Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

• Published in: Immunobiology (1979) Vol. 228; no. 6; p. 152750
• Main Authors: Gao, Yang, Tang, Xuan, Qian, Guanghui, Huang, Hongbiao, Wang, Nana, Wang, Yan, Zhuo, Wenyu, Jiang, Jiaqi, Zheng, Yiming, Li, Wenjie, Liu, Zhiheng, Li, Xuan, Xu, Lei, Zhang, Jiaying, Huang, Li, Liu, Ying, Lv, Haitao
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier GmbH 01.11.2023
• Subjects: Biomarkers; Child; Computational Biology; Humans; Kawasaki disease; LASSO regression model; Matrix Metalloproteinase 9; Mucocutaneous Lymph Node Syndrome - diagnosis; Mucocutaneous Lymph Node Syndrome - genetics; S100A12 Protein; Toll-Like Receptor 2; Weighted gene correlation network analysis; Biomarkers; Weighted gene correlation network analysis; Kawasaki disease; LASSO regression model
• ISSN: 0171-2985; 1878-3279
• DOI: 10.1016/j.imbio.2023.152750

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. 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. 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. 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.