Abstract：
BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood.
METHODS: Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers.
RESULTS: WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells.
CONCLUSIONS: Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.
METHODS: Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers.
RESULTS: WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells.
CONCLUSIONS: Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.
摘要：
背景：川崎病（KD）是一种全身性血管炎，通常影响儿童，其病因尚不清楚。越来越多的证据表明，外周血中免疫介导的炎症和免疫细胞在KD的病理生理中起着至关重要的作用。这项研究的目的是寻找与KD有关的重要生物标志物和免疫相关机制，以及它们与外周血中免疫细胞的相关性。
方法:在本研究中使用来自基因表达综合(GEO)的基因微阵列数据。三个数据集，即GSE63881（341个样品），GSE73463(233个样品),和GSE73461（279个样品），已获得。为了找到相交的基因，我们采用了差异表达基因（DEGs）分析和加权基因共表达网络分析（WGCNA）。随后，功能注释，蛋白质-蛋白质相互作用（PPI）网络的构建，进行最小绝对收缩和选择算子(LASSO)回归以鉴定hub基因。使用接受者工作特征曲线（ROC）评估了这些hub基因在鉴定KD中的准确性。此外，采用基因集变异分析(GSVA)来探索所评估的数据集内的循环免疫细胞的组成及其与中心基因标记的关系。
结果：WGCNA产生了八个共表达模块，一个集线器模块（MEblue模块）与急性KD的相关性最强。鉴定了425个不同的基因。整合WGCNA和DEGs产生总共277个交叉基因。通过进行LASSO分析，5个hub基因(S100A12、MMP9、TLR2、NLRC4和ARG1)被鉴定为KD的潜在生物标志物。通过ROC曲线分析证明了这5个hub基因的诊断价值,表明它们在诊断KD方面具有很高的准确性。对评估数据集内循环免疫细胞组成的分析揭示了KD与各种免疫细胞类型之间的显著关联。包括激活的树突状细胞，中性粒细胞，未成熟树突状细胞,巨噬细胞，和激活的CD8T细胞。重要的是,所有五个hub基因都与免疫细胞表现出很强的相关性。
结论：激活的树突状细胞，中性粒细胞，巨噬细胞与KD的发病密切相关。此外，hub基因(S100A12、MMP9、TLR2、NLRC4和ARG1)可能通过免疫相关信号通路参与KD的致病机制。
方法:在本研究中使用来自基因表达综合(GEO)的基因微阵列数据。三个数据集，即GSE63881（341个样品），GSE73463(233个样品),和GSE73461（279个样品），已获得。为了找到相交的基因，我们采用了差异表达基因（DEGs）分析和加权基因共表达网络分析（WGCNA）。随后，功能注释，蛋白质-蛋白质相互作用（PPI）网络的构建，进行最小绝对收缩和选择算子(LASSO)回归以鉴定hub基因。使用接受者工作特征曲线（ROC）评估了这些hub基因在鉴定KD中的准确性。此外，采用基因集变异分析(GSVA)来探索所评估的数据集内的循环免疫细胞的组成及其与中心基因标记的关系。
结果：WGCNA产生了八个共表达模块，一个集线器模块（MEblue模块）与急性KD的相关性最强。鉴定了425个不同的基因。整合WGCNA和DEGs产生总共277个交叉基因。通过进行LASSO分析，5个hub基因(S100A12、MMP9、TLR2、NLRC4和ARG1)被鉴定为KD的潜在生物标志物。通过ROC曲线分析证明了这5个hub基因的诊断价值,表明它们在诊断KD方面具有很高的准确性。对评估数据集内循环免疫细胞组成的分析揭示了KD与各种免疫细胞类型之间的显著关联。包括激活的树突状细胞，中性粒细胞，未成熟树突状细胞,巨噬细胞，和激活的CD8T细胞。重要的是,所有五个hub基因都与免疫细胞表现出很强的相关性。
结论：激活的树突状细胞，中性粒细胞，巨噬细胞与KD的发病密切相关。此外，hub基因(S100A12、MMP9、TLR2、NLRC4和ARG1)可能通过免疫相关信号通路参与KD的致病机制。
