Integrative bioinformatics and machine learning approaches identify inflammation-related genes and drug candidates for future preclinical validation in ischemic stroke.

BACKGROUND: Inflammation plays a critical role in ischemic stroke (IS). This study aimed to identify inflammation-related genes and explore potential pharmacological agents for future preclinical validation in IS. METHODS: Transcriptome data were integrated to identify inflammation-related genes, which were functionally characterized and evaluated for diagnostic potential, with single-cell analysis and computational drug prediction. RESULTS: Four inflammation-related genes, C-C chemokine receptor type 7 (CCR7), CD7, CD96, and interleukin-7 receptor (IL-7R), were identified from integrated transcriptome analyses. These genes showed promising diagnostic potential (area under the curve (AUC) > 0.8) and were functionally associated with cytokine signaling, immune interactions, and calcium homeostasis. Drug-gene interaction and molecular docking analyses indicated that capecitabine and ruxolitinib are potential candidates for modulating CD96 and IL-7R. CONCLUSION: This study reveals four inflammation-related genes with preliminary diagnostic value and proposes capecitabine and ruxolitinib as candidate drugs for future preclinical research on IS.