Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy

Background Hypertrophic cardiomyopathy (HCM) is a common inherited heart disease frequently leading to heart failure. Although sarcomeric gene mutations are known, they only account for a subset of cases. The role of immune dysregulation in HCM progression has gained increasing attention, necessitating the exploration of immune-related biomarkers and therapeutic targets. This study integrates Mendelian randomization (MR), transcriptomics, machine learning, and experimental validation to investigate the immune mechanisms underlying HCM. Methods We analyzed three transcriptomic datasets from the GEO database (210 healthy controls, 152 HCM patients) and identified differentially expressed genes (DEGs) using the R package limma. MR analysis was performed on 19,942 expression quantitative trait loci (eQTLs) and HCM cases using the TwoSampleMR package. Machine learning (10 algorithms) was employed to construct diagnostic models, and SHAP analysis was applied to assess key gene contributions. Functional enrichment was performed with clusterProfiler, diagnostic performance was evaluated via ROC curves, and immune cell infiltration was analyzed using CIBERSORT. A competing endogenous RNA (ceRNA) network was constructed, and drug targets were predicted via the DGIdb database. Key gene expression was validated by qPCR. Results We identified 472 DEGs and 205 HCM-associated loci, narrowing down to seven key genes: RNF165, SNCA, SRGN, MARCO, STEAP4, SIGLEC9, and TKT. These genes were enriched in immune-related pathways (e.g., cytokine activity, leukocyte migration, JAK-STAT signaling). The Random Forest model exhibited the highest diagnostic performance (AUC: 0.939), with SHAP analysis revealing MARCO as the top contributor. Gene expression was associated with immune cell infiltration: HCM samples showed increased CD4+ T cells and M0 macrophages but decreased M2 macrophages and neutrophils. The ceRNA network comprised 5 mRNAs, 40 miRNAs, and 152 lncRNAs. SRGN and SNCA were identified as potential targets for heparin and 33 other drugs, respectively. qRT-PCR performed on a small number of myocardial samples supported expression trends of the identified genes, in line with transcriptomic analysis. Conclusion This study reveals immune-related mechanistic biomarkers and potential therapeutic targets in HCM, highlighting the role of immune dysregulation in disease progression. Machine learning and SHAP analysis improved diagnostic model interpretability, providing a basis for future development of non-invasive diagnostic tools.