Background. Coronary heart disease (CHD) is the leading cause of death and disability worldwide. Accumulating evidence reveals that atherosclerosis (AS), characterized by systemic, chronic, and multifocal disease, and is the primary pathological basis of cardiovascular diseases, including CHD. However, the molecular underpinnings of CHD are still far from well understood. Our study attempted to identify aberrant plasma exosome-derived circRNAs and key exosomal circRNA biomarkers for CHD. Methods. The expression profiles of mRNAs, circRNAs, and lncRNAs in the blood exosomes of CHD patients and healthy controls were obtained from the exoRBase database. The corresponding miRNAs of the differentially expressed mRNAs, circRNAs, and lncRNAs were predicted via ENCORI and the miRcode database. LncRNAs/circRNAs and mRNAs with the cotargeted miRNAs were selected to construct an interaction network. Multiple machine learning algorithms have been used to explore potential biomarkers, followed by verification in patients with CHD using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Results. Based on the cutoff criterion of , we identified 85 differentially expressed circRNAs (4 upregulated and 81 downregulated), 43 differentially expressed lncRNAs (24 upregulated and 19 downregulated), and 312 differentially expressed mRNAs (55 upregulated and 257 downregulated). Functional enrichment analysis revealed that the differentially expressed mRNAs were involved mainly in neutrophil extracellular trap (NET) formation and the nucleotide-binding oligomerization domain- (NOD-) like receptor signaling pathway. Further analysis revealed that the DEGs in the circRNA/lncRNA-miRNA-mRNA interaction network were closely related to lipid and atherosclerotic signaling pathways. Hsa_circ_0001360 and hsa_circ_0000038 were identified as potential biomarkers for CHD based on three machine learning algorithms. The relative expression levels of hsa_circ_0001360 and hsa_circ_0000038 were significantly altered in plasma exosomes from patients with CHD. ROC curve analysis revealed that the areas under the curve (AUCs) were 0.860, 0.870, and 0.940 for hsa_circ_0001360, hsa_circ_0000038, and the two-gene combination, respectively. Conclusion. The circRNA/lncRNA-miRNA-mRNA interaction network might help to elucidate the pathogenesis of CHD. Hsa_circ_0001360 combined with hsa_circ_0000038 might be an important diagnostic biomarker.

中文翻译：

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鉴定血浆外泌体 hsa_circ_0001360 和 hsa_circ_0000038 作为冠心病的关键生物标志物

背景。冠心病（CHD）是全世界死亡和残疾的主要原因。越来越多的证据表明，动脉粥样硬化（AS）以全身、慢性、多灶性疾病为特征，是包括冠心病在内的心血管疾病的首要病理基础。然而，CHD 的分子基础仍远未得到充分了解。我们的研究试图鉴定异常血浆外泌体衍生的 circRNA 和 CHD 的关键外泌体 circRNA 生物标志物。方法。从 exoRBase 数据库获得先心病患者和健康对照者血液外泌体中 mRNA、circRNA 和 lncRNA 的表达谱。通过ENCORI和miRcode数据库预测差异表达的mRNA、circRNA和lncRNA的相应miRNA。选择 LncRNA/circRNA 和具有共靶 miRNA 的 mRNA 来构建相互作用网络。多种机器学习算法已被用来探索潜在的生物标志物，然后使用实时定量逆转录聚合酶链反应（RT-qPCR）在先心病患者中进行验证。结果。基于截止标准，我们鉴定了8​​5个差异表达的circRNA（4个上调和81个下调）、43个差异表达的lncRNA（24个上调和19个下调）和312个差异表达的mRNA（55个上调和257个下调）。功能富集分析显示，差异表达的mRNA主要参与中性粒细胞胞外陷阱（NET）的形成和核苷酸结合寡聚化结构域（NOD-）样受体信号通路。进一步分析发现，circRNA/lncRNA-miRNA-mRNA相互作用网络中的DEG与脂质和动脉粥样硬化信号通路密切相关。基于三种机器学习算法，hsa_circ_0001360 和 hsa_circ_0000038 被确定为 CHD 的潜在生物标志物。 CHD 患者血浆外泌体中 hsa_circ_0001360 和 hsa_circ_0000038 的相对表达水平显着改变。 ROC曲线分析显示，hsa_circ_0001360、hsa_circ_0000038和双基因组合的曲线下面积（AUC）分别为0.860、0.870和0.940。结论。 circRNA/lncRNA-miRNA-mRNA相互作用网络可能有助于阐明CHD的发病机制。 Hsa_circ_0001360 与 hsa_circ_0000038 组合可能是重要的诊断生物标志物。