ArticleFrontiers in cell and developmental biology2022
Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure.
Article in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 30 citations in OpenAlex.
- Experimental Validation of an Immune Cell Infiltration Signature in Psoriasis: Translating Computational Modeling to In Vivo Efficacy.Journal of clinical laboratory analysis · 2026Article
- MYOM1 deficiency leads to dilated cardiomyopathy by disrupting the homeostasis of sarcoplasmic reticulum.Redox biology · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Integrating co-expression network analysis and machine learning to reveal the regulatory landscape ofPeerJ · 2026Article
- BMPR2 affects valve development via ECM-receptor interaction in zebrafish.Frontiers in cell and developmental biology · 2026Article
- Identification of Key Biomarkers of Growth-Related Traits in the Bay Scallop Argopecten irradians irradians via Multi-omics Analysis Strategies.Marine biotechnology (New York, N.Y.) · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches.Frontiers in neuroscience · 2025Article
- Bioinformatics analysis of FCER1A as a key immune marker in dilated cardiomyopathy and systemic lupus erythematosus.American journal of clinical and experimental immunology · 2025Article
- Identification of key genes for heart failure in dilated cardiomyopathy in different populations.Frontiers in genetics · 2025Article
- Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
- Decoding cardiac metabolic reprogramming through single-cell multi-omics: from mechanisms to therapeutic applications.Frontiers in cell and developmental biology · 2025Review
- Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.ESC heart failure · 2024Article
- Protein glycosylation in cardiovascular health and disease.Nature reviews. Cardiology · 2024Review
- Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.Biology · 2024Article
- Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis.Molecular biotechnology · 2024Article
- Modular Hub Genes in DNA Microarray Suggest Potential Signaling Pathway Interconnectivity in Various Glioma Grades.Biology · 2024Article
- Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy.Journal of inflammation research · 2024Article
- DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.Frontiers in cardiovascular medicine · 2024Article
- Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.Clinical, cosmetic and investigational dermatology · 2024Article
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3 authors at 1 institution in 1 country.
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Abstract
The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.
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