Translational pediatrics · 2026
Identification of potential biomarkers of tryptophan metabolism in Kawasaki disease and exploration of potential mechanisms.
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Yuan Yonghua, Cao Yuan, He Xuehua, Liu Liping, Zhang Xianhua, Zhong Lili
Аннотация
BACKGROUND: Kawasaki disease (KD) is an acute febrile illness, primarily affecting children under 5 years old. The relationship between tryptophan metabolism (TM) and KD is unclear. In this study, we identified biomarkers associated with TM-related genes in KD, and explore their potential biological roles.
METHODS: Differentially expressed genes (DEGs) were identified that intersected with key module genes from a weighted gene co-expression network analysis. We used three algorithms to obtain key genes. Biomarkers were described as characteristic genes with an area under the curve (AUC) above 0.7 in the GSE68004 and GSE73461 datasets. The pathways associated with biomarkers were probed using gene set enrichment analysis, whereas immune infiltration analysis revealed their correlations with immune cells. Furthermore, the N6-methyladenosine (m6A) modification sites, chromosome positions, microRNAs (miRNAs), transcription factors (TFs), and potential drugs associated with the putative biomarkers were predicted. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was applied to clinical samples to analyze biomarker expression.
RESULTS: A total of 906 DEGs and 1,194 key module genes overlapped, which resulted in 234 key genes. Based on three machine-learning algorithms, SPI1, FCER1G, ITGAX, and NCF2 were identified as characteristic genes with an AUC greater than 0.7, which may represent biomarkers for validation. A positive link was observed between the biomarkers and ribosomes, osteoclast differentiation, and neutrophil extracellular trap formation. A strong negative correlation was found between the biomarkers and CD8+ T cells. A total of 86 TFs, 76 potential drugs, and four miRNAs were predicted as modifiers of these four biomarkers. RT-qPCR analysis revealed the expression of four genes that were significantly different in the KD and control groups.
CONCLUSIONS: Four biomarkers associated with TM in KD were identified, and their mechanisms were further examined. The results provide mechanistic insight for the improved diagnosis and treatment of KD.
METHODS: Differentially expressed genes (DEGs) were identified that intersected with key module genes from a weighted gene co-expression network analysis. We used three algorithms to obtain key genes. Biomarkers were described as characteristic genes with an area under the curve (AUC) above 0.7 in the GSE68004 and GSE73461 datasets. The pathways associated with biomarkers were probed using gene set enrichment analysis, whereas immune infiltration analysis revealed their correlations with immune cells. Furthermore, the N6-methyladenosine (m6A) modification sites, chromosome positions, microRNAs (miRNAs), transcription factors (TFs), and potential drugs associated with the putative biomarkers were predicted. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was applied to clinical samples to analyze biomarker expression.
RESULTS: A total of 906 DEGs and 1,194 key module genes overlapped, which resulted in 234 key genes. Based on three machine-learning algorithms, SPI1, FCER1G, ITGAX, and NCF2 were identified as characteristic genes with an AUC greater than 0.7, which may represent biomarkers for validation. A positive link was observed between the biomarkers and ribosomes, osteoclast differentiation, and neutrophil extracellular trap formation. A strong negative correlation was found between the biomarkers and CD8+ T cells. A total of 86 TFs, 76 potential drugs, and four miRNAs were predicted as modifiers of these four biomarkers. RT-qPCR analysis revealed the expression of four genes that were significantly different in the KD and control groups.
CONCLUSIONS: Four biomarkers associated with TM in KD were identified, and their mechanisms were further examined. The results provide mechanistic insight for the improved diagnosis and treatment of KD.