A Machine Learning Approach to Gene Expression in Hypertrophic Cardiomyopathy.

BACKGROUND/OBJECTIVES: Hypertrophic cardiomyopathy (HCM) is a common heart disorder characterized by the thickening of the heart muscle, particularly in the left ventricle, which increases the risk of cardiac complications. This study aims to analyze the expression of apoptosis-regulating genes (, , , , and ) in blood samples from HCM patients, to better understand their potential as biomarkers for disease progression.

METHODS

Quantitative real-time PCR (qPCR) was used to evaluate gene expression in blood samples from 93 HCM patients. The correlation between apoptosis-regulating genes was conducted and clinical parameters were integrated for feature importance and clustering analysis.

RESULTS

Most patients exhibited significant downregulation of , , and . In contrast, BAX expression was elevated in 71 out of 93 patients, while was increased in 55 out of 93 patients. Correlation analysis revealed weak negative correlations between the / ratio and gene expression.

CONCLUSIONS

These findings suggest that reduced expression of apoptotic genes may indicate a protective cellular mechanism, which could serve as a biomarker for disease progression. Further studies are needed to investigate the potential for therapeutic modulation of these pathways to improve patient outcomes.