What is the clinical evidence from this study?

Study design: Observational. Population: Hypertrophic cardiomyopathy (n=168). Intervention: 12-gene diagnostic signature vs. Healthy controls. Primary outcome: Diagnostic performance of the 12-gene signature.

June 17, 2025Open Access

Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy

FZFeng ZhangAffiliated Hospital of North Sichuan Medical College CLChunrui LiThe People's Hospital of LinXia LZLulu ZhangJiangsu Province Hospital

Key Result

A 12-gene diagnostic signature constructed using machine learning algorithms demonstrated robust predictive performance in distinguishing hypertrophic cardiomyopathy from healthy myocardial tissues.

Study Design

Type

Observational (n=168)

Structured PICO

Does transcriptomic analysis and machine learning modeling identify novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy?

Population

RNA-sequencing datasets (GSE230585, GSE249925, GSE180313) of normal cardiac tissues from healthy donors and myocardial tissues from hypertrophic cardiomyopathy (HCM) patients

Intervention

Bioinformatics analysis and machine learning modeling (113 combinations of 12 algorithms)

Comparator

Normal cardiac tissues from healthy donors

Outcome

Differentially expressed genes (DEGs) and a predictive diagnostic signature for HCMsurrogate

A machine learning-derived 12-gene signature provides a novel diagnostic biomarker profile for hypertrophic cardiomyopathy.

Limitations

Reliance on publicly available datasets restricts analysis to transcriptomic data, precluding validation at the protein and epigenetic levels
Small sample size in the external validation cohort (GSE180313)
Lack of formal overfitting evaluations, such as learning curves and permutation tests
Clinical utility remains untested in real-world settings
Did not account for confounding variables such as medication use or comorbidities

Abstract

Objective: This study aimed to leverage bioinformatics approaches to identify novel biomarkers and characterize the molecular mechanisms underlying hypertrophic cardiomyopathy (HCM). Methods: Two RNA-sequencing datasets (GSE230585 and GSE249925) were obtained from the Gene Expression Omnibus (GEO) repository. Computational analysis was performed to compare transcriptomic profiles between normal cardiac tissues from healthy donors and myocardial tissues from HCM patients. Functional annotation of differentially expressed genes (DEGs) was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Immune cell infiltration patterns were quantified via single-sample gene set enrichment analysis (ssGSEA). A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313). Results: A total of 271 DEGs were identified, primarily enriched in multiple biological pathways. Immune infiltration analysis revealed distinct patterns of immune cell composition. Based on the top differentially expressed genes, a robust 12-gene diagnostic signature (COMP, SFRP4, RASD1, IL1RL1, S100A8, S100A9, ESM1, CA3, MYL1, VGLL2, MCEMP1, and MT1A) was constructed, demonstrating superior performance in both training and testing cohorts. Conclusion: This study utilized bioinformatics approaches to analyze RNA-sequencing datasets, identifying DEGs and distinct immune infiltration patterns in HCM. These findings enabled the construction of a 12-gene diagnostic signature with robust predictive performance, thereby advancing our understanding of HCM's molecular biomarkers and pathogenic mechanisms.

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