New biomarkers of Kawasaki disease identified by gingival crevicular fluid proteomics

Abstract

Introduction: Kawasaki disease (KD) is an acute systemic vasculitis that primarily affects coronary arteries, and delayed diagnosis increases the risk of cardiovascular complications. Biomarkers are essential for improving diagnostic accuracy, especially in atypical cases. Gingival crevicular fluid (GCF), derived from periodontal tissues, contains serum components and inflammatory mediators, and has emerged as a valuable biofluid for systemic disease diagnosis. Previous studies suggest GCF protein profiles reflect immune status and metabolic disorders, such as type 2 diabetes. Given the immune-related nature of KD, GCF protein composition may also be altered, yet no studies have systematically explored GCF biomarkers in KD. This study uses DIA and MRM-MS proteomics to identify potential GCF biomarkers for KD diagnosis.

Methods: Twenty-seven patients with KD were enrolled in this study, and 18 healthy volunteers were recruited as the control group. GCF samples were collected from the KD patients, who formed the experimental group, before they received intravenous immunoglobulin treatment. Data-independent acquisition (DIA) quantitative proteomics mass spectrometry was performed on the GCF samples to analyze the protein expression profiles in both groups. DEPs were identified and subjected to functional enrichment analysis using KEGG and GO. Protein-protein interaction (PPI) analysis was conducted for all detected DEPs. Finally, multiple reaction monitoring mass spectrometry (MRM-MS) was used to validate the selected DEPs.

Results: A total of 197 DEPs were identified in GCF between the KD group and the normal control group, with 174 upregulated and 23 downregulated proteins. Functional enrichment analysis revealed that cellular and metabolic processes were the most significantly altered biological processes, while binding and catalytic activity were the most affected molecular functions. Pathway analysis further highlighted the NOD-like receptor signaling pathway, protein processing in the endoplasmic reticulum, and the influenza pathway as the most significantly enriched pathways. In the PPI network, EIF2AK2, B2M, and GBP1 were identified as key hub proteins, suggesting their potential regulatory roles in KD pathophysiology. Finally, MRM-MS confirmed the expression patterns of 12 DEPs (IFIT3, UB2L6, HP, A1AT, HSP90AA1, HNRPC, HSP90AB1, SAA1, MX1, B2M, FKBP4, and TRAP1), thereby demonstrating high consistency with the DIA results and further validating the DEPs' potential as biomarkers for KD.

Conclusion: Our findings suggest that 12 proteins in GCF could serve as potential biomarkers for the early diagnosis of KD. Additionally, the molecular analysis revealed a close association between KD and gingival inflammation, offering new insights into KD's pathophysiology and potential directions for improved diagnosis and treatment.

Keywords: Kawasaki disease; bioinformatics analysis; coronary artery lesions; gingival crevicular fluid; proteomics.

FIGURE 1

Repeats steadity and differentially expressed proteins. (A) Pearson coeffiency test of biological repeats. (B) Volcano plot of differential proteins. Fold change (absolute value of fold change) >2 was the standard screening differential protein, based on p < 0.05. Differential proteins are significantly upregulated in the red part and significantly downregulated in the green part of the figure.

FIGURE 2

GO enrichment of DEPs.GO enrichment plot of differential proteins. The horizontal axis is the number of differential proteins, and the vertical axis is the GO-enriched entries.

FIGURE 3

Pathway enrichment of all DEPs. (A) Cluster plot of differential protein expression. The horizontal axis is the pathway name and the vertical axis is the number of DEPs found in the KD group. (B) Pathway enrichment of differential proteins. The horizontal axis is the group name and the vertical axis is the protein ID of the differential protein. The legend on the right shows the metrics for the differential proteins (converted from p-values to fold change, not added in parentheses). In the figure, the horizontal axis is the number of differential proteins and the vertical axis is the pathway enrichment entries.

FIGURE 4

Differential protein interaction network. Red circles indicate protein upregulation and blue circles indicate protein downregulation. The degree of denseness of the junction line represents the degree of tightness of the relationship between this protein and other differential proteins.

FIGURE 5

Correlation plot between the expression of alternative protein markers detected by MRM and DIA.