ArticleFrontiers in bioengineering and biotechnology2020
The Joint Analysis of Multi-Omics Data Revealed the Methylation-Expression Regulations in Atrial Fibrillation.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- KIF15 (Kinesin-12): molecular biology, physiological functions, and roles in disease.Journal of cancer research and clinical oncology · 2026Review
- Atrial fibrillation: pathophysiology, genetic and epigenetic mechanisms.The Lancet regional health. Europe · 2024Review
- Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation.Biomedicines · 2023Article
- Multiple targets related to mitochondrial function unveiled by metabolomics and proteomics profiles of hearts from atrial fibrillation patients.Frontiers in physiology · 2023Article
- Article
- Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation.BioMed research international · 2021Article
- Genetics and Epigenetics of Atrial Fibrillation.International journal of molecular sciences · 2020Review
- A genome-wide association study of stroke risk in Asian statin users: evidence from KoGES and UK Biobank.PharmacogenomicsArticle
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is one of the most prevalent heart rhythm disorder. The causes of AF include age, male sex, diabetes, hypertension, valve disease, and systolic/diastolic dysfunction. But on molecular level, its mechanisms are largely unknown. In this study, we collected 10 patients with persistent atrial fibrillation, 10 patients with paroxymal atrial fibrillation and 10 healthy individuals and did Methylation EPICBead Chip and RNA sequencing. By analyzing the methylation and gene expression data using machine learning based feature selection method Boruta, we identified the key genes that were strongly associated with AF and found their interconnections. The results suggested that the methylation of KIF15 may regulate the expression of
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Registered trials
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