Expression of key genes in IVIG-unresponsive Kawasaki disease and their immune correlation based on bioinformatics analysis.

This study employed bioinformatics to analyze the gene expression profile of Kawasaki disease (KD) patients and identify key genes associated with intravenous immunoglobulin (IVIG) non-response. The aim was to elucidate the pathogenesis of IVIG non-responsive KD and identify potential predictive biomarkers and therapeutic targets. Gene expression datasets GSE48498 and GSE16797 were analyzed. Key steps included differential expression analysis, functional enrichment (GO/KEGG), weighted gene co-expression network analysis (WGCNA), immune cell infiltration analysis (CIBERSORT), protein-protein interaction (PPI) network construction, and machine learning algorithms (LASSO regression and random forest). Findings were validated using RT-qPCR. Differential expression analysis identified 327 differentially expressed genes (DEGs), enriched primarily in immune-related pathways. WGCNA revealed that the cyan and yellow modules exhibited a positive correlation with neutrophil and M0 macrophage infiltration levels. Integration of PPI networks, least absolute shrinkage and selection operator (LASSO) regression, and random forest algorithms identified CXCR1, FPR2, and MMP25 as core genes. Receiver operating characteristic (ROC) analysis yielded area under the curve (AUC) values of 0.887, 0.869, and 0.869 for CXCR1, FPR2, and MMP25, respectively; the combined diagnostic efficacy reached an AUC of 0.905. RT-qPCR validation confirmed significantly higher expression levels of CXCR1 and FPR2 in the IVIG non-responder group compared to responders (P < 0.05), while MMP25 expression showed no significant difference between groups. This study, combining bioinformatics analysis with experimental validation, identifies CXCR1 and FPR2 as key genes implicated in IVIG non-responsive KD.