Transcriptome-Wide Identification of Hub Genes and Therapeutic Targets in Type 2 Diabetes Mellitus Through Integrated Bioinformatics and Molecular Docking-Based Drug Repurposing
Abstract
Abstract This study identified GLP1R as the strongest therapeutic target for T2DM through an integrated bioinformatics and molecular docking-based drug repurposing approach. Three independent microarray datasets of human pancreatic islet tissue (GSE20966, GSE25724, GSE41762) from the NCBI Gene Expression Omnibus were analysed to identify differentially expressed genes (DEGs) using GEO2R, and overlapping DEGs across datasets were determined by Venn diagram analysis. Functional enrichment analysis using Gene Ontology and KEGG pathway annotations revealed calcium signalling, insulin biosynthesis, and endoplasmic reticulum stress as dominant biological themes among the 27 curated DEGs. A protein-protein interaction (PPI) network was constructed using the STRING database and analysed with multiple topological algorithms (Maximal Clique Centrality, Degree, Edge Percolated Component, and Betweenness Centrality), identifying five convergent hub genes, INS, CPE, CALM3, CALM2, and GLP1R, all confirmed as expressed in pancreatic tissue using the Genotype-Tissue Expression (GTEx) database. Regulatory network analysis identified the transcription factors NEUROD1, PDX1, and MAFA, and the microRNAs hsa-miR-1 and hsa-miR-29a, as master regulatory drivers of hub gene expression. Druggability assessment using DrugBank and ChEMBL confirmed all five hub genes as viable drug targets. Molecular docking of 25 FDA-approved antidiabetic compounds against GLP1R showed that Glimepiride (−11.2…
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