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Updated: May 12, 2026
Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
Bioinformatics analysis of circular RNAs associated with atrial fibrillation and their evaluation as predictive
Manman Wang1,2, Yuanyuan Chen1, Weiwei Yang3
1Shandong Provincial Key Medical and Health Discipline of Cardiology, Jining Key Laboratory for Diagnosis and Treatment of Cardiovascular Diseases, Jining Key Laboratory of Precise Therapeutic Research of Coronary Intervention, Department of Cardiology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China.
Background:
Circular noncoding RNAs (circRNAs) are implicated in many human diseases, but their role in atrial fibrillation (AF) is poorly understood. In this study, we performed bioinformatics analysis of circRNA sequencing data to identify AF-related circRNAs.
Methods:
Left atrial appendage (LAA) samples were obtained from patients with valvular heart disease and were categorised into the sinus rhythm (SR; n = 4) and AF (n = 4) groups. CircRNA sequencing analysis was performed to identify differentially expressed (DE) circRNAs in AF patients. Functional enrichment analysis of DE circRNAs was performed to identify enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
Results:
We identified 3338 DE circRNAs, including 2147 upregulated and 1191 downregulated circRNAs, in AF patients. A ceRNA network of 16 DE circRNAs, 11 DE miRNAs, and 15 DE mRNAs was constructed. Functional enrichment analyses revealed that the AF-related DE circRNAs were enriched in response to vitamin D, the potassium channel complex, delayed rectifier potassium channel activity, osteoclast differentiation, primary immunodeficiency, endocrine and other factor-regulated calcium reabsorption and other processes. ROC curve analysis identified circRNA_00324, circRNA_17225, circRNA_16305, circRNA_10233, circRNA_05499, circRNA_03183, circRNA_14211, and circRNA_18422 as potential predictive biomarkers for distinguishing AF patients from SR patients, with AUC values of 0.9138, 0.7370, 0.8526, 0.6803, 0.8163, 0.8662, 0.7664, and 0.9320, respectively.
Conclusions:
In this study, we constructed an AF-related ceRNA network and identified eight circRNAs as potential predictive biomarkers of AF.
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