Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

Background: Heart failure (HF) is a global health burden marked by high morbidity and limited treatment efficacy across subtypes. The lack of reliable molecular biomarkers for heart failure impedes personalized therapy. Emerging evidence suggests that macrophage-trained immunity drives chronic inflammation and cardiac remodeling, highlighting immune-related genes as promising biomarkers. Methods: We integrated transcriptomic data from five independent HF cohorts and one macrophage-trained immunity model. Differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), immune infiltration profiling, and six machine-learning algorithms were applied to screen immune-related candidate genes. Functional relevance was assessed by gene set enrichment analysis (GSEA) and single-cell RNA-seq of human cardiac tissue. Finally, we established a THP-1-derived macrophage trained immunity model to validate the paracrine effects of macrophage Maturin (MTURN) and Piezo-type mechanosensitive ion channel component 1 (PIEZO1) in cardiomyocytes. Results: ., NPPA/B) in AC16 cardiomyocytes. Conclusion: Multi-cohort, single-cell RNA-seq, and experimental data collectively suggest MTURN as a trained immunity-related biomarker for the diagnosis of heart failure with a potential link to PIEZO1-mediated cardiac remodeling.