysis provides a complete overview of the molecular mechanisms involved at the system level to identify the set of genes that have stayed unnoticed so far for complex diseases, like T2DM. This study has been conducted to identify novel hub gene candidates for T2DM. Five human GEO datasets from pancreatic islets of normal healthy volunteers and T2DM subjects, namely, GSE118139, GSE20966, GSE25724, GSE38642 and GSE41762 were chosen and the differentially expressed genes were shortlisted. The overlapping hub genes between these datasets were identified, which included ERBB2, HGF, MET, MMP1, PDGFRA and PLAU as downregulated genes; and ABCC8, G6PC2, IAPP, PCSK1 and SLC2A2 as up-regulated genes in the pancreatic islets from T2DM subjects. These hub genes could be developed as a panel for diagnosis/prognosis of T2DM in the population and can be targeted for drug development for more effective management of T2DM in the future.