Li Wang, Zhu Wenjie, Tang Xiangting, Peng Zhiting, Ye Jiaqi, Nie Shuping
Department of Clinical laboratory, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Middle Road, Shenzhen, Guangdong, 518000, China.
BMC Pediatr. 2025 May 19;25(1):400. doi: 10.1186/s12887-025-05752-z.
Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD.
We first identified disease-associated modules in KD using weighted gene co-expression network analysis. Then, we determined shared differentially expressed genes (DEGs) in training datasets for KD (GSE100154) and COVID-19 (GSE225220), performed functional annotation of these shared DEGs, and used Cytoscape plug-ins (MCODE and Cytohubba) to characterize the protein-protein interaction (PPI) network and identify the hub genes. We performed Least Absolute Shrinkage and Selection Operator(LASSO) regression and receiver operating characteristic (ROC) curve analysis to identify the most robust markers, validated these results by analysis of two other datasets (GSE73461 and GSE18606), and then calculated the correlations of these key genes with immune cells.
This analysis identified 26 shared DEGs in COVID-19 and KD. The results from functional annotation showed that the shared DEGs primarily functioned in immune responses, the formation of neutrophil extracellular traps, and NOD-like receptor signaling pathways. There were three key genes (PGLYRP1, DEFA4, RETN), and they had positive correlations with monocytes, M0 macrophages, and dendritic cells, which function as immune infiltrating cells in KD.
The potential immune-associated biomarkers (PGLYRP1, DEFA4, RETN) along with their shared pathways, hold promise for advancing investigations into the underlying pathogenesis of KD and COVID-19.
严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染可导致2019冠状病毒病（COVID-19），还可加重川崎病（KD）的症状，川崎病是一种主要影响儿童的急性血管炎。本研究运用生物信息学和机器学习方法，探究COVID-19与KD在分子发病机制上的相似性。
我们首先通过加权基因共表达网络分析，在KD中鉴定出疾病相关模块。然后，我们在KD（GSE100154）和COVID-19（GSE225220）的训练数据集中确定共享的差异表达基因（DEG），对这些共享DEG进行功能注释，并使用Cytoscape插件（MCODE和Cytohubba）来表征蛋白质-蛋白质相互作用（PPI）网络并识别枢纽基因。我们进行了最小绝对收缩和选择算子（LASSO）回归及受试者工作特征（ROC）曲线分析，以确定最可靠的标志物，并通过分析另外两个数据集（GSE73461和GSE18606）验证这些结果，然后计算这些关键基因与免疫细胞的相关性。
该分析在COVID-19和KD中鉴定出26个共享DEG。功能注释结果表明，共享DEG主要在免疫反应、中性粒细胞胞外陷阱的形成以及NOD样受体信号通路中发挥作用。有三个关键基因（肽聚糖识别蛋白1、防御素4、抵抗素），它们与单核细胞、M0巨噬细胞和树突状细胞呈正相关，这些细胞在KD中作为免疫浸润细胞发挥作用。
潜在的免疫相关生物标志物（肽聚糖识别蛋白1、防御素4、抵抗素）及其共享通路，有望推动对KD和COVID-19潜在发病机制的研究。
