ArticleFrontiers in cardiovascular medicine2023
Alterated gene expression in dilated cardiomyopathy after left ventricular assist device support by bioinformatics analysis.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- LVAD Therapy Improves Metabolic and Inflammatory State in the Heart.JACC. Basic to translational science · 2026Article
- DNA methylation predicts adverse outcomes of coronary artery disease.Nature communications · 2025Article
- Senescence-related Genes as Prognostic Markers for STEMI Patients: LASSO Regression-Based Bioinformatics and External Validation.Journal of cardiovascular translational research · 2025Article
- Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.Life (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
Introduction: Heart transplantation is the best treatment for end-stage dilated cardiomyopathy (DCM). Left ventricular assist device (LVAD) support is becoming more prevalent and may delay heart transplantation. Gene expression of the left ventricular myocardium usually changes following LVAD implantation. In this study, we aimed to identify potential biomarkers to determine the prognosis of patients with DCM after receiving LVAD support. Methods: We extracted microarray datasets from Gene Expression Omnibus (GEO), including GSE430 and GSE21610. There were 28 paired DCM samples in the GSE430 and GSE21610 profiles. Differentially expressed genes (DEGs) were identified at LVAD implantation and heart transplantation. DEGs were annotated according to Gene Ontology (GO) and analyzed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed. The top 10 crucial genes were predicted using Cytoscape plugin CytoHubba in conformity with the network degree algorithm. The levels of gene expression and the diagnostic values of crucial genes were confirmed in the clinical datasets. Results: The 28 DEGs were clustered into the GSE datasets. GO annotations and KEGG pathway enrichment analyses revealed that inflammation might be involved. They were associated with correlative inflammation. Combined with PPI networks, these results revealed CytoHubba's top 10 hub genes, including Conclusion:
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