ArticleGenetics research2022
Bioinformatics Analysis of Competing Endogenous RNA Network and Immune Infiltration in Atrial Fibrillation.
Article in Genetics research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Mendelian randomization and single-cell transcriptomics prioritize CD7 and LAPTM5 in primary Sjögren's disease: an integrative multi-omics analysis.Clinical rheumatology · 2026Article
- Epigenetics in Atrial Fibrillation: Molecular Mechanisms and Therapeutic Avenues.Reviews in cardiovascular medicine · 2026Review
- Article
- Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation.PloS one · 2025Article
- Construction and integrated analysis of the ceRNA network hsa_circ_0000672/miR-516a-5p/TRAF6 and its potential function in atrial fibrillation.Scientific reports · 2023Article
- Integrative bioinformatics analysis reveals STAT2 as a novel biomarker of inflammation-related cardiac dysfunction in atrial fibrillation.Open medicine (Warsaw, Poland) · 2023Article
- Analyses of m6A regulatory genes and subtype classification in atrial fibrillation.Frontiers in cellular neuroscience · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is still no clear understanding of the pathogenesis of atrial fibrillation (AF). For this purpose, we used integrated analysis to uncover immune infiltration characteristics and investigated their relationship with competing endogenous RNA (ceRNA) network in AF. Methods: Three AF mRNA data sets (GSE14975, GSE79768, and GSE41177) were integrated using the SVA method from Gene Expression Omnibus (GEO). Together with AF circRNA data set (GSE129409) and miRNA data set (GSE70887) from GEO database, we built a ceRNA network. Then hub genes were screened by the Cytoscape plug-in cytoHubba from a protein-protein interaction (PPI) network. As well, CIBERSORT was employed to investigate immune infiltration, followed by Pearson correlation coefficients to unravel the correlation between AF-related infiltrating immune cells and hub genes. Ulteriorly, circRNA-miRNA-mRNA regulatory axises that could be immunologically related to AF were obtained. Results: Ten hub genes were identified from the constructing PPI network. The immune infiltration analysis revealed that the number of monocytes and neutrophils was higher, as well as the number of dendritic cells activated and T cells regulatory (Tregs) was lower in AF. Seven hub genes (C5AR1, CXCR4, HCK, LAPTM5, MPEG1, TLR8, and TNFSF13B) were associated with those 4 immune cells ( Conclusion: The findings of our study provide insights into immuno-related ceRNA networks as potential molecular regulators of AF progression.
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Registered trials
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