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<p>Hypertrophic cardiomyopathy (HCM) is a complex genetic disorder characterized by left ventricular hypertrophy and impaired cardiac function. Although progress has been made in understanding its genetic basis, the discovery of novel biomarker candidates and therapeutic targets remains crucial for improving diagnosis and treatment. Recent studies have demonstrated that copper metabolism plays an important role in various diseases, suggesting that copper-related genes (CRGs) may be of significant importance in HCM. In this study, we analyzed bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data from healthy controls (HC) and HCM patients. Differentially expressed genes (DEGs) were identified through differential expression analysis, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify associated pathways. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify gene modules related to the HCM phenotype, and a diagnostic model was constructed based on these modules. Additionally, cell type-specific expression patterns were explored through single-cell analysis, and Gene Set Enrichment Analysis (GSEA) and MR analyses were conducted to evaluate the causal relationships between genes and HCM risk. The study found that DEGs associated with HCM were significantly enriched in pathways related to immune responses. WGCNA identified gene modules highly correlated with HCM, among which the blue module exhibited the strongest correlation with HCM. The diagnostic model constructed based on DEGs and WGCNA module genes demonstrated good diagnostic performance, with FCN3, TIPARP, and PROM1 emerging as potential diagnostic biomarker candidates for HCM. Additionally, single-cell analysis revealed the expression characteristics of different cell types in HCM, and causal relationships between key genes and HCM risk were confirmed through GSEA and MR analyses. This study identified FCN3, TIPARP, and PROM1 as cuproptosis-associated biomarker candidates that showed reproducible expression patterns across cohorts and experimental validation. These findings are associative and hypothesis-generating; they suggest potential diagnostic utility that warrants prospective clinical evaluation and mechanistic studies (e.g., perturbation of copper homeostasis and gene manipulation) before any therapeutic inference can be made.</p>

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Identification of Cuproptosis-Associated Biomarker Candidates in Hypertrophic Cardiomyopathy via Machine Learning and Multi-Omics Integration

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Weidong Wang,
  • Ye Tian,
  • Chunyu Wang,
  • Xiuhua Yang

摘要

Hypertrophic cardiomyopathy (HCM) is a complex genetic disorder characterized by left ventricular hypertrophy and impaired cardiac function. Although progress has been made in understanding its genetic basis, the discovery of novel biomarker candidates and therapeutic targets remains crucial for improving diagnosis and treatment. Recent studies have demonstrated that copper metabolism plays an important role in various diseases, suggesting that copper-related genes (CRGs) may be of significant importance in HCM. In this study, we analyzed bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data from healthy controls (HC) and HCM patients. Differentially expressed genes (DEGs) were identified through differential expression analysis, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify associated pathways. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify gene modules related to the HCM phenotype, and a diagnostic model was constructed based on these modules. Additionally, cell type-specific expression patterns were explored through single-cell analysis, and Gene Set Enrichment Analysis (GSEA) and MR analyses were conducted to evaluate the causal relationships between genes and HCM risk. The study found that DEGs associated with HCM were significantly enriched in pathways related to immune responses. WGCNA identified gene modules highly correlated with HCM, among which the blue module exhibited the strongest correlation with HCM. The diagnostic model constructed based on DEGs and WGCNA module genes demonstrated good diagnostic performance, with FCN3, TIPARP, and PROM1 emerging as potential diagnostic biomarker candidates for HCM. Additionally, single-cell analysis revealed the expression characteristics of different cell types in HCM, and causal relationships between key genes and HCM risk were confirmed through GSEA and MR analyses. This study identified FCN3, TIPARP, and PROM1 as cuproptosis-associated biomarker candidates that showed reproducible expression patterns across cohorts and experimental validation. These findings are associative and hypothesis-generating; they suggest potential diagnostic utility that warrants prospective clinical evaluation and mechanistic studies (e.g., perturbation of copper homeostasis and gene manipulation) before any therapeutic inference can be made.