Abstract
Kawasaki disease (KD), as a common pediatric inflammatory vasculitis, has an unclear pathogenesis. This study integrated bioinformatics and clinical data analysis to explore the characteristics of ferroptosis in KD. We used data from the Gene Expression Omnibus (GEO) database to identify ferroptosis-related genes, and variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) analysis. We employed seven machine learning methods to determine the optimal predictive model, revealing that the Support Vector Machine (SVM) model exhibited optimal performance in external validation. Unsupervised clustering based on FRGs stratified KD patients into high and low expression subtypes. The high expression subtype showed significantly elevated levels of monocyte immune infiltration, which was further validated by single-cell analysis. Clinical data analysis demonstrated that patients in the high-monocyte group not only had a higher incidence of incomplete KD presentations but also exhibited lower resistance to intravenous immunoglobulin (IVIG) therapy. These results suggest that ferroptosis may participate in the pathogenesis of KD by modulating monocyte levels, providing new insights into explaining clinical heterogeneity and differences in IVIG treatment responses.