Upregulated Expression of IL2RB Causes Disorder of Immune Microenvironment in Patients with Kawasaki Disease

Abstract

Aims: The clinical diagnosis of Kawasaki disease (KD) is not easy because of many atypical manifestations. This study is aimed at finding potential diagnostic markers and therapeutic targets for KD and analysing their correlation with immune cell infiltrations.

Methods: First, we downloaded the KD dataset from the Gene Expression Omnibus (GEO) database and used R software to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then, CIBERSORT algorithm was used to evaluate immune cell infiltrations in samples. Coexpression analysis between DEGs and infiltrating immune cells was performed to screen the main infiltrating immune cells. Subsequently, the least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to screen the core genes related to KD. Finally, correlation analysis between the core genes and the main infiltrating immune cells was performed.

Results: 327 DEGs were screened out in this study. Among them, 72 shared genes were the category of genes most likely to be disease-causing for they did not change before and after treatment. After analysis, it was found that expression level of IL2RB in KD tissues was significantly upregulated, the number of resting CD4+ memory T cells was decreased, and the decrease was significantly negatively correlated with the upregulated expression of IL2RB. Therefore, it was speculated that the upregulated expression of IL2RB disrupted Th1/Th2 cell differentiation balance, which led to a decrease of resting CD4+ memory T cells and finally caused disorder of immune microenvironment in patients with KD.

Conclusions: Upregulated expression of IL2RB leads to disorder of immune microenvironment in patients with KD and eventually causes the occurrence and development of KD. Therefore, IL2RB may serve as a diagnostic marker and potential therapeutic target for KD.

Figure 1

Data quality assessment, differentially expressed gene (DEG) extraction and subgroup analysis. (a) Workflow of this study. (b) Principal component analysis (PCA) of Kawasaki disease (KD) case group vs. control samples based on normalized gene expression level in the GSE64486 dataset. (c) The cluster heat map of the whole DEGs (including DEGs1 and DEGs2). DEGs1 were extracted from the comparison between the untreated group and the control group, and DEGs2 were extracted from the comparison between the treated group and the control group. (d, e) The volcano maps of DEGs1 and DEGs2. (f) The number of DEGs. The number of upregulated and downregulated DEGs was showed in bar plot. (g) The Venn diagram showing the overlapping genes between DEGs1 and DEGs2. (h) Three-dimensional PCA of these three subtypes of genes.

Figure 2

Functional annotations of differentially expressed genes (DEGs). (a) Top 16 most enriched Gene Ontology (GO) terms of DEGs. (b) Major genes and important functional modules were shown in protein-protein interaction (PPI) network. (c) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed the KEGG signaling pathways and key genes.

Figure 3

Immune cell infiltration analysis of Kawasaki disease (KD) vs. control group tissues. (a) Fractions of cell type estimated by CIBERSORT algorithm in each sample. (b) Principal component analysis (PCA) of immune score data based on different subgroups. (c) Correlation heat map of the 22 subtypes of immune cells. (d, e) The boxplot showed the difference in the number of infiltrating immune cells between case and control groups. Immune cells with statistically significant differences in the number of infiltrations were highlighted in red. ∗∗ represents P < 0.01, and ∗ represents P < 0.05.

Figure 4

Screening and verification of diagnostic markers for patients with Kawasaki disease (KD). (a) Coexpression analysis of DEGs and immune cell populations. (b) CV (coefficient of variation) statistical graph showed the optimal lambda (λ, dotted line on the left). (c) Regression model built by the optimal λ screened 15 core genes related to KD. (d) The chord diagram showed the correlation and their statistical significances between these 15 core genes and the two most important immune cell components. (e) The box plot showed the differences of IL2RB expression between the case groups and the control group. Kruskal–Wallis tests was used to analyse the significance. (f) Correlation analysis between IL2RB expression and the number of resting CD4+ memory T cells. ∗∗∗ represents P < 0.001, ∗∗ represents P < 0.01, ∗ represents P < 0.05, and ns represents P > 0.05.

Figure 5

Schematic diagram of the mechanism.