| Abstrakt: |
Kawasaki disease (KD) is an immune-response disorder with unknown etiology. KD is an acute systemic immune vasculitis caused by infectious factors that can be complicated by coronary artery lesions. Innate immune cells are closely associated with KD onset, but we know little regarding the expression of immunity-related genes (IRGs) and the possible immune regulatory mechanisms involved in KD. In this study, we analyzed public single-cell RNA sequencing (scRNA-seq) and microarray data of peripheral blood mononuclear cells from normal controls and KD patients. The results of scRNA-seq revealed myeloid cells, T cells, B cells, NK cells, erythrocytes, platelets, plasma cells, hematopoietic stem cells, and progenitor cells in the peripheral blood of patients with KD. In particular, myeloid cells were expanded and heterogeneous. Further analysis of the myeloid cell population revealed that monocytes in KD exhibited higher expression of the inflammatory genes S100A8, S100A9, and S100A12; furthermore, CD14+CD16+ monocyte clusters were associated with inflammatory responses. Microarray data revealed that activation of the innate immune response contributed to KD development and progression. Differential expression and weighted gene coexpression network analysis identified 48 differentially expressed IRGs associated with response to intravenous immunoglobulin, currently the most effective treatment of KD, although numerous patients are resistant. Protein–protein interaction analysis identified ten hub genes (IL1R1, SOCS3, IL1R2, TLR8, IL1RN, CCR1, IL1B, IL4R, IL10RB, and IFNGR1) among the IRGs. In addition, the expressions of IL1R1, SOCS3, CCR1, IL1B, and IL10RB were validated in Chinese KD patients using the real-time reverse transcriptase-polymerase chain reaction. Finally, we found that the neutrophil/lymphocyte ratio could be used as a biomarker to predict responsiveness to intravenous immunoglobulin in KD. In conclusion, our data highlight the importance of innate immunity in KD pathogenesis and its potential in predicting treatment response. [ABSTRACT FROM AUTHOR] |
