ArticleTranslational pediatrics2024
Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.
Article in Translational pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Comprehensive machine learning for identifying platelet-associated diagnostic biomarkers and immune landscape in Kawasaki disease.Translational pediatrics · 2026Article
- A pro-inflammatory neutrophil subpopulation drives intestinal ischemia-reperfusion injury via the ATF4-mediated endoplasmic reticulum stress pathway.Scientific reports · 2026Article
- Intravenous immunoglobulin remodels innate immune cell communication and induces differential autophagy pathways in Kawasaki disease.Frontiers in immunology · 2026Article
- Erratum: Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.Translational pediatrics · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood. Methods: This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Results: Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 ( Conclusions:
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