Identification of potential therapeutic targets for stroke using data mining, network analysis, enrichment, and docking analysis

Abstract

Stroke is a leading cause of disability and death worldwide. In this study, we identified potential therapeutic targets for stroke using a data mining, network analysis, enrichment, and docking analysis approach. We first identified 1991 genes associated with stroke from two publicly available databases: GeneCards and DisGeNET. We then constructed a protein-protein interaction (PPI) network using the STRING database and identified 1301 nodes and 5413 edges. We used Metascape to perform GO enrichment analysis and KEGG pathway enrichment analysis. The results of these analyses identified ten hub genes (TNF, IL6, ACTB, AKT1, IL1B, TP53, VEGFA, STAT3, CASP3, and CTNNB1) and five KEGG pathways (cancer, lipid and atherosclerosis, cytokine-cytokine receptor interaction, AGE RAGE signaling pathway in complications, and TNF signaling pathway) that are enriched in stroke genes. We then performed molecular docking analysis to screen potential drug candidates for these targets. The results of this analysis identified several promising drug candidates that could be used to develop new therapeutic strategies for stroke.

Keywords: KEGG pathway; Stroke; data mining; drug discovery; molecular docking; network pharmacology; protein-protein interaction.