{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:00:26Z","timestamp":1785218426303,"version":"3.55.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"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>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Kawasaki disease (KD) is a systemic vasculitis. Mitochondria was found to promote the activation of NLRP3 inflammatory vesicles, which have been shown to be a key driver of vascular disease. And there are few relevant reports of mitochondrial dynamic (MD) in KD. This study aimed to distinguish the potential biomarkers related to MD in KD and supply ideas for the intervention and treatment of KD. Currently, functional experimental verification in this field is lacking, and this study preliminarily explores their potential correlations through bioinformatics analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Differential expression analysis, machine learning, and expression validation were employed to identify potential biomarkers. Subsequently, analyses like enrichment analysis, immune infiltration analysis, and molecular regulatory network were applied to probe the underlying mechanisms of potential biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>RAP2C, DPM2, and DDX59 were identified as potential biomarkers associated with MD in KD and were mainly involved in inflammation-related pathways. Notably, 13 differential immune cells were gained in KD and control groups, 9 of which showed strong correlation with potential biomarkers, suggesting a relationship between potential biomarkers and the immune microenvironment in KD. For example, DPM2 had the positive relationship with CD8\u2005T cells, while DDX59 had the negative relationship with CD8\u2005T cells. Afterwards, molecular regulatory networks of potential biomarkers were constructed, specifically, they shared 5 common microRNAs (miRNAs) and 3 common transcription factors (TFs). Ultimately, potential biomarkers had 3 common targeting drugs [valproic acid, benzo(a)pyrene, and acetaminophen]; notably, benzo(a)pyrene was excluded due to carcinogenicity, which supplied the basis for targeting potential biomarkers to treat KD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The potential biomarkers (RAP2C, DPM2, and DDX59) related to MD in KD were acquired. These findings provide preliminary insights for future drug repurposing studies targeting KD and offer bioinformatic evidence for a potential link between mitochondrial dynamics and the immune microenvironment in KD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1856834","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:37:52Z","timestamp":1785217072000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bioinformatics identification of mitochondrial dynamics-related potential biomarkers in kawasaki disease and prediction of potential regulatory correlations"],"prefix":"10.3389","volume":"13","author":[{"given":"Tingting","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Cardiology, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, 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