Machine learning combined with multi-omics analysis: identifying nucleotide metabolism-associated immune genes and validating their functions in cardiomyopathy

作者：Pei Huang, Yanning Huang, Zhenbang Lie · 发表于：BMC Cardiovascular Disorders · 年份：2026 · DOI：10.1186/s12872-026-05855-0 · 研究领域：Cardiac Fibrosis and Remodeling、Cardiomyopathy and Myosin Studies、Cardiovascular Function and Risk Factors

BACKGROUND: Cardiomyopathy (CMP) is a heterogeneous group of myocardial disorders with diverse etiologies, posing a significant threat to patient health and quality of life. Accumulating studies have emphasized the role of nucleotide metabolism in CMP pathogenesis, such as regulating myocardial energy homeostasis and inflammatory responses. However, the association between nucleotide metabolism-associated genes (NMGs) and immune dysregulation in CMP remains unclear, and the diagnostic and therapeutic potential of nucleotide metabolism-associated genes (NMGs) in Cardiomyopathy (CMP) has not been fully explored. This study was designed to detect NMGs linked to CMP, dissect their roles in disease progression (especially immune regulation), and uncover novel diagnostic biomarkers and therapeutic targets. METHODS: We collected RNA sequencing data of CMP from the Gene Expression Omnibus (GEO). Using R, differential expression analysis and weighted gene co-expression network analysis(WGCNA) were carried out, and the resulting data were cross-referenced with a nucleotide metabolism gene set. We employed functional enrichment analysis and the connectivity map (CMap) to identify both differentially expressed genes (DEGs) and potential therapeutic agents.Key immune-associated genes were filtered out using LASSO regression, SVM-RFE, and random forest algorithms. CIBERSORT was utilized to analyze the infiltration patterns and correlation of immune cells, while we conducted in vivo experim...