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Updated: Jun 21, 2025
Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
Published on: August 8, 2022
Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel
Guanmou Li1, Dongqun Lin2,3,4, Xiaoping Fan2,3,4
1Zhujiang Hospital of Southern Medical University, Guangzhou 510120, Guangdong, China.
Insights
Researchers identified RTN4, COL4A1, and IER3 as key genes for hypertrophic cardiomyopathy (HCM). These potential biomarkers may aid in diagnosing and treating this complex heart condition, potentially linked to protein degradation and hypoxia.
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.
Abstract:
HCM is a heterogeneous monogenic cardiac disease that can lead to arrhythmia, heart failure, and atrial fibrillation. This study aims to identify biomarkers that have a positive impact on the treatment, diagnosis, and prediction of HCM through bioinformatics analysis. We selected the GSE36961 and GSE180313 datasets from the Gene Expression Omnibus (GEO) database for differential analysis. GSE36961 generated 6 modules through weighted gene co-expression network analysis (WGCNA), with the green and grey modules showing the highest positive correlation with HCM (green module: cor = 0.88, p = 2e - 48; grey module: cor = 0.78, p = 4e - 31). GSE180313 generated 17 modules through WGCNA, with the turquoise module exhibiting the highest positive correlation with HCM (turquoise module: cor = 0.92, p = 6e - 09). We conducted GO and KEGG pathway analysis on the intersection genes of the selected modules from GSE36961 and GSE180313 and intersected their GO enriched pathways with the GO enriched pathways of endothelial cell subtypes calculated after clustering single-cell data GSE181764, resulting in 383 genes on the enriched pathways. Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM. The expression levels of these genes were validated in the GSE68316 and GSE32453 datasets. In conclusion, RTN4, COL4A1, and IER3 are potential biomarkers of HCM, and protein degradation, mechanical stress, and hypoxia may be associated with the occurrence and development of HCM.
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