Identification of PANoptosis-Related Biomarkers in Hypertrophic Cardiomyopathy: Insights from Multi-Omics Analysis

Jinlong Zhong,1 Qinghui Zhao,2 Ruiqing Wu,3 Shuguang Zhang,4 Guoyi Liu,5 Zhihui Zhang,6 Xia Han,7 Lin Shi1,8 1Department of Pathology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 2Department of Medical Engineering, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 3Department of Cardiology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 4Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot, People’s Republic of China; 5Public Security Sub-Bureau of Xincheng District, Hohhot, People’s Republic of China; 6Central Laboratory, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 7Stem Cell Laboratory, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 8Inner Mongolia Medical University, Hohhot, People’s Republic of ChinaCorrespondence: Lin Shi, Email [email protected]: Hypertrophic cardiomyopathy (HCM) is a common inherited cardiomyopathy characterized by ventricular hypertrophy, fibrosis, and increased risk of sudden cardiac death. However, the underlying molecular pathways contributing to its progression remain incompletely defined. PANoptosis, a newly defined inflammatory form of programmed cell death integrating pyroptosis, apoptosis, and necroptosis, has been implicated in cardiac injury and may represent a convergent mechanism linking inflammation and myocardial remodeling, but remains uninvestigated in HCM.Methods: Transcriptomic profiles from HCM and control hearts were analyzed to identify differentially expressed PANoptosis-related genes. A nine-gene diagnostic panel was constructed using a comprehensive multi-algorithm machine learning framework integrating ensemble, kernel-based, and regularized regression models, and validated in external cohorts. Molecular subtypes were identified through consensus clustering. Immune infiltration, functional enrichment, and ceRNA regulatory networks were evaluated. Single-nucleus RNA sequencing localized gene expression to specific cardiac cell types. Cell–cell communication analysis explored intercellular signaling. Experimental validation was performed in a murine HCM model using echocardiography, histology, and RT-qPCR. Molecular docking assessed therapeutic potential of candidate compounds. Finally, molecular docking and target prediction were applied to explore potential therapeutic compounds acting on the PANoptosis axis.Results: Nine PANoptosis-related genes (S100A9, GADD45A, IER3, STAT3, SFRP1, PHLDA1, JAK2, MYC, S100A8) showed high diagnostic performance (AUC > 0.95). Two molecular subtypes displayed distinct immune and metabolic signatures. PANoptosis genes correlated with T cells, macrophages, and dendritic cells. CellChat analysis revealed PDGF-mediated signaling between cardiomyocytes and fibroblasts. Key genes exhibited cell-type–specific expression. In vivo validation confirmed gene expression trends. Moreover, folic acid and tretinoin exhibited favorable docking affinity with core targets, suggesting potential therapeutic relevance.Conclusion: This study provides the first systematic evidence linking PANoptosis to the molecular pathogenesis of HCM. PANoptosis contributes to HCM pathogenesis and immune remodeling, and the identified biomarkers demonstrate translational potential as diagnostic indicators and therapeutic targets. The integrated analysis highlights novel PANoptotic signaling axes that may guide future precision diagnosis and intervention strategies for HCM. Keywords: hypertrophic cardiomyopathy, PANoptosis, diagnostic biomarkers, machine learning