{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:09:10Z","timestamp":1784009350646,"version":"3.55.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2013miRNA-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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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, <jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.001; 5.24-fold, <jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.001, respectively). ROC analysis revealed hsa_circ_0001490 had an AUC of 0.792 (95% CI: 0.69\u20130.89; sensitivity: 93.5%; specificity: 67.4%), while hsa_circ_0001296 showed superior accuracy (AUC\u2009=\u20090.896; 95% CI: 0.83\u20130.96; 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2025.1639767","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T05:33:23Z","timestamp":1754026403000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Expression profiles and bioinformatic analysis of circular RNA in rheumatic heart disease: potential hsa_circ_0001490 and hsa_circ_0001296 as a diagnostic biomarker"],"prefix":"10.3389","volume":"12","author":[{"given":"Xiaoliang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ni","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linwen","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guofeng","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1038\/s41569-021-00570-z","article-title":"Global 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