Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel...

Area of Science:

• Cardiovascular Genetics
• Bioinformatics
• Molecular Biology

Background:

• Hypertrophic cardiomyopathy (HCM) is a genetic heart disease leading to serious complications like arrhythmia and heart failure.
• Identifying reliable biomarkers is crucial for improving HCM diagnosis, treatment, and prediction.
• Bioinformatics approaches offer powerful tools for dissecting complex genetic diseases like HCM.

Purpose of the Study:

• To identify novel biomarkers for hypertrophic cardiomyopathy (HCM) using bioinformatics analysis.
• To investigate the molecular pathways and key genes associated with HCM development.
• To validate potential biomarkers for clinical relevance in HCM.

Main Methods:

• Differential gene expression analysis was performed on two GEO datasets (GSE36961, GSE180313).
• Weighted gene co-expression network analysis (WGCNA) identified modules correlated with HCM.
• Gene Ontology (GO) and KEGG pathway analyses, LASSO prediction, and cross-dataset validation were employed.

Main Results:

• Key modules positively correlated with HCM were identified in both datasets.
• Intersection analysis revealed 383 relevant genes, leading to the identification of RTN4, COL4A1, and IER3 as key genes.
• Validation confirmed the significant expression of RTN4, COL4A1, and IER3 in HCM.

Conclusions:

• RTN4, COL4A1, and IER3 are identified as potential diagnostic and predictive biomarkers for HCM.
• The study suggests that protein degradation, mechanical stress, and hypoxia are implicated in HCM pathogenesis.
• These findings provide a foundation for developing targeted therapies and improved management strategies for HCM patients.