ArticleBMC cardiovascular disorders2022
Exploration of dilated cardiomyopathy for biomarkers and immune microenvironment: evidence from RNA-seq.
Article in BMC cardiovascular disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Identifying Novel Therapeutic Opportunities for Dilated Cardiomyopathy: A Bioinformatics Approach to Drug Repositioning and Herbal Medicine Prediction.Current pharmaceutical biotechnology · 2026Article
- Comprehensive Plasma Oxylipin Profiling Reveals a Pro-Inflammatory Eicosanoid Signature and Diagnostic Biomarker Panel in Dilated Cardiomyopathy.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- CD4International journal of molecular sciences · 2025Article
- Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy.Journal of inflammation research · 2024Article
- Transcriptomic Comparison of Human Peripartum and Dilated Cardiomyopathy Identifies Differences in Key Disease Pathways.Journal of cardiovascular development and disease · 2023Article
- Identification of key immune-related genes in dilated cardiomyopathy using bioinformatics analysis.Scientific reports · 2023Article
- GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data.PeerJ. Computer science · 2023Article
- Identification of cuproptosis-related biomarkers in dilated cardiomyopathy and potential therapeutic prediction of herbal medicines.Frontiers in molecular biosciences · 2023Article
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7 authors.
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Abstract
backgroundThe pathogenic mechanism of dilated cardiomyopathy (DCM) remains to be defined. This study aimed to identify hub genes and immune cells that could serve as potential therapeutic targets for DCM.
methodsWe downloaded four datasets from the Gene Expression Omnibus (GEO) database: GSE141910, GSE3585, GSE42955 and GSE79962. Weighted gene coexpression network analysis (WGCNA) and differential expression analysis were performed to identify gene panels related to DCM. Meanwhile, the CIBERSORT algorithm was used to estimate the immune cells in DCM tissues. Multiple machine learning approaches were used to screen the hub genes and immune cells. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis. An experimental mouse model of dilated cardiomyopathy was used to validate the bioinformatics results.
resultsFRZB and EXT1 were identified as hub biomarkers, and the ROC curves suggested an excellent diagnostic ability of the above genes for DCM. In addition, naive B cells were upregulated in DCM tissues, while eosinophils, M2 macrophages, and memory CD4 T cells were downregulated in DCM tissues. The increase in two hub genes and naive B cells was validated in animal experiments.
conclusionThese results indicated that FRZB and EXT1 could be used as promising biomarkers, and eosinophils, M2 macrophages, resting memory CD4 T cells and naive B cells may also affect the occurrence of DCM.
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