Wang Manman, Chen Yuanyuan, Yang Weiwei, Li Xiangting, Liu Genli, Wang Xin, Liu Shuai, Gao Ge, Meng Fanhua, Kong Feifei, Sun Dandan, Qin Wei, Dong Bo, Zhang Jinguo
Shandong 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.
Postdoctoral Mobile Station of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Hum Genomics. 2025 May 12;19(1):52. doi: 10.1186/s40246-025-00760-7.
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.
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.
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.
In this study, we constructed an AF-related ceRNA network and identified eight circRNAs as potential predictive biomarkers of AF.
环状非编码RNA(circRNAs)与多种人类疾病有关,但其在心房颤动(AF)中的作用尚不清楚。在本研究中,我们对circRNA测序数据进行了生物信息学分析,以鉴定与AF相关的circRNAs。
从瓣膜性心脏病患者中获取左心耳(LAA)样本,并分为窦性心律(SR;n = 4)组和AF(n = 4)组。进行circRNA测序分析,以鉴定AF患者中差异表达(DE)的circRNAs。对DE circRNAs进行功能富集分析,以鉴定富集的基因本体(GO)术语和京都基因与基因组百科全书(KEGG)通路。
我们在AF患者中鉴定出3338个DE circRNAs,包括2147个上调的和1191个下调的circRNAs。构建了一个由16个DE circRNAs、11个DE miRNAs和15个DE mRNAs组成的ceRNA网络。功能富集分析显示,与AF相关的DE circRNAs在对维生素D的反应、钾通道复合物、延迟整流钾通道活性、破骨细胞分化、原发性免疫缺陷、内分泌和其他因子调节的钙重吸收等过程中富集。ROC曲线分析确定circRNA_00324、circRNA_17225、circRNA_16305、circRNA_10233、circRNA_05499、circRNA_03183、circRNA_14211和circRNA_18422为区分AF患者和SR患者的潜在预测生物标志物,AUC值分别为0.9138、0.7370、0.8526、0.6803、0.8163、0.8662、0.7664和0.9320。
在本研究中,我们构建了一个与AF相关的ceRNA网络,并鉴定出8个circRNAs作为AF的潜在预测生物标志物。