ArticlePediatric rheumatology online journal2025
Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease.
Article in Pediatric rheumatology online journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The Autophagy-Inflammasome Axis as a Molecular Switch: From Persistent Inflammation to Vascular Remodeling in IVIG-Resistant Kawasaki Disease.International journal of molecular sciences · 2026Review
- [Effect of miR-155-5p regulating the TLR4/MAPK/NF-κB pathway on inflammatory response, coronary artery lesion and immune function in mice with Kawasaki disease].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Organic cation transporter novel 1 (OCTN1): beyond an ergothioneine transporter.Cell communication and signaling : CCS · 2026Review
- Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and treatment.Clinical and experimental pediatrics · 2026Article
- The future of Kawasaki disease management: data-driven innovations from bedside to bench and back again.Pediatric research · 2025Article
- An integrated and interpretable machine learning framework for Kawasaki disease diagnosis and risk prediction.Translational pediatrics · 2025Article
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Authors and funding
2 authors.
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Abstract
backgroundKawasaki disease (KD), a pediatric systemic vasculitis, lacks reliable diagnostic biomarkers and exhibits immune heterogeneity, complicating clinical management. Current therapies face challenges in targeting specific immune pathways and predicting treatment responses.
methodsMulti-cohort transcriptomic data were integrated to identify inflammation-related genes (IRGs). Differential analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, Boruta, SVM-RFE, Random Forest) were applied to screen diagnostic biomarkers. Immune infiltration and molecular subtyping based on diagnostic biomarkers were analyzed, complemented by regulatory network analysis to explore transcriptional, pharmacological, and miRNA interactions.
resultsSix robust diagnostic biomarkers (ADM, ALPL, FCGR1A, HP, S100A12, SLC22A4) were identified, achieving AUC > 0.9 in cohorts. KD exhibited elevated neutrophils, monocytes, and Tregs but reduced CD8 + T cells and cytolytic activity. Consensus clustering stratified KD into two immune-heterogeneous subtypes: Cluster1 (neutrophil/Treg-dominant, enriched in TLR signaling) and Cluster2 (B cell/CD8 + T cell-dominant, linked to cytolytic activity). Regulatory networks revealed subtype-specific transcriptional regulators and therapeutic agents.
conclusionThis study establishes inflammation-related diagnostic biomarkers and immune-stratified subtypes for KD, offering a framework for precision immunomodulatory therapies.
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