Integrated network pharmacology and comprehensive bioinformatics analysis to identify the mechanisms and molecular targets of ketamine in ischemic stroke–depression comorbidity

Comorbid depression following ischemic stroke is a debilitating condition with complex pathophysiology. Although ketamine demonstrates rapid antidepressant effects, its molecular mechanisms in the context of this comorbidity are poorly understood. We employed a network pharmacology approach to hypothesize potential molecular targets and pathways of ketamine relevant to both ischemic stroke and depression. Differentially expressed genes for ischemic stroke and major depressive disorder were identified from public Gene Expression Omnibus datasets. A broad set of potential ketamine-associated genes was compiled from the SwissTargetPrediction, Comparative Toxicogenomics Database, and GeneCards databases. The intersection of these gene sets was analyzed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A protein–protein interaction network was constructed, and hub genes were identified. SHAP-based interpretability analysis was performed to rank the relative importance of these hub genes. Further analyses, including gene set enrichment analysis, immune infiltration estimation, and signaling network reconstruction, were conducted to characterize the functional context of key candidates. Molecular docking was performed to probe potential interactions between ketamine and candidate proteins. Our integrative analysis identified 42 intersecting genes, with enrichment in pathways such as lipid and atherosclerosis, interleukin-17 signaling, and cellular response to stimuli. Two genes, IL1RN and DDIT3, emerged as central candidates and were linked to immune regulation, neurotrophin signaling, and ubiquitin-mediated processes in our subsequent analyses. Docking simulations suggested potential binding of ketamine to these proteins. These in silico findings propose that the putative effects of ketamine on comorbid ischemic stroke and depression may involve modulation of multiple pathways, with IL1RN and DDIT3 as potential key contributors. This work provides a hypothesis-generating framework for future experimental and clinical validation.