Kawasaki disease (KD) is a systemic vasculitis that predominantly damages medium- and small-sized vessels, and mainly causes coronary artery lesions (CALs). KD has become the main cause of acquired heart disease in children; 80% of these cases occur in children aged 6 months to 5 years (Makino et al., 2015), and timely diagnosis and treatment with intravenous immunoglobulin (IVIG) have reduced CALs by about 84% (McCrindle et al., 2017). However, some patients, especially infants under 6 months of age and adolescents, did not meet the criterion of KD, called “incomplete KD”, and it is difficult to make a diagnosis in the early stage of the disease. If there is a special index for KD, the early diagnosis will be easy. On the other hand, delayed diagnosis or missed diagnosis may also cause excessive drug treatment or invasive procedures in KD children. Therefore, an efficient diagnostic index of KD is pursued.

Diagnosis guidelines of KD provided by AHA and the Japanese Ministry of Health are mainly based on clinical symptoms and signs (Ayusawa et al., 2005; McCrindle et al., 2017). Some laboratory data, such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), albumin and N-terminal pro-B-type natriuretic peptide (NT-proBNP) level, thrombocytosis, leukocytosis, raised transaminases, hyponatremia, and pyuria, may be helpful in the diagnosis of KD (Cho et al., 2011; Lin et al., 2015; Reddy et al., 2016; Dionne et al., 2017), but all of them are yet to find the same change in other immune and infectious disease, and cannot act as special diagnostic indexes for KD.

The pathogenesis of KD have not been clear so far. However, studies have demonstrated that both immune cells and immune-related genes are involved in the pathogenesis of KD (Sánchez-Manubens et al., 2014; Sakurai et al., 2019). Evaluating the difference of immune cell composition in KD from the perspective of the immune system may have great value in elucidating the molecular mechanism and developing new diagnostic algorithms of KD. Bioinformatics has now been widely used in revealing the molecular mechanism of diseases. CIBERSORTx is an analysis tool to infer the expression of immune cells and obtain various immune cell proportions from samples based on gene expression or sequencing data (Newman et al., 2019).

In this study, we downloaded the microarray datasets of KD from Gene Expression Omnibus (GEO) database and performed differential expression immune-related gene analysis; least absolute shrinkage and selection operator (Lasso) regression algorithm and support vector machine-recursive feature elimination (SVM-RFE) algorithm were used to further screen the diagnostic markers of KD. Based on the diagnostic markers, we have developed and verified a diagnostic model. Subsequently, CIBERSORTx was used to assess the relative content of 22 kinds of immune cell subsets in whole blood in children with KD. In addition, the relationship between diagnostic markers and immune cells was analyzed to better understand the molecular immune mechanism of KD.

The diagnostic criterion of KD mainly depends on clinical symptoms and signs, but some patients, especially infants under 6 months old and adolescents, did not meet the criterion in the early stage of disease, and thus it is difficult to make a timely diagnosis. Delayed diagnosis and treatment may cause serious adverse outcomes such as CAAs. Abnormal gene expression level has been studied in diagnosis of KD (Kuo et al., 2016; Jaggi et al., 2018; Wright et al., 2018). However, these studies proposed that biomarkers were too many or not further validated by the external cohort. In this study, we developed a simple and concise diagnosis model, composed of four genes, by analyzing expression levels of immune-related genes. Verification of this model was performed by three other external datasets including KD and non-KD, and shows a great capacity for diagnosis of KD. At the same time, this model was validated with two datasets including acute KD and convalescent KD.

The training set was used to identify 14 immune-related DEGs. DO enrichment analysis showed that the DEGs were mainly related to KD, atherosclerosis, arteriosclerosis, and arteriosclerotic cardiovascular disease. This enrichment result is consistent with previous studies that have shown an increased risk of early atherosclerosis in children with KD (Cho et al., 2014; Zhang et al., 2016). In addition, both KEGG and GSEA were enriched in fluid shear stress and atherosclerosis, and tumor necrosis factor (TNF) signaling pathway. Masaki et al. (Xie et al., 2018). revealed that gene polymorphisms of TNF may affect KD susceptibility, and TNF-α is associated with the development of CALs in KD patients (Hu et al., 2017; Guo et al., 2020). The results indicated that the analysis results of this study are accurate. The etiology of KD is considered to have an infectious trigger in a genetically susceptible individual, followed by the activation of the immune system (Yim et al., 2013). The main trigger pathogens suspected were the viral agents (Usta Guc et al., 2008; Alexoudi et al., 2011; Chang et al., 2014; Shulman et al., 2015; Kim et al., 2016); some studies have linked triggers to bacteria agents (Galeotti et al., 2010; Alexoudi et al., 2011). However, in our study, the levels of immune-related differentially expressed genes (DEGs) in bacterial infection samples were more similar to those of KD than viral infection samples. It is speculated that the bacteria triggering may be the main factor of the activation of the KD immune system.

