ArticleJournal of inflammation research2024
Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy.
Article in Journal of inflammation research, 2024. 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.
- Cardiac fibroblasts in myocardial injury and heart failure.European heart journal · 2026Review
- Machine learning combined with multi-omics analysis: identifying nucleotide metabolism-associated immune genes and validating their functions in cardiomyopathy.BMC cardiovascular disorders · 2026Article
- Comprehensive Analysis of RNA Modifications Related Genes in the Diagnosis and Subtype Classification of Dilated Cardiomyopathy.Journal of inflammation research · 2025Article
- Identification of biomarkers for the diagnosis of chronic kidney disease (CKD) with dilated cardiomyopathy (DCM) by bioinformatics analysis and machine learning.Frontiers in genetics · 2025Article
- Identification of key genes for heart failure in dilated cardiomyopathy in different populations.Frontiers in genetics · 2025Article
- Single cell deciphering of pruritic keloids: the interaction between fibroblasts and Schwann cells through the Midkine signaling.Burns & trauma · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease. Methods: This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models. Results: Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes ( Conclusion: Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes
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