Integrative Transcriptomics and Machine Learning Identify Macrophage-Associated Biomarkers in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease, with macrophages playing a critical role in its pathological remodeling. Our study aims to investigate the molecular basis of HCM by analyzing macrophage-related gene expression at the single-cell level. Utilizing published scRNA-seq datasets (GSE181764 and GSE161921), we identified macrophages as the key cell cluster most associated with HCM. Integration with bulk RNA-seq data (GSE249925) and differential expression analysis revealed three hub genes: ASPN (asporin), F13A1 (Coagulation Factor XIII A Chain), and SORBS2 (Sorbin and SH3 domain-containing protein 2). Immune infiltration analysis showed significant decreases in multiple immune cell subsets in HCM patients, including neutrophil and macrophages. Intercellular communication analysis revealed an approximately 50% reduction in total interactions in HCM, accompanied by markedly weakened macrophage signaling reception and loss of regulatory pathways. Single-cell validation confirmed that F13A1 expression was predominantly restricted to macrophage clusters and significantly downregulated in HCM macrophages, demonstrating strong macrophage specificity and diagnostic potential. Furthermore, a LASSO-based diagnostic model incorporating three genes (IGFBP4, FOS, CTSC) exhibited high predictive performance, with validated accuracy in both training and external validation sets. Collectively, our findings shed light on the mechanisms underlying macrophage dysfunction in HCM and offer novel insights into the cellular and molecular dynamics.