Exploring cuproptosis-related molecular clusters and immunological characterization in ischemic stroke through machine learning.

Objective: Ischemic stroke (IS) is a significant health concern with high disability and fatality rates despite available treatments. Immune cells and cuproptosis are associated with the onset and progression of IS. Investigating the interaction between cuproptosis-related genes (CURGs) and immune cells in IS can provide a theoretical basis for IS treatment. Methods: We obtained IS datasets from the Gene Expression Omnibus (GEO) and employed machine learning to identify CURGs. The diagnostic efficiency of the CURGs was evaluated using receiver operating characteristic (ROC) curves. KEGG and gene set enrichment analysis (GSEA) were also conducted to identify biologically relevant pathways associated with CURGs in IS patients. Single-cell analysis was used to confirm the expression of 19 CURGs, and pathway activity calculations were performed using the AUCell package. Additionally, a risk prediction model for IS patients was developed, and core modules and hub genes related to IS were identified using weighted gene coexpression network analysis (WGCNA). We classified IS patients using a method of consensus clustering. Results: We established a precise diagnostic model for IS. Enrichment analysis revealed major pathways, including oxidative phosphorylation, the NF-kappa B signaling pathway, the apoptosis pathway, and the Wnt signaling pathway. At the single-cell level, compared to those in non-IS samples, 19 CURGs were primarily overexpressed in the immune cells of IS samples and exhibited high activity in natural killer cell-mediated cytotoxicity, steroid hormone biosynthesis, and oxidative phosphorylation. Two clusters were obtained through consensus clustering. Notably, immune cell types including B cells, plasma cells, and resting NK cells, varied between the two clusters. Furthermore, the red module and hub genes associated with IS were uncovered. The expression patterns of CURGs varied over time. Conclusion: This study developed a precise diagnostic model for IS by identifying CURGs and evaluating their interaction with immune cells. Enrichment analyses revealed key pathways involved in IS, and single-cell analysis confirmed CURG overexpression in immune cells. A risk prediction model and core modules associated with IS were also identified.