ArticleBMC medical genomics2022
Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
- [Integrating Bioinformatics and Machine Learning Algorithms to Screen Inflammatory Biomarkers for Atrial Fibrillation and Experimental Validation].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Identification of tryptophan metabolism-related biomarkers for nonalcoholic fatty liver disease through network analysis.Endocrine connections · 2025Article
- Article
- Molecular Subtyping of Hepatocellular Carcinoma via Lysosome-Related Genes for Prognosis and Therapy Prediction.International journal of general medicine · 2025Article
- Myeloid-specific S100A8/A9 deficiency attenuates atrial fibrillation through prevention of TLR4/NF-kB-mediated immune cell recruitment and inflammation.Frontiers in immunology · 2025Article
- Machine Learning and Single-Cell Analysis Identify Molecular Features of IPF-Associated Fibroblast Subtypes and Their Implications on IPF Prognosis.International journal of molecular sciences · 2023Article
- Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.The Journal of international medical research · 2023Article
- Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis.Scientific reports · 2023Article
- Polarimetric imaging for cervical pre-cancer screening aided by machine learning:Journal of biomedical optics · 2023Article
- Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.Frontiers in genetics · 2023Article
- Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration.Frontiers in medicine · 2023Article
- Effects of rhythm control on left atrial structure remodeling in atrial fibrillation and heart failure with preserved ejection fraction.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail.
methodsTwo datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using "limma" package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells.
results129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells.
conclusionsIn conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.
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