Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke

Tianzhi Li Xiaojia Kang Sijie Zhang Yihan Wang Jinshan He Hongyan Li ^* Chen Shao ^* Jingsong Kang ^*

• Jilin University, Changchun, China

Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes associated with IS and explore their roles in disease mechanisms for new diagnostic and therapeutic strategies.IS gene expression data were sourced from the GEO database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted in R, intersecting results with nucleotide metabolism-related genes. Functional enrichment and connectivity map (cMAP) analyses identified key genes and potential therapeutic agents. Core immune-related genes were determined using LASSO regression, SVM-RFE, and Random Forest algorithms. Immune cell infiltration levels and correlations were analyzed via CIBERSORT. Single-cell RNA sequencing (scRNA-seq) data and molecular docking assessed gene expression, localization, and gene-drug binding. In vivo experiments validated core gene expression.Thirty-three candidate genes were identified, mainly involved in immune and inflammatory responses. CFL1, HMCES, and GIMAP1 emerged as key immune-related genes, linked to immune cell infiltration and showing high diagnostic potential. cMAP analysis indicated these genes as drug targets. scRNA-seq clarified their expression and localization, and molecular docking confirmed strong drug binding. In vivo experiments validated their significant expression in IS.This study underscores the role of nucleotide metabolism in IS, identifying CFL1, HMCES, and GIMAP1 as potential biomarkers and therapeutic targets, providing insights for IS diagnosis and therapy development.