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Integrated Bioinformatics Analysis Identifies Vascular Endothelial Cell-Related Biomarkers for Hypertrophic Cardiomyopathy
1 Department of Cardiology, Nantong Third People’s Hospital and the Third People’s Hospital Affiliated to Nantong University, Nantong, 226000, China
2 Department of Intensive Care Unit, Tumor Hospital Affiliated to Nantong University, Nantong Tumor Hospital, Nantong, 226300, China
* Corresponding Author: Yong Tao. Email:
Congenital Heart Disease 2024, 19(6), 653-669. https://doi.org/10.32604/chd.2025.060406
Received 31 October 2024; Accepted 16 January 2025; Issue published 27 January 2025
Abstract
Background: Previous studies combined integrated scRNA-seq with bulk RNA data to screen biomarkers for cardiomyopathy. This study extended this approach to identify biomarkers specific for hypertrophic cardiomyopathy (HCM). Methods: Datasets GSE36961, GSE130036, GSE249925 and GSE203274 were analyzed in this study. ScRNA-seq analysis was employed to identify distinct cell populations. Differentially expression analysis was conducted to screen vascular endothelial cells (VECs)-related feature genes. After calculating VECs score, WGCNA was used to correlate gene modules with the VECs score. Key HCM biomarkers were determined using random forest analysis, and LASSO regression analyses to construct a diagnostic model based on their diagnostic efficacy and differential expression. Results: Our analysis identified nine distinct cell populations, with VECs accounting for a notably higher proportion in HCM samples. Genes associated with the VECs were enriched in the pathways related to blood vessel, immunity and cardiac function. After classifying significant gene modules based on VEC-related genes, a strong correlation between the blue module and the VECs score was detected. Notably, genes in the blue module were enriched in the pathways related to metabolism and immune response. Key genes with a high expression in HCM were determined by intersecting differentially expressed genes (DEGs) in HCM with those in the blue module. Finally, random forest analysis and LASSO regression analysis identified five central hub genes for the diagnosis of HCM, including Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1B (DYRK1B), Growth Arrest and DNA Damage Inducible Alpha (GADD45A), Influenza Virus NS1A Binding Protein (IVNS1ABP), Leiomodin 2 (LMOD2), and Pleckstrin Homology Like Domain Family B Member 2 (PHLDB2). Conclusion: Collectively, our study discovered novel VEC-related biomarkers for HCM and comprehensively examined the mechanisms underlying the pathogenesis of HCM.Keywords
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