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

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.