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Updated: Nov 19, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial
Yaozhong Liu1, Fan Bai1, Zhenwei Tang2
1Department of Cardiovascular Medicine/Cardiac Catheterization Lab, Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan Province, People's Republic of China.
Background:
Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.
Methods:
At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.
Results:
863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.
Conclusion:
Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.
