In-depth investigation of the complex pathophysiological mechanisms between diabetes and ischemic stroke through gene expression and regulatory network analysis

•Gene expression analysis identifies key biomarkers linking diabetes and ischemic stroke (IS).•DEG analysis reveals 307 upregulated and 156 downregulated genes in both diabetes and IS datasets.•Enrichment analysis highlights immune response and inflammation pathways in diabetes and IS comorbidity.•PPI network reveals hub genes TLR2, TLR4, HDAC1, and ITGAM central to immune and inflammatory pathways.•Transcription factor analysis identifies RELA, SPI1, STAT3, and SP1 as potential therapeutic targets. This study explores the intricate relationship between diabetes and ischemic stroke (IS) through gene expression analysis and regulatory network investigation to identify potential biomarkers and therapeutic targets. Using datasets from the Gene Expression Omnibus (GEO) database, differential gene analysis was conducted on GSE43950 (diabetes) and GSE16561 (IS), revealing overlapping differentially expressed genes (DEGs). Functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, and hub gene identification were performed, followed by validation in independent datasets (GSE156035 and GSE58294). The analysis identified 307 upregulated and 156 downregulated overlapping DEGs with significant enrichment in GO and KEGG pathways. Key hub genes (TLR2, TLR4, HDAC1, ITGAM) were identified through a PPI network (257 nodes, 456 interactions), with their roles in immune and inflammatory responses highlighted through GeneMANIA analysis. TRRUST-based transcription factor enrichment analysis revealed regulatory links involving RELA, SPI1, STAT3, and SP1. Differential expression analysis confirmed that RELA and SPI1 were upregulated in diabetes, while SPI1, STAT3, and SP1 were linked to IS. These transcription factors are involved in regulating immunity and inflammation, providing insights into the molecular mechanisms underlying diabetes-IS comorbidity. This bioinformatics-driven approach offers new understanding of the gene interactions and pathways involved, paving the way for potential therapeutic targets.