Translational pediatrics · 2026
Construction and validation of a diagnostic model for Kawasaki disease based on neutrophil-related genes and analysis of immune infiltration.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Jing, Zhang Huan
Аннотация
BACKGROUND: Kawasaki disease (KD) is an acute self-limiting systemic vasculitis of unknown etiology, lacking specific diagnostic biomarkers. Neutrophils and their released extracellular traps play a central role in disease pathogenesis and coronary artery injury, making targeting this pathological process a key direction for exploring novel diagnostic and therapeutic strategies. Therefore, this study aimed to identify neutrophil-related genes associated with KD and to construct and validate a diagnostic model for improving the early and precise diagnosis of KD.
METHODS: Based on Gene Expression Omnibus (GEO) database, this study integrated datasets for training (GSE18606, GSE68004) and validation (GSE63881, GSE73461). Key diagnostic genes were identified through differential analysis and machine learning (least absolute shrinkage and selection operator, extreme gradient boosting, random forest). A nomogram model was constructed and evaluated via receiver operating characteristic, calibration and decision curves. Immune infiltration was analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, followed by molecular subtyping and functional enrichment based on the diagnostic genes.
RESULTS: This study identified four neutrophil-related diagnostic genes (ALPL, F5, HK3, MGAM) and developed a high-performance nomogram model. Immune analysis showed increased neutrophil/macrophage infiltration and decreased adaptive immune cells in KD. Two immune-distinct molecular subtypes were identified, and upstream regulatory networks with potential therapeutic drugs were predicted, offering new insights into KD mechanisms and treatment.
CONCLUSIONS: This study developed a neutrophil-based diagnostic model for KD, revealing key immune microenvironment features and molecular subtypes. These findings enhance the understanding of disease heterogeneity and support early molecular diagnosis and targeted treatment strategies.
METHODS: Based on Gene Expression Omnibus (GEO) database, this study integrated datasets for training (GSE18606, GSE68004) and validation (GSE63881, GSE73461). Key diagnostic genes were identified through differential analysis and machine learning (least absolute shrinkage and selection operator, extreme gradient boosting, random forest). A nomogram model was constructed and evaluated via receiver operating characteristic, calibration and decision curves. Immune infiltration was analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT, followed by molecular subtyping and functional enrichment based on the diagnostic genes.
RESULTS: This study identified four neutrophil-related diagnostic genes (ALPL, F5, HK3, MGAM) and developed a high-performance nomogram model. Immune analysis showed increased neutrophil/macrophage infiltration and decreased adaptive immune cells in KD. Two immune-distinct molecular subtypes were identified, and upstream regulatory networks with potential therapeutic drugs were predicted, offering new insights into KD mechanisms and treatment.
CONCLUSIONS: This study developed a neutrophil-based diagnostic model for KD, revealing key immune microenvironment features and molecular subtypes. These findings enhance the understanding of disease heterogeneity and support early molecular diagnosis and targeted treatment strategies.