Chen Xiaoliang, Chen Lina, Bi Li, Zhao Shunying, Hu Xiaoyan, Li Ni, Zhu Linwen, Shao Guofeng
Department of Cardiosurgery Intensive Care Unit, Ningbo Medical Centre Lihuili Hospital, Ningbo University, Ningbo, China.
Department of Cardiothoracic Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University, Ningbo, China.
Front Cardiovasc Med. 2025 Aug 1;12:1639767. doi: 10.3389/fcvm.2025.1639767. eCollection 2025.
Circular RNAs (circRNAs) are involved in various Cardiovascular diseases; however, the circRNA expression profiles and the circRNA-microRNA(miRNA)-messenger RNA (mRNA) regulatory network in rheumatic heart disease (RHD) remain poorly understood. This study aimed to investigate the expression profiles of circRNAs and construct a circRNA-miRNA-mRNA interaction network to reveal new diagnostic biomarkers and potential pathogenesis of RHD.
Clinical data and plasma samples from 46 patients with RHD and 46 non-RHD patients were collected between January 2021 and December 2023. Arraystar Human CircRNA microarray was used to profile differentially expressed circRNAs in 3 paired samples (RHD vs. non-RHD). Quantitative real-time PCR (qRT-PCR) validated four candidate circRNAs in all 92 samples. The diagnostic value of differentially expressed circRNAs was analyzed by the Receiver Operating Characteristic (ROC) Curve. Bioinformatics analysis was used to predict the target miRNA and analyze the co-expressed mRNA to construct a circRNA-miRNA-mRNA regulatory network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to predict the potential functions of the differentially expressed genes and RHD-related pathways.
Four circRNAs were selected from circRNA microarray data. qRT-PCR confirmed that hsa_circ_0001490 and hsa_circ_0001296 were significantly upregulated in RHD plasma (4.28-fold, < 0.001; 5.24-fold, < 0.001, respectively). ROC analysis revealed hsa_circ_0001490 had an AUC of 0.792 (95% CI: 0.69-0.89; sensitivity: 93.5%; specificity: 67.4%), while hsa_circ_0001296 showed superior accuracy (AUC = 0.896; 95% CI: 0.83-0.96; sensitivity: 69.6%; specificity: 95.7%). A predicted hsa_circ_0001490-miRNA-mRNA regulatory network included 11 miRNAs and 1,973 mRNAs, and hsa_the circ_0001296-miRNA-mRNA interaction network included 9 miRNAs and 1,404 mRNAs. Moreover, the top 10 hub genes were screened within the two networks, respectively. The significantly enriched GO terms associated with hsa_circ_0001490 downstream genes were Smad binding and regulation of the Wnt signaling pathway. The significantly involved KEGG pathways included the Wnt signaling pathway, MAPK signaling pathway and TGF-beta signaling pathway. For hsa_circ_0001296, the significantly enriched GO terms were transforming growth factor beta receptor activity(type I) and Smad binding. The Autophagy pathway and MAPK signaling pathway were significantly involved in KEGG pathways.
This study provides the first evidence of significant upregulation of hsa_circ_0001490 and hsa_circ_0001296 in RHD patients, suggesting their potential as diagnostic biomarkers for RHD. The constructed circRNA-miRNA-mRNA network reveals potential molecular mechanisms underlying RHD pathogenesis. Future studies should investigate these circRNAs' functional roles to fully elucidate their contribution to RHD development.
环状RNA(circRNAs)参与多种心血管疾病;然而,风湿性心脏病(RHD)中的circRNA表达谱以及circRNA-微小RNA(miRNA)-信使核糖核酸(mRNA)调控网络仍知之甚少。本研究旨在调查circRNAs的表达谱,并构建一个circRNA-miRNA-mRNA相互作用网络,以揭示RHD新的诊断生物标志物和潜在发病机制。
收集2021年1月至2023年12月期间46例RHD患者和46例非RHD患者的临床资料和血浆样本。使用Arraystar人类circRNA微阵列分析3对样本(RHD与非RHD)中差异表达的circRNAs。采用定量实时聚合酶链反应(qRT-PCR)在所有92个样本中验证4个候选circRNAs。通过受试者工作特征(ROC)曲线分析差异表达circRNAs的诊断价值。利用生物信息学分析预测靶miRNA并分析共表达的mRNA,构建circRNA-miRNA-mRNA调控网络。进行基因本体(GO)和京都基因与基因组百科全书(KEGG)通路富集分析,以预测差异表达基因的潜在功能和RHD相关通路。
从circRNA微阵列数据中筛选出4个circRNAs。qRT-PCR证实hsa_circ_0001490和hsa_circ_0001296在RHD血浆中显著上调(分别为4.28倍,<0.001;5.24倍,<0.0者01)。ROC分析显示hsa_circ_0001490的曲线下面积(AUC)为0.792(95%CI:0.69-0.89;敏感性:93.5%;特异性:67.4%),而hsa_circ_0001296显示出更高的准确性(AUC=0.896;95%CI:0.83-0.96;敏感性:69.6%;特异性:95.7%)。预测的hsa_circ_0001490-miRNA-mRNA调控网络包括11个miRNA和1973个mRNA,hsa_circ_0001296-miRNA-mRNA相互作用网络包括9个miRNA和1404个mRNA。此外,分别在两个网络中筛选出前10个枢纽基因。与hsa_circ_0001490下游基因显著富集的GO术语是Smad结合和Wnt信号通路的调控。显著涉及的KEGG通路包括Wnt信号通路、丝裂原活化蛋白激酶(MAPK)信号通路和转化生长因子β(TGF-β)信号通路。对于hsa_circ_0001296,显著富集的GO术语是转化生长因子β受体活性(I型)和Smad结合。自噬通路和MAPK信号通路在KEGG通路中显著涉及。
本研究首次证明RHD患者中hsa_circ_0001490和hsa_circ_0001296显著上调,提示它们作为RHD诊断生物标志物的潜力。构建的circRNA-miRNA-mRNA网络揭示了RHD发病机制的潜在分子机制。未来的研究应调查这些circRNAs的功能作用,以充分阐明它们对RHD发展的贡献。