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

ABSTRACT:

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 express

SUBMITTER: Qin R 

PROVIDER: S-EPMC11408831 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature