Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and treatment

Jong Gyun Ahn; Insoo Kang

doi:10.3345/cep.2025.02901

Clin Exp Pediatr > Volume 69(3); 2026

Ahn and Kang: Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and treatment

Review Article

Immunology

Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and treatment

Jong Gyun Ahn, MD, PhD^1,^2,³

, Insoo Kang, MD³

¹Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, Seoul, Korea

²Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Korea

³Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA

Corresponding author: Jong Gyun Ahn, MD, PhD. Department of Pediatrics, Severance Children’s Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea Email: JGAHN@yuhs.ac

Co-corresponding Author: Insoo Kang, MD. S525C TAC Section of Rheumatology, Allergy & Immunology, Department of Internal Medicine, Yale School of Medicine, 300 Cedar Street, New Haven, CT 06520, USA Email: insoo.kang@yale.edu

Received December 9, 2025 Revised January 7, 2026 Accepted January 9, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Clinical and Experimental Pediatrics 2026;69(3):197-210.

Published online: February 25, 2026

DOI: https://doi.org/10.3345/cep.2025.02901

Abstract

Kawasaki disease (KD) is an acute febrile vasculitis and the leading cause of acquired heart disease in children. Despite decades of research, the etiology remains unknown and key mechanisms linking systemic inflammation to coronary artery lesions are incompletely defined. High-throughput technologies—including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and immunomics—have enabled systems-level profiling of KD and highlighted reproducible inflammatory and vascular pathways. Multiomics integration increasingly supports convergent mechanistic axes, particularly interleukin (IL-1/IL-6–neutrophil programs, Fcγ-receptor signaling related to intravenous immunoglobulin (IVIG) pharmacodynamics, Ca²⁺/nuclear factor of activated T cells-dependent T-cell activation, and endothelial/extracellular matrix remodeling associated with coronary outcomes. While these findings provide a robust framework for biomarker discovery and therapeutic hypothesis generation, most signatures remain investigational and require prospective validation, standardized sampling (pre-/post-IVIG), and clinically scalable assays before routine implementation. This review summarizes current multiomics applications in KD, prioritizes the most consistently supported pathways, and outlines a pragmatic roadmap toward clinically useful risk stratification, disease monitoring, and outcome prediction.

Key words: Kawasaki disease, Multiomics, Biomarkers, Risk stratification

Introduction

Kawasaki disease (KD) is an acute, self-limited systemic vasculitis that predominantly affects children under 5 years of age. Despite timely intravenous immunoglobulin (IVIG) therapy significantly reduces the risk of coronary artery lesions (CALs), the underlying etiology and molecular mechanisms remain incompletely understood. Traditional immunological and clinical studies have provided important insights into KD pathogenesis; however, they often fail to capture the disease’s multi-layered molecular complexity spanning genetic, immunologic, metabolic, and environmental dimensions.

The advent of high-throughput multiomics technologies— including genomics, transcriptomics, proteomics, metabolomics, and epigenomics—has enabled comprehensive, systems-level analyses that link molecular alterations to immune dysregulation and vascular injury. By integrating diverse omics layers, researchers can now trace the biological cascade from genetic susceptibility to immune activation and endothelial dysfunction. This holistic approach offers a powerful framework for identifying biomarkers and redefining disease subtypes in KD. At present, however, most multiomics findings primarily provide mechanistic and hypothesis-generating insights, serving as a foundation for future translational studies rather than enabling immediate clinical implementation.

Overview of multiomics technologies in KD

1. Genomics

1) Genetic susceptibility and population specificity

Genomic investigations have begun to refine the concept of KD as an idiopathic disease with underlying polygenic susceptibility, in which host genetic background interacts with environmental triggers. Although the precise cause remains unknown, susceptibility patterns—such as higher incidence in East Asian populations and familial aggregation— suggest contributions from population-enriched alleles alongside shared environmental exposures [1].

Genome-wide association studies (GWAS) have identified reproducible susceptibility loci converging on immune and vascular pathways. The earliest and most robust finding involves inositol 1,4,5-trisphosphate 3-kinase C (ITPKC), where a functional single nucleotide polymorphism (rs28493229) alters splicing efficiency and the Ca²⁺/nuclear factor of activated T cells (Ca²⁺/NFAT) signaling pathway, augmenting T-cell activation and inflammasome/IL-1 responses, thereby conferring increased aneurysm risk [2-4]. Variants in Fc gamma receptor IIa (FCGR2A) (rs1801274) implicate Fcγ-mediated IgG interactions in KD pathogenesis and IVIG pharmacodynamics. However, large-scale studies indicate that while FCGR2A/2B/3 polymorphisms influence disease susceptibility, they do not consistently predict IVIG resistance or coronary outcomes [5-8)].

Additional loci—including caspase 3 (CASP3) (affecting NFAT binding and apoptosis regulation) [9-11], B lymphoid tyrosine kinase (BLK), CD40, human leukocyte antigen (HLA) class II genes, and vascular remodeling factors such as matrix metalloproteinase (MMP) haplotypes, angiopoietin-1 (ANGPT1), and vasoactive endothelial growth factor A (VEGFA)—underscore the interplay between adaptive immunity and endothelial integrity [1,12-14]. Genetic variations in chemokine and transporter genes (CC chemokine receptor 5 [CCR5], CC chemokine ligand 3-like 1 [CCL3L1], ATP-binding cassette subfamily C member 4 [ABCC4]) further supports the involvement of leukocyte trafficking and vascular homeostasis [15,16]. Collectively, these loci define overlapping pathogenic axes encompassing immune hyperreactivity (Ca²⁺/NFAT, Fcγ, IL-1), adaptive immunity (B/T-cell activation, HLA), and vascular stability.

2) Rare variants and pharmacogenomics

Rare-variant analyses from whole-exome and whole-genome sequencing (WES/WGS) extend these insights by identifying alleles enriched in patients with severe vascular phenotypes, particularly CAL. Variants in nebulette sarcomeric isoform (NEBL), tubulin, alpha 3c (TUBA3C), tumor necrosis factor receptor-associated factor 5 (TRAF5), and MAM domain containing GPI anchor 1/2 (MDGA1/2)—genes linked to vascular or cytoskeletal structure—suggest that determinants of aneurysm formation may be distinct from those driving overall disease susceptibility [17].

Pharmacogenomic WGS studies in multiancestry cohorts have also revealed candidate variants associated with IVIG nonresponsiveness, such as fibronectin type III and ankyrin repeat domains 1 (FANK1), mitogen-activated protein kinase kinase 3:potassium inwardly rectifying channel subfamily J member 12 (MAP2K3:KCNJ12), mannosidase alpha class 1A member 2 (MAN1A2), endothelin-1 (EDN1), and Scm-like with 4 MBT domains 2 (SFMBT2), underscoring the polygenic and ancestry-dependent nature of treatment outcomes [18].

3) Population and ancestry-specific effects

Population specificity remains a critical theme. Many initial discoveries arose from East Asian cohorts, where KD incidence is highest, with loci such as ITPKC showing stronger effect sizes in these populations [3]. Others, including FCGR2A and HLA class II, replicate across ancestries but with variable effect sizes or allele frequencies [8,13]. Recent European and multi-ethnic GWAS/WES efforts have confirmed core KD susceptibility loci (such as ITPKC and FCGR2A) and in some cases uncovered ancestry-specific risk variants, with many of these genes implicated in immune regulation [2,8,10,19]. These findings emphasize the need for larger, ethnically diverse datasets and fine-mapping that account for local linkage disequilibrium and allele frequency differences.

4) Translational implications

From a translational standpoint, genomics provides new opportunities for precision medicine in KD. Genotype-informed risk stratification may complement existing clinical scoring systems (e.g., Kobayashi, Egami, Sano), which demonstrate high predictive accuracy in Japanese cohorts but perform less effectively in North American populations [20,21]. Pharmacogenomic markers hold promise for identifying IVIG nonresponders, though single-gene predictors remain insufficient. Integrative approaches combining genomic, transcriptomic, and proteomic features are more likely to yield clinically actionable models.

Notably, convergent genetic evidence implicating the Ca²⁺/NFAT and IL-1 signaling pathways—especially via ITPKC and related gene variants—supports the rationale for targeted adjunctive therapies such as calcineurin inhibitors or IL-1 blockade in genetically defined high-risk subgroups [22,23].

2. Transcriptomics

1) Bulk transcriptomic insights into innate immune activation

Transcriptomic profiling has substantially deepened our understanding of KD immunopathogenesis. Bulk RNA-seq analyses consistently reveal a dominant innate immune signature during the acute phase, characterized by marked up-regulation of neutrophil-associated genes and IL-1/IL-6 signaling pathways, with interferon-stimulated genes variably enriched across cohorts [24-26]. Modules related to neutrophil degranulation, protease activity, and S100 family members robustly distinguish KD from other febrile illnesses and correlate with systemic inflammation and CAL risk [27,28]. Meta-analyses of bulk RNA-seq across human and murine datasets confirm that acute KD is driven by innate immune activation, neutrophil effector programs, and IL-1/IL-6 signaling enrichment [29].

2) Single-cell RNA sequencing reveals immune heterogeneity

Recent advances in single-cell RNA sequencing (scRNA-seq) have refined the observations from bulk RNA-seq analyses by resolving cellular heterogeneity in KD. Early scRNA-seq studies on peripheral blood demonstrated excessive neutrophil activation and innate immune dysregulation compared with controls [30], while analyses of IVIG-resistant patients revealed immune shifts associated with CAL formation [31]. A landmark study in human peripheral blood mononuclear cell (PBMC) further showed that monocytes display hyperinflammatory signatures with reduced antigen-presentation capacity during the acute phase, suggesting coordinated crosstalk among monocytes, neutrophils, and T cells [32].

Integration of scRNA-seq and bulk RNA-seq further revealed alterations in T, B, and natural killer cell compartments, including shifts in T-cell differentiation trajectories toward regulatory or alternative states and aberrant activation of mTOR and infection-response pathways in multiple immune subsets [33]. In CAL patients, scRNA-seq profiling of over 200,000 PBMCs identified distinct inflammatory immune subpopulations differentiating CAL from non-CAL cases, suggesting that peripheral immune states mirror coronary vascular injury predisposition [34].

3) Meta-analytic convergence and shared neutrophil programs

A recent large-scale, multicohort single-cell meta-analysis integrating over 500,000 immune transcriptomes from KD, multisystem inflammatory syndrome in children (MIS-C), and healthy controls identified a conserved CD177⁺ neutrophil effector program—defined by genes mediating degranulation, reactive oxygen species production, leukocyte transmigration, and neutrophil extracellular trap (NET) formation—with the transcription factor SPI1 implicated as a master regulator [35]. This discovery establishes a unifying framework connecting KD and MIS-C pathogenesis through shared neutrophil activation mechanisms.

4) T-cell remodeling and adaptive immune reprogramming

Beyond innate immunity, single-cell and integrative analyses increasingly highlight T-cell remodeling in KD. Enhanced activation of CD8⁺ cytotoxic T cells has been consistently observed [32], together with an imbalance between Th17 and regulatory T (Treg) cells. Most studies report an increase in IL-17A–producing Th17 cells accompanied by a relative reduction or functional impairment of Tregs [36], suggesting a shift toward a proinflammatory adaptive immune milieu. In a transcriptome-profiling study of KD coronary arteries, up-regulated pathways included T lymphocyte activation and type I interferon response, consistent with cytotoxic T-cell involvement [26]. In parallel, circulating levels of chemokines such as CXCL10 (IP-10) are elevated in acute KD, potentially promoting recruitment of effector T cells to inflamed vascular tissues [37]. Together, these findings suggest that, in addition to neutrophil-driven inflammation, adaptive immune reprogramming contributes to vascular pathology and potentially to repair processes depending on disease stage and treatment status.

5) Emerging spatial transcriptomics and vascular microenvironments

Spatial transcriptomic approaches in KD remain in their infancy, but proof-of-concept studies in murine models are emerging. A recent study employing spatial transcriptomics combined with scRNA-seq in a mouse vasculitis model revealed focal immune-cell infiltration and up-regulation of IL-1β/IL-18 signaling within vascular lesions [38]. Similarly, spatial transcriptomic analysis of coronary tissue from the Lactobacillus casei cell wall extract (LCWE) mouse model (GSE178799) demonstrated localized gradients of inflammatory transcripts, illustrating how spatially restricted immune–endothelial crosstalk may drive coronary artery injury.

Although human studies remain limited due to scarce coronary tissue availability, the ongoing development of high-resolution and minimally invasive spatial profiling technologies holds great promise for elucidating the vascular immune microenvironment in KD.

6) Integrative perspective and future directions

Collectively, bulk, single-cell, and spatial transcriptomic analyses converge on a model in which KD is driven by neutrophil hyperactivation, adaptive immune remodeling, and localized vascular inflammation. The conserved CD177⁺ neutrophil program establishes a mechanistic link between systemic cytokine responses and vascular injury, while insights into T-cell dynamics and nascent spatial mapping underscore the immunovascular microenvironment as a critical pathogenic nexus. Future work integrating transcriptomic data with immune receptor sequencing, proteomics, and epigenomics will help delineate cellular circuits underlying KD and advance precision immunomodulatory strategies.

3. Proteomics

1) Rationale — why proteomics matters in KD

Genomics and transcriptomics define susceptibility and transcriptional programs, but proteomics measures the actual effector molecules mediating inflammation, endothelial injury, and tissue remodeling. Mass-spectrometry (MS)-based discovery proteomics and affinity-based high throughput targeted proteomics platforms (e.g., Olink, SomaScan) now permit unbiased quantitation of hundreds-to-thousands of circulating proteins, enabling (1) identification of mechanistic effectors that bridge blood signatures to vascular pathology, (2) discovery of candidate biomarkers for diagnosis/IVIG response/CAL risk, and (3) evaluation of pathway activity for therapeutic targeting. Recent reviews summarize this shift toward MS- and affinity-based proteomics in pediatric inflammatory syndromes, including KD [39,40].

2) Acute-phase proteomic signatures: neutrophil and endothelial effectors

Unbiased proteomic analyses of KD plasma/serum repeatedly show up-regulation of neutrophil-derived proteins (S100A8/A9/A12, myeloperoxidase, neutrophil elastase) and acute-phase reactants (c-reactive protein, serum amyloid A), mirroring transcriptomic neutrophil signatures and supporting a central role for neutrophil activation in acute KD. Targeted/label-free MS and iTRAQ (isobaric tags for relative and absolute quantitation) studies have specifically identified S100A8/A9/A12 among top discriminating proteins which are linked to later coronary involvement in some cohorts [41].

Endothelial and coagulopathy-related proteins—including soluble adhesion molecules, von Willebrand factor, and serine protease inhibitor clade E member 1 (SERPINE1, also known as plasminogen activator inhibitor-1 [PAI-1])—are also elevated in acute KD, implicating early endothelial dysfunction and thrombogenic signaling. Notably, recent proteomic analyses identified SERPINE1 as a candidate predictor of CAL formation [42].

3) Proteomic correlates of IVIG response

Prediction of IVIG resistance remains a major clinical challenge. Comparative proteomic studies reveal that IVIG nonresponders exhibit higher pretreatment levels of neutrophil-derived inflammatory proteins (S100A8/A9, MRP8/14), proteases (neutrophil elastase), and complement/Fc-binding components—signatures consistent with exaggerated innate activation. Integrating proteomic markers with clinical variables significantly improves discrimination of IVIG nonresponse compared with clinical scores alone [41,43].

4) CAL-associated proteomic markers

Proteomic profiling implicates extracellular matrix-remodeling enzymes (MMP-8, MMP-9, cathepsins) and endothelial injury markers in CAL-positive patients, supporting a model of proteolytic degradation and aberrant angiogenic signaling in aneurysm formation. Persistent elevation of vascular injury-related proteins after IVIG treatment suggests incomplete resolution of endothelial inflammation and their potential utility as prognostic biomarkers or therapeutic targets [44,45].

5) Pathway and network interpretation

Pathway and network analyses of KD proteomes reveal three interacting functional modules: (1) Innate immune activation—neutrophil degranulation, complement activation, S100-driven inflammation. (2) Endothelial dysfunction and remodeling—adhesion molecules, coagulation regulators (SERPINE1), and MMP-mediated extracellular matrix degradation. (3) Metabolic rewiring—altered glycolytic and fatty acid oxidation enzymes linking immune-cell energetics to effector activity. These network hubs (e.g., S100 proteins, MMPs, SERPINE1) represent mechanistic candidates for targeted modulation and for development into multiplex biomarker panels [42,44].

6) Integration with other omics

Cross-omic comparisons reveal both concordant and discordant patterns between transcript and protein levels, underscoring the influence of posttranscriptional regulation and secretion dynamics. Proteogenomic approaches connect KD susceptibility loci (e.g., FCGR2A, ITPKC) to downstream protein networks, while combined proteome-metabolome analyses delineate coherent immune-metabolic phenotypes associated with IVIG resistance and vascular remodeling [29]. This integrative framework provides mechanistic links between genetic predisposition, cytokine signaling, and effector protein expression.

7) Emerging platforms and translational roadmap

Recent high-plex proteomic technologies are expanding translational potential in KD research.

The Olink platform identified IL-17A as a candidate biomarker differentiating KD from other febrile illnesses, highlighting an additional cytokine axis complementing neutrophil- and IL-1–dominated signatures [46]. Indeed, IL-17 is known to promote neutrophil-mediated immune responses [47]. Similarly, SomaScan proteomic profiling revealed differential abundance of hundreds of proteins across KD subgroups, capturing molecular and clinical heterogeneity within the disease [48]. Together, these technologies bridge proteomics and clinical translation, enabling high-throughput, standardized protein quantification. When integrated with genomic, transcriptomic, and metabolomic data, such proteomic frameworks can refine patient stratification, enhance biomarker-driven diagnostics, and accelerate the development of precision therapeutics for KD.

4. Metabolomics

1) Metabolomics as the functional readout

Metabolomics serves as the most proximate readout of cellular function, capturing the small-molecule substrates, intermediates, and products of biochemical pathways. In the context of KD, it provides a bridge from upstream genomic and transcriptomic perturbations to downstream effector metabolism, particularly in immune and vascular cells.

2) Lipid remodeling: LPC/LPE depletion and mechanistic hypotheses

One reproducible metabolomic observation in KD is decreased circulating lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) species in the acute phase [49,50]. Mechanistically, a plausible explanation is inflammation-driven activation of phospholipase A2 (PLA₂), which hydrolyzes membrane phospholipids to generate lysophospholipids and free fatty acids (including arachidonic acid) that feed proinflammatory eicosanoid and LPA pathways [51,52]. Altered LPC/LPE pools can influence endothelial and immune-cell function directly—for example by modulating receptor signaling platforms, promoting leukocyte recruitment, and shifting the balance of pro- versus anti-inflammatory lipid mediators—as reviewed for LPC biology [53]. Taken together, LPC/LPE depletion in KD likely reflects both downstream consequences of oxidative/inflammatory lipid remodeling and upstream contributions to vascular inflammation, although direct demonstration of PLA₂ overactivation in human KD remains to be established.

3) Amino acid and energy metabolism: acylcarnitines and tryptophan-kynurenine axis

Although studies on acylcarnitine changes in KD are limited, acylcarnitine dysregulation is a well-characterized marker of mitochondrial β-oxidation dysfunction in multiple disease contexts [54]. Plasma metabolomics in patients with systemic inflammation frequently reflect altered acylcarnitine species, suggesting immune bioenergetic stress in KD as well.

In parallel, perturbations in other amino acid metabolic pathways have been observed, notably in the arginine-nitric oxide (NO) pathway [55]. Disrupted arginine metabolism, which is pivotal for endothelial NO production, may contribute to endothelial dysfunction and vascular inflammation in KD, consistent with its vasculitic phenotype.

In addition, untargeted metabolomic profiling has demonstrated altered tryptophan metabolism in KD, with reduced tryptophan and increased kynurenine/kynurenic acid levels in patient cohorts [56]. This supports the involvement of the tryptophan-kynurenine axis, which is closely linked to cytokine-driven immune regulation through indoleamine 2,3-dioxygenase 1 (IDO1) activity. The exact role of this pathway in the pathogenesis of KD is yet to be determined since kynurenine is known to suppress inflammation and T-cell immune responses [57]. Increased levels of kynurenine in KD could be a counterregulatory response to increased inflammation.

Finally, bile acid metabolic alterations have been identified in KD, implicating dysregulation of the gut-liver-immune axis [58]. These findings raise the possibility that microbiome-derived metabolites modulate systemic inflammation, vascular injury, and treatment response variability.

4) Cross-study consistency and technical caveats

KD metabolomic studies employ various platforms (GCMS, LC-MS/MS, lipidomics pipelines, targeted acylcarnitine assays), and heterogeneity in sample type (serum vs. plasma), fasting status, timing relative to fever onset or IVIG, and normalization strategies introduces variability. Nonetheless, consistent pathway-level themes—lipid remodeling, mitochondrial stress, tryptophan metabolism—emerge across studies, reinforcing their biological relevance. Future work must standardize protocols, ensure replication in independent cohorts, and carefully control confounding factors.

5) Clinical implications, limitations, and future directions

Metabolomic signatures hold promise as early biomarkers for IVIG resistance and CAL risk, and may suggest adjunctive metabolic therapies (e.g., antioxidant, mitochondrial modulators, IDO pathway modulators). However, present evidence is still exploratory, with most studies constrained by small sample sizes, single-center design, and lack of prospective validation [59].

Looking ahead, a major frontier is single-cell metabolomics: just as scRNA-seq resolved cellular heterogeneity, future single-cell or spatial metabolomic methods could directly quantify metabolic states in hyperactivated neutrophils, IL-1 high monocytes, or endothelial cells. Such technology would enable linking cellular bioenergetics to transcriptomic and proteomic phenotypes in KD. Integrative metabolomics combined with immune receptor sequencing, proteomics, and transcriptomics can delineate causal pathways from genotype to phenotype, refine mechanistic targets, and guide precision immunometabolic therapy in KD.

5. Epigenomics

Epigenetic mechanisms—including DNA methylation, noncoding RNAs (microRNAs [miRNAs], long noncoding RNAs [lncRNAs], and exosomal RNAs), and chromatin-level regulation—provide a dynamic interface between genetic susceptibility, environmental exposures (including infectious triggers), and immune activation in KD. Unlike static genetic variants, epigenetic modifications are reversible and responsive to inflammatory cues, thereby shaping both acute immune responses and longer-term vascular outcomes.

1) DNA methylation: dominant and reproducible epigenetic alterations

Genome-wide methylation studies consistently demonstrate widespread DNA methylation changes during acute KD, particularly within immune-related loci such as FCGR2A/B, FCER1A, and inflammasome-associated genes (e.g., NLRC4, NLRP3) [60,61]. Hypomethylation of neutrophil and inflammasome genes correlates with heightened innate immune activation, whereas hypermethylation at T-cell regulatory loci parallels transcriptomic evidence of lymphocyte suppression [62,63].

Altered expression of DNA methyltransferases (DNMT1, DNMT3A) has been linked to CAL formation [64], suggesting transient global hypomethylation driven by systemic inflammation. Cytokine signaling pathways—particularly IL-6–STAT3—may directly suppress DNMT activity, reinforcing a cytokine-epigenetic feedback loop in acute KD [65].

2) Noncoding RNAs: circulating and cell-associated regulatory signals

Changes in miRNA expression represent another reproducible epigenetic feature of KD. Multiple studies report differential expression of miR-223, miR-145, miR-155, and miR-92a during the acute phase, with partial normalization after IVIG treatment. These miRNAs are implicated in toll-like receptor/nuclear factor kappa B signaling, neutrophil and monocyte activation, endothelial responses, and treatment responsiveness [61,66].

Exosomal miRNAs and lncRNAs have emerged as potential mediators of immune-vascular communication, acting in a paracrine manner to regulate endothelial activation, leukocyte adhesion, and vascular permeability [67,68]. However, heterogeneity in profiling platforms and limited cohort sizes currently restrict their immediate clinical utility, emphasizing the need for standardized and multicenter validation.

3) Histone modifications and chromatin remodeling (emerging layer)

Evidence for histone-level regulation in KD remains limited and largely exploratory. Preliminary reports suggest altered histone acetylation (H3K27ac) and methylation (H3K4me3) at inflammatory loci, implying that chromatin accessibility may contribute to sustained transcriptional activation beyond the acute cytokine surge [69,70]. Advanced approaches such as ChIP-seq, CUT&Tag, and scATAC-seq hold promise for resolving cell-type–specific chromatin landscapes in immune and vascular compartments, but currently represent an emerging research direction rather than an established pathogenic framework.

4) IVIG as an epigenetic modulator

IVIG therapy induces substantial epigenetic remodeling, in some cases exceeding changes observed at disease onset [60,63]. These include normalization of hypomethylated inflammatory loci and partial reversal of neutrophil-associated epigenetic signatures, suggesting that IVIG exerts durable immunomodulatory effects partly through epigenetic reprogramming rather than solely through passive immune complex neutralization.

5) Translational perspective and limitations

Integrative analyses indicate that epigenetic alterations may stabilize acute immune dysregulation into longer-lasting inflammatory or vascular phenotypes. Differentially methylated regions and circulating miRNA panels therefore represent candidate biomarkers for disease activity, IVIG response, and CAL risk. Nevertheless, current evidence is constrained by bulk-tissue analyses, small sample sizes, and limited longitudinal sampling. Future priorities include single-cell or spatial epigenomic profiling, pre- and post-IVIG longitudinal designs, and functional validation using epigenome-editing or induced pluripotent stem cell-derived vascular and immune models. At present, epigenomics in KD primarily provides mechanistic and hypothesis-generating insights that complement genomic and transcriptomic findings.

6. Immunomics

Immunomics—encompassing immune-cell composition, receptor repertoires (TCR/BCR), and soluble immune mediators at high dimensionality—provides an integrated framework to decipher the immune dysregulation underlying KD. High-dimensional technologies such as single-cell transcriptomics with paired TCR/BCR sequencing, mass cytometry (CyTOF), and cytokine proteomics have revealed both conserved and patient-specific immune states that connect systemic inflammation to vascular injury, IVIG resistance, and treatment response heterogeneity.

1) Innate immune drivers: neutrophils and monocytes

Across single-cell and bulk datasets, neutrophil hyperactivation is a consistent hallmark of acute KD. Expansion of CD177⁺ neutrophil subsets with degranulation, reactive oxygen species/neutrophil extracellular traps (ROS/NET) formation, and endothelial-homing signatures defines a shared neutrophil effector program observed in KD and MIS-C. This program attenuates after IVIG treatment and correlates with clinical inflammation and coronary artery involvement [35].

Monocytes, particularly classical CD14++CD16− and CD14+CD16+ intermediate subsets, display proinflammatory and inflammasome-related transcriptomes with diminished antigen-presentation programs, indicating a shift toward innate effector function. In infants with KD, scRNA-seq and flow-cytometric studies have shown expansion of SELL+CD14+CD16− classical monocytes with neutrophil-activating gene signatures, as well as an increase in CD14+CD16+ intermediate monocytes during the acute phase of disease [71-73]. Intercellular network analyses from single-cell PBMC data position these monocytes as central communication hubs linking neutrophils, endothelial cells, and adaptive lymphocytes [32,74].

Collectively, these innate immune programs provide both dynamic biomarkers for monitoring disease activity and IVIG response, and a clear mechanistic rationale for targeting innate cytokines (e.g., IL-1) and neutrophil effectors in refractory KD [35,75].

2) Adaptive immunity: T-cell remodeling and TCR features

Single-cell analyses have uncovered extensive T-cell remodeling in KD, with perturbed CD4/CD8 ratios, expansion of cytotoxic CD8⁺ T cells, and disrupted Th17/Treg balance. Paired scRNA–TCR sequencing demonstrates clonal expansions and a higher-than-expected frequency of dual-TCR T cells, which express two different TCRs, suggesting antigen-driven or superantigen-like responses in subsets of patients [32,76]. These findings implicate antigen-specific T-cell activation as a key driver of vascular inflammation, while interindividual heterogeneity underscores the importance of immune context and genetic background.

3) B cells, plasmablasts and antibody responses

Proteomic and repertoire analyses show transient plasmablast expansion during acute KD and a polyclonal activation pattern across B-cell receptors. Although convergent clonotypes have not been consistently observed, some studies have reported biased usage of immunoglobulin heavy chain variable gene families.

The potential contribution of anticytokine or antiendothelial autoantibodies—either endogenously produced or passively transferred via IVIG—remains under investigation. Understanding these transient humoral phenomena may help explain variable therapeutic responses and disease phenotypes [77].

4) Soluble immunomics: cytokine and proteomic signatures

High-plex proteomic profiling consistently highlights IL-1 and IL-6 pathway activation in acute KD, along with emerging evidence for IL-17 family cytokines as potential markers of IVIG resistance or coronary risk. Although Th17 cells have been implicated, recent single-cell studies point to innate lymphoid cells as rapid IL-17 producers that bridge innate and adaptive inflammation [78,79].

Elevated IL-1/IL-6 signaling provides a clear rationale for cytokine-targeted therapies, such as anakinra or tocilizumab, though population heterogeneity and sampling variability currently limit universal biomarker application [80,81].

5) Immune receptor repertoires and antigen specificity

Comprehensive TCR/BCR repertoire sequencing in KD remains in early stages but is rapidly advancing with integrated scRNA+TCR/BCR pipelines. Early findings indicate clonal T-cell expansions and biased V-gene usage, supporting an antigen-driven response hypothesis. However, no single public antigenic signature has yet been identified. Rigorous methodology and large, ethnically diverse cohorts will be essential for reproducible discovery [76,82].

6) Multiomic integration and translational implications

Integrative analyses linking immune-cell transcriptomes, cytokine proteomics, and metabolic signatures have started to map the molecular cascade from cytokine activation (e.g., IL-1 → kynurenine pathway induction → endothelial dysfunction) to vascular injury [33].

Composite immune risk scores, incorporating cell-subset frequencies, cytokine profiles, and receptor clonality, show promise for predicting IVIG resistance and CAL risk [33,35]. To ensure clinical utility, these findings must be validated prospectively across diverse populations, with standardized sample timing, paired multi-omic profiling, and harmonized clinical metadata (disease severity, treatment regimens, coronary imaging outcomes). Immunomics thus bridges cellular mechanisms with clinical translation, offering both diagnostic precision and targeted therapeutic guidance for KD [75,83].

7. Microbiomics

Microbiomics examines whether and how microbial communities at mucosal surfaces—primarily the gut and upper airway—are associated with KD susceptibility, immune activation, and vascular injury. At present, evidence from human studies largely supports association rather than direct causation; however, converging observational and experimental findings suggest that microbial dysbiosis and microbe-derived metabolites may modulate immune and vascular responses relevant to KD pathophysiology.

1) Clinical microbiome studies – associative evidence

Case-control and longitudinal studies using 16S rRNA sequencing or shotgun metagenomics have reported alterations in the gut microbiota during the acute phase of KD compared with convalescence or healthy states. These changes are variably associated with markers of systemic inflammation and with alterations in microbial metabolites, including short-chain fatty acids and bile acids, linking microbial metabolism to host immune activation.

Studies of the nasopharyngeal and oral microbiome have similarly described compositional differences in KD, suggesting a potential contribution of upper-airway microbial communities to immune triggering. However, findings across human cohorts are heterogeneous and largely correlative, with substantial variability related to population, sampling site, timing, and analytical methods.

Overall, current clinical microbiome data support an association between dysbiosis and immune activation, rather than a definitive causal role in KD pathogenesis.

Details of reported taxa are summarized in Supplementary Table 1.

2) Insights from animal models

Mechanistic support for microbiome involvement in KDlike vasculitis primarily derives from the LCWE mouse model. In this system, coronary arteritis is driven by IL-1R/MyD88-dependent innate immune pathways, and experimental manipulation of gut microbiota—through antibiotics or probiotic reconstitution—modulates disease severity [23] These studies provide proof-of-principle that microbial composition can influence immune tone and vascular inflammation, although direct extrapolation to human KD remains limited.

3) Putative immune-metabolic mechanisms

Proposed mechanisms linking microbes to KD include immune activation by microbial products and modulation of host immunity by microbial metabolites. Microbiome-derived bile acids, short-chain fatty acids, and tryptophan metabolites can influence IL-1 and IL-6 signaling, immune-cell metabolism, and epigenetic regulation. In addition, increased intestinal permeability during systemic inflammation may enhance exposure to microbial components, promoting neutrophil recruitment and vascular inflammation. Together, these observations support a gut-immune-vascular axis, best interpreted as a modulatory rather than initiating pathway in KD.

4) Limitations and future directions

Microbiome studies in KD are constrained by small sample sizes, regional and methodological heterogeneity, and limited longitudinal data. Importantly, a recent Mendelian randomization analysis did not demonstrate a clear causal relationship between gut microbiota and KD, underscoring the complexity of host-microbe interactions.

Future progress will require well-powered, longitudinal, multiomic studies integrating microbiome, metabolomic, and host immune profiling, alongside functional validation in experimental models. In this context, microbiomics is best viewed as a contextual and modulatory layer that may refine immune stratification or risk prediction, rather than as a primary causal driver of KD.

Integrated multiomics: the molecular cascade of KD

Integrative multiomics seeks to reconstruct the molecular cascade linking inherited susceptibility and environmental triggers to immune activation and endothelial injury, thereby improving biological understanding of IVIG resistance and coronary outcomes. This integrative view enables (1) prioritization of plausible causal genes at GWAS loci, (2) mapping of regulatory effects to specific cell types, (3) elucidation of cross-layer interactions (e.g., cytokine → metabolism → epigenetic remodeling), and (4) development of composite signatures that may support future risk stratification and monitoring [84,85].

A variety of integrative analytical frameworks have been applied in KD to link multiomics data with clinical phenotypes (Supplementary Table 2). In brief, these approaches aim to (1) identify shared inflammatory or immune-metabolic axes across omics layers, (2) derive composite molecular signatures associated with IVIG response or coronary outcomes, and (3) map molecular signals to specific immune or vascular cell populations [84,86-89].

1. Convergent molecular axes in KD

Integrative studies consistently converge on six key mechanistic axes (Table 1): (1) Innate inflammation dominated by IL-1/IL-6-driven neutrophil programs [29,87], (2) Fcγ receptor signaling modulating IVIG pharmacodynamics [8,90], (3) Ca²⁺/NFAT pathway variants (e.g., ITPKC) linking genetic susceptibility to T-cell activation [2,91] (4) Metabolic rewiring involving kynurenine and lipid remodeling under cytokine-driven mitochondrial stress [56,92], (5) Endothelial and ECM remodeling predisposing to coronary aneurysm formation [93,94], and (6) Microbiome-metabolite-immune crosstalk connecting environmental triggers to systemic inflammation and vascular outcomes [58]. These axes collectively depict a systems-level cascade from immune activation to vascular pathology.

2. Integrative framework for mechanistic and therapeutic translation

Recent multiomics research reframes KD as a coordinated immune-metabolic-vascular disorder rather than a purely idiopathic vasculitis. Functional variants such as ITPKC and FCGR2A remain key genetic anchors, shaping T-cell calcium signaling and Fcγ biology respectively [2,8].

Emerging single-cell and factor-analysis approaches are now mapping these cross-layer effects onto defined immune and endothelial populations, uncovering IL-1/IL-6-driven transcriptional programs relevant to neutrophil and vascular activation [86,95].

Epigenetic remodeling appears to underlie disease persistence: DNA methylation and chromatin accessibility changes after acute KD and IVIG have been linked to CAL formation and DNMT downregulation, suggesting that transient cytokine cues may imprint long-lasting inflammatory or vascular remodeling signatures [61,64].

Cross-omic analyses also highlight coupling between cytokine signaling and metabolic reprogramming, including activation of the tryptophan-kynurenine pathway and acylcarnitine perturbations indicative of mitochondrial stress—mechanisms plausibly connecting immune activation to endothelial injury and IVIG resistance [54,56].

Microbiome studies add an environmental dimension: TCR repertoire and scRNA/TCR-seq analyses support superantigen-like microbial activation in subsets of KD, while integrated microbiome-metabolome analyses have linked altered bile acid and tryptophan-derived metabolite pools to inflammatory phenotypes [76,96]. Although current findings remain preliminary, they collectively support a gut-microbe-metabolite-immune axis influencing disease severity and coronary outcomes.

Methodologically, early multiomic classifiers combining transcriptomic, proteomic, and metabolomic features have shown potential for improving prediction of IVIG resistance and coronary complications in research settings. However, larger, multiancestry, longitudinal studies integrating host genetics, immune states, and microbial ecology will be essential to establish causality and enable precision-guided therapies [86,95].

Taken together, converging evidence supports a unified molecular cascade in KD: innate cytokine activation (IL-1/IL-6) drives neutrophil effector expansion and metabolic stress, leading to epigenomic remodeling, endothelial activation, and ECM dysregulation that culminate in coronary pathology. Better understanding of this cascade would provide therapeutic interventions, such as IL-1/IL-6 blockade, metabolic modulation, and Fcγ-axis targeting, laying the groundwork for future precision immunotherapy in KD [35,56].

Clinical translation: what is actionable now, what remains investigational

1. Near-term opportunities (requires prospective validation)

First, multimarker blood-based panels integrating inflammatory proteins and transcripts may improve risk stratification for IVIG resistance beyond existing clinical scores, particularly outside Japanese populations. Second, longitudinal profiling across standardized time points (pre-IVIG, 36- to 48-hour post-IVIG, and early convalescence) can support disease monitoring and early identification of persistent vascular inflammation. Third, combining molecular features with echocardiographic data may enable better prediction of coronary outcomes, including early CAL progression.

2. Research-stage insights (not yet clinically deployable)

Mechanistic findings from spatial omics, microbiome studies, rare-variant discovery, and epigenetic/histone-level regulation remain largely hypothesis-generating due to limited replication, scarce tissue availability, and methodological heterogeneity. These layers are valuable for identifying causal circuits and therapeutic targets but should not be interpreted as ready for routine clinical decision-making.

3. Key barriers to implementation

Major barriers include small and heterogeneous cohorts, variability in sample type and timing, confounding by treatment, lack of external validation, and challenges in converting high-dimensional signatures into standardized, cost-effective assays with rapid turnaround. Harmonized, longitudinal, multiancestry cohorts with shared protocols and clinically meaningful endpoints (IVIG response, CAL incidence/progression, long-term cardiovascular outcomes) are essential to bridge discovery and practice.

Conclusion

Multiomics studies have shifted the understanding of KD from a single-layer vasculitis to a network disease shaped by coordinated disturbances across genetic, immunologic, metabolic, and vascular compartments. Importantly, independent omics layers consistently highlight convergent mechanistic axes—including an IL-1/IL-6–driven neutrophil inflammatory hub (transcriptomics/proteomics), Fcγ-receptor signaling influencing IVIG pharmacodynamics (genomics/proteomics), Ca²⁺/NFAT-dependent immune activation (genomics/transcriptomics), and endothelial/ECM remodeling associated with coronary pathology (transcriptomics/proteomics). This cross-layer convergence strengthens biological plausibility and provides a coherent framework for interpreting clinical heterogeneity such as IVIG resistance and CAL risk.

At the same time, most multiomics findings in KD remain at the discovery-to-early validation stage, and translation into routine clinical tools is still limited. Moving forward, the most realistic near-term clinical opportunities include improved risk stratification (e.g., identifying patients at higher risk of IVIG resistance or coronary complications), disease monitoring through biologically informed signatures, and long-term outcome prediction using standardized longitudinal sampling. Achieving these goals will require harmonized, multiancestry cohorts; standardized sampling across disease phases; transparent analytical pipelines; and prospective validation demonstrating added value over conventional clinical and laboratory markers. With these steps, multiomics can provide a practical translational roadmap—linking mechanism-based insights to clinically meaningful endpoints—while avoiding overstatement of immediate diagnostic or therapeutic applicability.

Supplementary materials

Supplementary Tables 1-2 are available at https://doi.org/10.3345/cep.2025.02901.

Supplementary Table 1.

Lifestyle modification program: stages, goals, and specialist roles

cep-2025-02901-Supplementary-Table-1.pdf

Supplementary Table 2.

Lifestyle modification program: stages, goals, and specialist roles

cep-2025-02901-Supplementary-Table-2.pdf

Footnotes

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Funding

This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author contribution

Conceptualization: JGA, IK; Data curation: JGA; Formal analysis: JGA; Methodology: JGA, IK; Project administration: JGA; Visualization: JGA; Writing - original draft: JGA; Writing - review & editing: JGA, IK

Table 1.

Representative multiomics integration strategies and applications

Axis	Multiomics evidence	Key implication in KD
1. Innate inflammation	scRNA-seq: conserved CD177⁺ neutrophil effector program	Upstream inflammatory hub; supports IL-1/IL-6 blockade
	Proteomics: IL-1/IL-6 pathways↑, S100 family↑
	Epigenome: neutrophil-gene hypomethylation after IVIG
	Transcriptomics: S100A8/A9, IL1β↑
2. Fcγ biology/IVIG pharmacodynamics	GWAS: FCGR2A locus	Fcγ pathway as determinant of IVIG responsiveness; genotype-guided strategies
	Proteomics: altered Fc-receptor signaling & plasmablast activation
	Integrated: genotype–proteome predicts IVIG resistance
3. Ca²⁺/NFAT–T-cell hyperreactivity	ITPKC rs28493229 → reduced ITPKC → ↑Ca²⁺/NFAT → T-cell hyperactivation; integrated transcriptome/methylome support	Genetic–epigenetic T-cell dysregulation; calcineurin pathway targeting
4. Metabolic reprogramming	Metabolomics: Kynurenine pathway activation, acylcarnitine imbalance, lipid remodeling;	Immunometabolic coupling; suggests IDO1 and lipid metabolism as potential therapeutic targets
4. Metabolic reprogramming	Transcriptomics/Proteomics: Cytokine-driven IDO1
5. Endothelial activation/ECM remodeling	Genomics: MMP, VEGFA, ANGPT1 variants	Vascular remodeling secondary to inflammation; potential biomarkers and targets for CAL prevention
	Proteomics: Endothelial activation & ECM turnover proteins ↑
	Integrated: Downstream of IL-1/IL-6
6. Microbiome-metabolite-immune axis	Metagenomics / Metabolomics: Altered gut and nasopharyngeal flora; dysregulated bile acid and shortchain fatty acid metabolism	Gut-immune-vascular axis; microbiome-derived metabolites as potential modulators and therapeutic targets
6. Microbiome-metabolite-immune axis	Integrated: Microbial indoles modulate IL-1/IL-6 & epigenetic tone

KD, Kawasaki disease; scRNA-seq, single-cell RNA sequencing; IVIG, intravenous immunoglobulin; GWAS, genome-wide association study; FCGR2A, Fc gamma receptor IIa; Ca²⁺/NFAT, Ca²⁺/nuclear factor of activated T cells; ITPKC, 1,4,5-trisphosphate 3-kinase C; SNP, single nucleotide polymorphism; IDO1, indoleamine 2,3-dioxygenase 1; ECM, extracellular matrix; MMP, matrix metalloproteinase; VEGFA, vascular endothelial growth factor A; ANGPT1, angiopoietin-1; CAL, coronary artery lesion.

References

1. Onouchi Y. The genetics of Kawasaki disease. Int J Rheum Dis 2018;21:26-30.

2. Onouchi Y, Gunji T, Burns JC, Shimizu C, Newburger JW, Yashiro M, et al. Itpkc functional polymorphism associated with Kawasaki disease susceptibility and formation of coronary artery aneurysms. Nat Genet 2008;40:35-42.

3. Habibi A, Talebi H, Bahrami R, Golshan-Tafti M, Shahbazi A, Dastgheib SA, et al. A comprehensive integration of data on the association of ITPKC polymorphisms with susceptibility to Kawasaki disease: a meta-analysis. BMC Med Genomics 2025;18:56.

4. Alphonse MP, Duong TT, Shumitzu C, Hoang TL, McCrindle BW, Franco A, et al. Inositol-triphosphate 3-kinase C mediates inflammasome activation and treatment response in Kawasaki disease. J Immunol 2016;197:3481-9.

5. Uittenbogaard P, Netea SA, Tanck MWT, Geissler J, Buda P, Kowalczyk-Domagała M, et al. Fcgr2/3 polymorphisms are associated with susceptibility to Kawasaki disease but do not predict intravenous immunoglobulin resistance and coronary artery aneurysms. Front Immunol 2024;15:1323171.

6. Kuo HC, Hsu YW, Wu MS, Woon PY, Wong HS, Tsai LJ, et al. Fcgr2a promoter methylation and risks for intravenous immunoglobulin treatment responses in Kawasaki disease. Mediators Inflamm 2015;2015:564625.

7. Duan J, Lou J, Zhang Q, Ke J, Qi Y, Shen N, et al. A genetic variant rs1801274 in FCGR2A as a potential risk marker for Kawasaki disease: a case-control study and meta-analysis. PLoS One 2014;9:e103329.

8. Khor CC, Davila S, Breunis WB, Lee YC, Shimizu C, Wright VJ, et al. Genome-wide association study identifies FCGR2A as a susceptibility locus for Kawasaki disease. Nat Genet 2011;43:1241-6.

9. Onouchi Y, Ozaki K, Buns JC, Shimizu C, Hamada H, Honda T, et al. Common variants in CASP3 confer susceptibility to Kawasaki disease. Hum Mol Genet 2010;19:2898-906.

10. Hoggart C, Shimizu C, Galassini R, Wright VJ, Shailes H, Bellos E, et al. Identification of novel locus associated with coronary artery aneurysms and validation of loci for susceptibility to Kawasaki disease. Eur J Hum Genet 2021;29:1734-44.

11. Wu W, Misra RS, Russell JQ, Flavell RA, Rincón M, Budd RC. Proteolytic regulation of nuclear factor of activated T (NFAT) c2 cells and NFAT activity by caspase-3. J Biol Chem 2006;281:10682-90.

12. Shimizu C, Matsubara T, Onouchi Y, Jain S, Sun S, Nievergelt CM, et al. Matrix metalloproteinase haplotypes associated with coronary artery aneurysm formation in patients with Kawasaki disease. J Hum Genet 2010;55:779-84.

13. Onouchi Y, Ozaki K, Burns JC, Shimizu C, Terai M, Hamada H, et al. A genome-wide association study identifies three new risk loci for Kawasaki disease. Nat Genet 2012;44:517-21.

14. Breunis WB, Davila S, Shimizu C, Oharaseki T, Takahashi K, van Houdt M, et al. Disruption of vascular homeostasis in patients with Kawasaki disease: involvement of vascular endothelial growth factor and angiopoietins. Arthritis Rheum 2012;64:306-15.

15. Burns JC, Shimizu C, Gonzalez E, Kulkarni H, Patel S, Shike H, et al. Genetic variations in the receptor-ligand pair CCR5 and CCL3L1 are important determinants of susceptibility to Kawasaki disease. J Infect Dis 2005;192:344-9.

16. Khor CC, Davila S, Shimizu C, Sheng S, Matsubara T, Suzuki Y, et al. Genome-wide linkage and association mapping identify susceptibility alleles in ABCC4 for Kawasaki disease. J Med Genet 2011;48:467-72.

17. Kuo HC, Li SC, Guo MM, Huang YH, Yu HR, Huang FC, et al. Genome-wide association study identifies novel susceptibility genes associated with coronary artery aneurysm formation in Kawasaki disease. PLoS One 2016;11:e0154943.

18. Shrestha S, Wiener HW, Kajimoto H, Srinivasasainagendra V, Ledee D, Chowdhury S, et al. Pharmacogenomics of intravenous immunoglobulin response in Kawasaki disease. Front Immunol 2023;14:1287094.

19. Burgner D, Davila S, Breunis WB, Ng SB, Li Y, Bonnard C, et al. A genome-wide association study identifies novel and functionally related susceptibility loci for Kawasaki disease. PLoS Genet 2009;5:e1000319.

20. Rigante D, Andreozzi L, Fastiggi M, Bracci B, Natale MF, Esposito S. Critical overview of the risk scoring systems to predict non-responsiveness to intravenous immunoglobulin in Kawasaki syndrome. Int J Mol Sci 2016;17:278.

21. Sleeper LA, Minich LL, McCrindle BM, Li JS, Mason W, Colan SD, et al. Evaluation of Kawasaki disease risk-scoring systems for intravenous immunoglobulin resistance. J Pediatr 2011;158:831-5.e3.

22. Wang Y, Hu J, Liu J, Geng Z, Tao Y, Zheng F, et al. The role of Ca2+/NFAT in dysfunction and inflammation of human coronary endothelial cells induced by sera from patients with Kawasaki disease. Sci Rep 2020;10:4706.

23. Lee Y, Wakita D, Dagvadorj J, Shimada K, Chen S, Huang G, et al. Il-1 signaling is critically required in stromal cells in Kawasaki disease vasculitis mouse model: role of both IL-1α and IL-1β. Arterioscler Thromb Vasc Biol 2015;35:2605-16.

24. Kim DS. Serum interleukin-6 in Kawasaki disease. Yonsei Med J 1992;33:183-8.

25. Hoang LT, Shimizu C, Ling L, Naim AN, Khor CC, Tremoulet AH, et al. Global gene expression profiling identifies new therapeutic targets in acute Kawasaki disease. Genome Med 2014;6:541.

26. Rowley AH, Wylie KM, Kim KY, Pink AJ, Yang A, Reindel R, et al. The transcriptional profile of coronary arteritis in Kawasaki disease. BMC Genomics 2015;16:1076.

27. Ye F, Foell D, Hirono KI, Vogl T, Rui C, Yu X, et al. Neutrophil-derived S100A12 is profoundly upregulated in the early stage of acute Kawasaki disease. Am J Cardiol 2004;94:840-4.

28. Ebihara T, Endo R, Kikuta H, Ishiguro N, Ma X, Shimazu M, et al. Differential gene expression of S100 protein family in leukocytes from patients with Kawasaki disease. Eur J Pediatr 2005;164:427-31.

29. Gu W, Mirsaidi-Madjdabadi S, Ramirez F, Simonson TS, Makino A. Transcriptome meta-analysis of Kawasaki disease in humans and mice. Front Pediatr 2024;12:1423958.

30. Chen KD, Huang YH, Wu WS, Chang LS, Chu CL, Kuo HC. Comparable bidirectional neutrophil immune dysregulation between Kawasaki disease and severe COVID-19. Front Immunol 2022;13:995886.

31. Zheng Y, Zhou Y, Zhu D, Fu X, Xie C, Sun S, et al. Single-cell mapping of peripheral blood mononuclear cells reveals key transcriptomic changes favoring coronary artery lesion in IVIG-resistant Kawasaki disease. Heliyon 2024;10:e37857.

32. Wang Z, Xie L, Ding G, Song S, Chen L, Li G, et al. Single-cell RNA sequencing of peripheral blood mononuclear cells from acute Kawasaki disease patients. Nat Commun 2021;12:5444.

33. Cao N, Ouyang H, Zhang X, Xu Y, Li J, Chen Y. Integration of scRNA-Seq and bulk RNA-Seq uncover perturbed immune cell types and pathways of Kawasaki disease. Front Immunol 2023;14:1259353.

34. Chen Y, Yang M, Zhang M, Wang H, Zheng Y, Sun R, et al. Single-cell transcriptome reveals potential mechanisms for coronary artery lesions in Kawasaki Disease. Arterioscler Thromb Vasc Biol 2024;44:866-82.

35. Beltran JVB, Lin FP, Chang CL, Ko TM. Single-cell meta-analysis of neutrophil activation in Kawasaki disease and multisystem inflammatory syndrome in children reveals potential shared immunological drivers. Circulation 2023;148:1778-96.

36. Wang S, Qian G, Liu Y, Li X, Huang H, Sun L, et al. Kawasaki disease: insights into the roles of T cells. Front Immunol 2025;16:1582638.

37. Ko TM, Kuo HC, Chang JS, Chen SP, Liu YM, Chen HW, et al. Cxcl10/ip-10 is a biomarker and mediator for Kawasaki disease. Circ Res 2015;116:876-83.

38. Porritt RA, Zemmour D, Abe M, Lee Y, Narayanan M, Carvalho TT, et al. NLRP3 inflammasome mediates immunestromal interactions in vasculitis. Circ Res 2021;129:e183-200.

39. Kumrah R, Jindal AK, Rawat A, Singh S. Proteomics approach for biomarker discovery in Kawasaki disease. Expert Rev Clin Immunol 2024;20:1449-60.

40. Nygaard U, Nielsen AB, Dungu KHS, Drici L, Holm M, Ottenheijm ME, et al. Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. Commun Biol 2024;7:688.

41. Weng KP, Chien KJ, Huang SH, Huang LH, Lin PH, Lin Y, et al. iTRAQ proteomics identified the potential biomarkers of coronary artery lesion in Kawasaki disease and in vitro studies demonstrated that S100A4 treatment made HCAECs more susceptible to neutrophil infiltration. Int J Mol Sci 2022;23:12770.

42. Wang N, Gao Y, Wang Y, Dai Y, Tang Y, Huang J, et al. Plasma proteomic profiling reveals that SERPINE1 is a potential biomarker associated with coronary artery lesions in Kawasaki disease. Int Immunopharmacol 2024;139:112698.

43. Yi C, Zhou YN, Guo J, Chen J, She X. Novel predictors of intravenous immunoglobulin resistance in patients with Kawasaki disease: a retrospective study. Front Immunol 2024;15:1399150.

44. Tian F, Ma L, Zhao R, Ji L, Wang X, Sun W, et al. Correlation between matrix metalloproteinases with coronary artery lesion caused by Kawasaki disease. Front Pediatr 2022;10:802217.

45. Gavin PJ, Crawford SE, Shulman ST, Garcia FL, Rowley AH. Systemic arterial expression of matrix metalloproteinases 2 and 9 in acute Kawasaki disease. Arterioscler Thromb Vasc Biol 2003;23:576-81.

46. Tu X, Chen X, Xu L, Yang C, Li J, Liu Y, et al. Serum Olink targeted proteomics identifies IL-17A as a prospective inflammatory marker for the prediction and diagnosis of Kawasaki disease. J Inflamm Res 2025;18:3093-103.

47. Fan X, Shu P, Wang Y, Ji N, Zhang D. Interactions between neutrophils and T-helper 17 cells. Front Immunol 2023;14:1279837.

48. Wang H, Shimizu C, Bainto E, Hamilton S, Jackson HR, Estrada-Rivadeneyra D, et al. Subgroups of children with Kawasaki disease: a data-driven cluster analysis. Lancet Child Adolesc Health 2023;7:697-707.

49. Chen Z, Sai S, Nagumo K, Wu Y, Chiba H, Hui SP. Distinctive serum lipidomic profile of IVIG-resistant Kawasaki disease children before and after treatment. PLoS One 2023;18:e0283710.

50. Nakashima Y, Sakai Y, Mizuno Y, Furuno K, Hirono K, Takatsuki S, et al. Lipidomics links oxidized phosphatidylcholines and coronary arteritis in Kawasaki disease. Cardiovasc Res 2021;117:96-108.

51. Sevastou I, Kaffe E, Mouratis MA, Aidinis V. Lysoglycerophospholipids in chronic inflammatory disorders: the PLA(2)/LPC and ATX/LPA axes. Biochim Biophys Acta 2013;1831:42-60.

52. Sun GY, Shelat PB, Jensen MB, He Y, Sun AY, Simonyi A. Phospholipases A2 and inflammatory responses in the central nervous system. Neuromolecular Med 2010;12:133-48.

53. Law SH, Chan ML, Marathe GK, Parveen F, Chen CH, Ke LY. An updated review of lysophosphatidylcholine metabolism in human diseases. Int J Mol Sci 2019;20:1149.

54. Dambrova M, Makrecka-Kuka M, Kuka J, Vilskersts R, Nordberg D, Attwood MM, et al. Acylcarnitines: nomenclature, biomarkers, therapeutic potential, drug targets, and clinical trials. Pharmacol Rev 2022;74:506-51.

55. Tsuge M, Uda K, Eitoku T, Matsumoto N, Yorifuji T, Tsukahara H. Roles of oxidative injury and nitric oxide system derangements in Kawasaki disease pathogenesis: a systematic review. Int J Mol Sci 2023;24:15450.

56. Fan X, Li K, Guo X, Liao S, Zhang Q, Xu Y, et al. Metabolic profiling reveals altered tryptophan metabolism in patients with Kawasaki disease. Front Mol Biosci 2023;10:1180537.

57. Seo SK, Kwon B. Immune regulation through tryptophan metabolism. Exp Mol Med 2023;55:1371-9.

58. Wang X, Han L, Jiang J, Fan Z, Hua Y, He L, et al. Alterations in bile acid metabolites associated with pathogenicity and IVIG resistance in Kawasaki disease. Front Cardiovasc Med 2025;12:1549900.

59. Amirsardari Z, Moghadam EA, Mohebbi A, Fanos V, Ayati A, Portman MA, et al. Metabolomic profiling for predicting coronary artery aneurysms and IVIG resistance in Kawasaki disease- an exploratory study. J Cardiovasc Transl Res 2025;18:951-9.

60. Li SC, Chan WC, Huang YH, Guo MM, Yu HR, Huang FC, et al. Major methylation alterations on the CpG markers of inflammatory immune associated genes after IVIG treatment in Kawasaki disease. BMC Med Genomics 2016;9 Suppl 1(Suppl 1): 37.

61. Sharma K, Vignesh P, Srivastava P, Sharma J, Chaudhary H, Mondal S, et al. Epigenetics in Kawasaki disease. Front Pediatr 2021;9:673294.

62. Huang YH, Lo MH, Cai XY, Kuo HC. Epigenetic hypomethylation and upregulation of NLRC4 and NLRP12 in Kawasaki disease. Oncotarget 2018;9:18939-48.

63. Yu B, Zheng B, Shen Y, Shen Y, Qiu H, Wu L, et al. Nlrc4 methylation and its response to intravenous immunoglobulin therapy in Kawasaki disease: a case control study. BMC Pediatr 2024;24:190.

64. Huang YH, Chen KD, Lo MH, Cai XY, Chang LS, Kuo YH, et al. Decreased DNA methyltransferases expression is associated with coronary artery lesion formation in Kawasaki disease. Int J Med Sci 2019;16:576-82.

65. Das D, Karthik N, Taneja R. Crosstalk between inflammatory signaling and methylation in cancer. Front Cell Dev Biol 2021;9:756458.

66. Xiong Y, Xu J, Zhang D, Wu S, Li Z, Zhang J, et al. MicroRNAs in Kawasaki disease: an update on diagnosis, therapy and monitoring. Front Immunol 2022;13:1016575.

67. Huang X, Li H, Zhao X, Zhang H, Li Y, Niu Q, et al. Combination of CRP and miRNA signature as a potential diagnostic strategy for Kawasaki disease. Front Pediatr 2025;13:1678095.

68. Wu WS, Yang TH, Chen KD, Lin PH, Chen GR, Kuo HC. Kdmarkers: A biomarker database for investigating epigenetic methylation and gene expression levels in Kawasaki disease. Comput Struct Biotechnol J 2022;20:1295-305.

69. Yeter D, Kuo H. Abstract 32: Improper histone modification of gene expression in Kawasaki disease may influence both its development and outcome. Circulation 2015;131(suppl 2): 32.

70. Honer MA, Ferman BI, Gray ZH, Bondarenko EA, Whetstine JR. Epigenetic modulators provide a path to understanding disease and therapeutic opportunity. Genes Dev 2024;38:473-503.

71. Katayama K, Matsubara T, Fujiwara M, Koga M, Furukawa S. Cd14+cd16+ monocyte subpopulation in Kawasaki disease. Clin Exp Immunol 2000;121:566-70.

72. Kim YS, Yang HJ, Kee SJ, Choi I, Ha K, Ki KK, et al. The "intermediate" CD14 + CD16 + monocyte subpopulation plays a role in IVIG responsiveness of children with Kawasaki disease. Pediatr Rheumatol Online J 2021;19:76.

73. Geng Z, Tao Y, Zheng F, Wu L, Wang Y, Wang Y, et al. Altered Monocyte Subsets in Kawasaki Disease Revealed by Single-cell RNA-Sequencing. J Inflamm Res 2021;14:885-96.

74. Song S, Chen L, Zhou Y, Xu Y, Li G, Shen L, et al. Cd14+ monocytes: the immune communication hub in early vasculitis symptoms of Kawasaki disease. Front Immunol 2025;16:1557231.

75. Kessel C, Koné-Paut I, Tellier S, Belot A, Masjosthusmann K, Wittkowski H, et al. An immunological axis involving interleukin 1β and leucine-rich-α2-glycoprotein reflects therapeutic response of children with Kawasaki disease: implications from the KAWAKINRA trial. J Clin Immunol 2022;42:1330-41.

76. Xu Y, Yuan Y, Mou L, Hui L, Zhang X, Yao X, et al. ScRNA+ TCR-seq reveals the pivotal role of dual receptor T lymphocytes in the pathogenesis of Kawasaki disease and during IVIG treatment. Front Immunol 2024;15:1457687.

77. Netea SA, Biesbroek G, van Stijn D, Ijspeert H, van der Made CI, Jansen MH, et al. Transient anti-cytokine autoantibodies superimpose the hyperinflammatory response in Kawasaki disease and multisystem inflammatory syndrome in children: a comparative cohort study on correlates of disease. EBioMedicine 2023;95:104736.

78. Brodeur KE, Liu M, Ibanez D, de Groot MJ, Chen L, Du Y, et al. Elevation of IL-17 cytokines distinguishes Kawasaki disease from other pediatric inflammatory disorders. Arthritis Rheumatol 2024;76:285-92.

79. Lin IC, Suen JL, Huang SK, Chou MH, Kuo HC, Lo MH, et al. Involvement of IL-17 A/IL-17 Receptor A with Neutrophil Recruitment and the Severity of Coronary Arteritis in Kawasaki Disease. J Clin Immunol 2024;44:77.

80. Jone PN, Tremoulet A, Choueiter N, Dominguez SR, Harahsheh AS, Mitani Y, et al. Update on diagnosis and management of Kawasaki disease: a scientific statement from the American Heart Association. Circulation 2024;150:e481-500.

81. Lu Y, Hu FQ. Elevated serum IL-17A in Kawasaki disease patients predicts responsiveness to intravenous immunoglobulin therapy. Int Arch Allergy Immunol 2025;186:159-65.

82. Seo K, Choi JK. Comprehensive analysis of TCR and BCR repertoires: insights into methodologies, challenges, and applications. Genomics Inform 2025;23:6.

83. Ling J, Xie F, Zhou Q, Ouyang Q, Li L, Zhao W, et al. Case series on the efficacy and safety of tocilizumab in IVIG-resistant Kawasaki disease: a retrospective analysis of five patients. J Inflamm Res 2024;17:10991-8.

84. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, et al. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 2018;14:e8124.

85. Vahabi N, Michailidis G. Unsupervised multi-omics data integration methods: a comprehensive review. Front Genet 2022;13:854752.

86. Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC, et al. Mofa+: A statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol 2020;21:111.

87. Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, et al. Diablo: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 2019;35:3055-62.

88. Jeon J, Han EY, Jung I. MOPA: an integrative multi-omics pathway analysis method for measuring omics activity. PLoS One 2023;18:e0278272.

89. Maghsoudi Z, Nguyen H, Tavakkoli A, Nguyen T. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. Brief Bioinform 2022;23:bbac435.

90. Wang X, Zhang L. Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease. Pediatr Rheumatol Online J 2025;23:61.

91. Kuo HC, Yang KD, Juo SH, Liang CD, Chen WC, Wang YS, et al. Itpkc single nucleotide polymorphism associated with the Kawasaki disease in a Taiwanese population. PLoS One 2011;6:e17370.

92. Feng C, Wei Z, Li X. Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms. Front Immunol 2025;16:1541939.

93. Goyal T, Sharma S, Pilania RK, Jawallia K, Chawla S, Sharma M, et al. Genetic landscape of Kawasaki disease: an update. Lymphatics 2025;3:21.

94. Wang C, Yu W, Wu X, Wang S, Chen L, Liu G. Proteomic insights into molecular alterations associated with Kawasaki disease in children. Ital J Pediatr 2025;51:56.

95. Velten B, Braunger JM, Argelaguet R, Arnol D, Wirbel J, Bredikhin D, et al. Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nat Methods 2022;19:179-86.

96. Leung DYM, Schlievert PM. Kawasaki syndrome: role of superantigens revisited. FEBS J 2021;288:1771-7.

97. Jena PK, Arditi M, Noval Rivas M. Gut microbiota alterations in patients with Kawasaki disease. Arterioscler Thromb Vasc Biol 2025;45:345-58.

98. Sánchez-Manubens J, Henares D, Muñoz-Almagro C, Brotons de Los Reyes P, Timoneda N, Antón J. Characterization of the nasopharyngeal microbiome in patients with Kawasaki disease. An Pediatr (Engl Ed) 2022;97:300-9.

99. Zeng Q, Zeng R, Ye J. Alteration of the oral and gut microbiota in patients with Kawasaki disease. PeerJ 2023;11:e15662.

100. Jena PK, Wakita D, Gomez AC, Carvalho TT, Atici AE, Aubuchon E, et al. Intestinal microbiota contributes to the development of cardiovascular inflammation and vasculitis in mice. Circ Res 2025;136:e53-72.