Based on immune-related DEGs and machine learning algorithms, Lasso and SVM-RFE were performed to screen KD diagnostic markers. By integrating the features of the two algorithms, IL1B, ADM, PDGFC, and TGFA were selected as diagnostic biomarkers. Although the single biomarker for diagnosis and prognosis has been widely reported, value and robustness are also a major concern. So, we further developed a logistics prediction model with these 4 markers, and further external verification shows that the model was reliable. In our validation sets, two sets only contain acute KD and convalescent KD, but both diagnostic markers and model showed well differentiation. Interleukin-1β (IL-1β) is a potential inflammation-causing cytokine released predominantly by immune-derived cells. Studies indicate that IL-1β has a significant role in several aspects of vascular inflammation (Chamberlain et al., 2006); there is a high level of IL-1β in the endothelium of atherosclerotic coronary arteries (Chibana et al., 2017) and might be one of the targets of atherosclerotic protective therapy (Ridker et al., 2017). Similarly, there are reports that the gene polymorphisms of IL-1β may be associated with initial IVIG treatment failure (Weng et al., 2010) and significantly impact the risk of CAL formation in children with KD (Fu et al., 2019). IL-1β may also increase the production of neutrophil extracellular traps by activating neutrophils, thereby inducing the progression of coronary artery ectasia (Guo et al., 2020). Adrenomedullin (ADM) is a vasoactive peptide involved in vasodilation and regulation of endothelial function. ADM inhibits the apoptosis of cultured endothelial cells and the high permeability of human vascular smooth muscle cells induced by agonists (Hippenstiel et al., 2002). Plasma ADM levels can reflect the degree of endothelial damage in atherosclerosis patients (Bunton et al., 2004). It is a good indicator of prognosis in patients with coronary artery disease (Wong et al., 2012). In KD, the expression level of ADM gene in plasma has been reported to be elevated (Nishida et al., 2001; Nomura et al., 2005); plasma ADM levels were higher in KD patients developed CAAs than in those did not (Nishida K et al., 2001). Platelet-derived growth factors (PDGFs) play an important role in vascular pathologies such as atherosclerosis, restenosis, and aortic aneurysm. PDGF-C can promote the migration and proliferation of macrophages (Boor et al., 2010), endothelial cells (Li et al., 2005), and vascular smooth muscle cells (Crawford et al., 2009). Karvinen et al. (2009) have revealed that PDGF-C is expressed in different stages of atherosclerosis. However, as a new member of the PDGF family, PDGF-C has been less studied in cardiovascular diseases since its discovery. However, PDGFC failed in GSE18606 when independently distinguishing KD from non-KD, which may be related to the small sample size of this dataset. Transforming growth factor α (TGF-α) as a member of the epidermal growth factor (EGF) family has been associated with cerebrovascular diseases (Dai et al., 2020) and pulmonary vascular disease (Le Cras et al., 2003; Bouzina et al., 2018). Similarly, TGF-α has been less studied in cardiovascular disease.

CIBERSORTx evaluation showed that the abundance of naive B cells, activated memory CD4 T cells, gamma delta T cells, monocytes, M0 macrophages, activated dendritic cells, activated mast cells, and neutrophils was increased, and the abundance of memory B cells, CD8 T cells, activated memory CD4 T cells, activated NK cells, M1 and M2 macrophages, and resting mast cells was decreased. Studies have shown that, CD8 T cells, neutrophils, IgA plasma cells, and macrophages infiltrate and destroy the internal elastic lamina of medium-sized vessels (Takahashi et al., 2005; Alexoudi et al., 2011; Orenstein et al., 2012). However, compared to CD8 T cells, CD4 T cells predominate in peripheral blood (Alexoudi et al., 2011), which may be due to the CD8 T-cell transmission to tissues during the development of KD. In the acute phase of KD, the increase in the number of circulating neutrophils and activation eventually cause endothelial dysfunction (Sakurai et al., 2019). In addition, our results revealed that CD8 T cells are closely related to resting NK cells, macrophages M0, monocytes, activated dendritic cells, and neutrophils; regulatory T cells (Tregs) are closely related to monocytes; resting NK cells are closely related to monocytes and activated dendritic cells; and activated dendritic cells are closely related to monocytes and resting mast cells. Considering the complexity of the immune system, it is difficult to study the immune cells of KD. Our analysis provides reference for the study of immune response in the pathogenesis of KD.

We used novel scientific algorithms to identify the diagnostic markers for KD and verified them with multiple external datasets. CIBERSORTx was used to analyze the proportion of various immune cells in KD whole blood for the first time. Although the diagnostic model in this study is composed of only four genes with significant diagnostic impact, which makes it handy to use in clinical practice, it still has some limitations. Firstly, our research is the second mining and analysis of previously published datasets, and the sample size of two validation sets is small. Secondly, CIBERSORTx analysis is based on limited genetic data to infer cell-type abundance and cell-type-specific gene expression, which may deviate from the actual situation. In addition, the reliability of the results of our study needs further experiments to verify.

In the present study, 4 immune-related DEGs were identified, and a diagnostic model for KD was established and verified. The biological functions and pathways of the immune-related genes provide a detailed molecular mechanism for understanding the pathogenesis of KD. By CIBERSORTx algorithms, we found a difference in immune cells between KD and non-KD. These immune cells may play a key role in the pathogenesis and development of KD. Further analysis of the relation between immune-related DEGs and immune cells will be helpful to further explore the pathogenesis of KD and may determine the immunotherapy targets of KD (Bajolle et al., 2014).