A Machine Learning Approach to Gene Expression in Hypertrophic Cardiomyopathy.

Area of Science:

• Cardiovascular Genetics
• Molecular Biology
• Biomarker Discovery

Background:

• Hypertrophic cardiomyopathy (HCM) is a prevalent cardiac condition marked by left ventricular thickening, elevating risks of cardiac complications.
• Understanding the molecular mechanisms, specifically apoptosis regulation, is crucial for predicting HCM progression.

Purpose of the Study:

• To investigate the expression patterns of key apoptosis-regulating genes (CASP8, CASP9, CASP3, BAX, BCL2) in blood samples from HCM patients.
• To explore the potential of these genes as biomarkers for monitoring HCM disease progression.

Main Methods:

• Quantitative real-time PCR (qPCR) was employed to measure gene expression in 93 HCM patients.
• Correlation analyses were performed on apoptosis-regulating genes, integrating clinical parameters for feature importance and clustering.

Main Results:

• Significant downregulation of CASP8, CASP9, and CASP3 was observed in most HCM patients.
• BAX expression was elevated in 71% of patients, and BCL2 in 55%, with weak negative correlations between the BAX/BCL2 ratio and CASP gene expression.

Conclusions:

• Reduced expression of specific apoptotic genes may represent a protective cellular response in HCM.
• These gene expression profiles hold potential as biomarkers for HCM progression, warranting further investigation for therapeutic strategies.