Dataset Information

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.

ABSTRACT:

Objectives

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

Methods

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

Results

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

Conclusions

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

SUBMITTER: You H

PROVIDER: S-EPMC10683566 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.

You Hongjun H Dong Mengya M

The Journal of international medical research 20231101 11

<h4>Objectives</h4>Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.<h4>Methods</h4>Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene ...[more]

PMID: 38006610

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Dataset Information

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.

Objectives

Methods

Results

Conclusions

Publications

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.

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