ArticleFrontiers in cardiovascular medicine2022
Exploring Key Genes to Construct a Diagnosis Model of Dilated Cardiomyopathy.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Bioinformatics-based screening of genes associated with dilated cardiomyopathy.Journal of thoracic disease · 2025Article
- Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.ESC heart failure · 2024Article
- Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy.Journal of thoracic disease · 2023Article
- Dysregulation of Iron Metabolism-Linked Genes at Myocardial Tissue and Cell Levels in Dilated Cardiomyopathy.International journal of molecular sciences · 2023Article
- Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis.Frontiers in immunology · 2023Article
- Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure.Frontiers in cell and developmental biology · 2022Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dilated cardiomyopathy (DCM) is characterized by left ventricular dilatation and systolic dysfunction. The pathogenesis and etiologies of DCM remain elusive. This study aims to identify the key genes to construct a genetic diagnosis model of DCM. Methods: A total of 257 DCM samples from five independent cohorts were enrolled. The Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify the key modules associated with DCM. The latent mechanisms and protein-protein interaction network underlying the key modules were further revealed. Subsequently, we developed and validated a LASSO diagnostic model in five independent cohorts. Results: Two key modules were identified using WGCNA. Novel mechanisms related to the extracellular, mitochondrial matrix or IL-17 signaling pathway were pinpointed, which might significantly influence DCM. Besides, 23 key genes were screened out by combining WGCNA and differential expression analysis. Based on the key genes, a genetic diagnosis model was constructed and validated using five cohorts with excellent AUCs (0.975, 0.954, 0.722, 0.850, 0.988). Finally, significant differences in immune infiltration were observed between the two groups divided by the diagnostic model. Conclusion: Our study revealed several novel pathways and key genes to provide potential targets and biomarkers for DCM treatment. A key genes' diagnosis model was built to offer a new tool for diagnosing DCM.
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