Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates

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 GS...

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Main Authors: Guanmou Li, Dongqun Lin, Xiaoping Fan, Bo Peng
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Cardiology Research and Practice
Online Access:http://dx.doi.org/10.1155/2024/4639334
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author Guanmou Li
Dongqun Lin
Xiaoping Fan
Bo Peng
author_facet Guanmou Li
Dongqun Lin
Xiaoping Fan
Bo Peng
author_sort Guanmou Li
collection DOAJ
description 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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spelling doaj-art-0fcea4ab474d4c1e9a9b395e77fc89ce2025-02-03T01:30:21ZengWileyCardiology Research and Practice2090-05972024-01-01202410.1155/2024/4639334Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic CandidatesGuanmou Li0Dongqun Lin1Xiaoping Fan2Bo Peng3Zhujiang Hospital of Southern Medical UniversityDepartment of Cardiovascular SurgeryDepartment of Cardiovascular SurgeryDepartment of Cardiovascular SurgeryHCM 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.http://dx.doi.org/10.1155/2024/4639334
spellingShingle Guanmou Li
Dongqun Lin
Xiaoping Fan
Bo Peng
Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
Cardiology Research and Practice
title Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
title_full Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
title_fullStr Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
title_full_unstemmed Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
title_short Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates
title_sort exploring hypertrophic cardiomyopathy biomarkers through integrated bioinformatics analysis uncovering novel diagnostic candidates
url http://dx.doi.org/10.1155/2024/4639334
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AT dongqunlin exploringhypertrophiccardiomyopathybiomarkersthroughintegratedbioinformaticsanalysisuncoveringnoveldiagnosticcandidates
AT xiaopingfan exploringhypertrophiccardiomyopathybiomarkersthroughintegratedbioinformaticsanalysisuncoveringnoveldiagnosticcandidates
AT bopeng exploringhypertrophiccardiomyopathybiomarkersthroughintegratedbioinformaticsanalysisuncoveringnoveldiagnosticcandidates