Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms - Feng, Chenhui; Wei, Zhimiao; Li, Xiaohui (Frontiers in Immunology, 2025)
BackgroundThe bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper understanding of their role in the pathogenesis of KD.MethodWe analyzed the public single-cell RNA sequencing (scRNA-seq) dataset GSE1687323 to characterize the immune cell-type landscape in KD. Gene sets related to BAM and FAM were collected from the Gene Set Enrichment Analysis (GSEA) database and previous literature. We analyzed the cellular heterogeneity of BAM and FAM at the single-cell level using R packages. Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. The cellular expression patterns of signature genes were further validated using our own scRNA-seq dataset. Finally, quantitative real-time PCR (qRT–PCR) was performed to validate the expression levels of signature genes in KD, and Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate their diagnostic potential.ResultsEnhanced BAM and FAM were detected in monocytes and natural killer (NK) cells from KD in the public scRNA-seq dataset. Our scRNA-seq data confirmed the signature genes identified by machine learning algorithms: Vimentin (VIM) and chloride intracellular channel 1 (CLIC1) were upregulated in monocytes, while integrin subunit beta 2 (ITGB2) was elevated in NK cells of KD. qRT-PCR results also validated the bioinformatic analysis. Moreover, these genes demonstrated significant diagnostic potential. In the training dataset (GSE68004), the area under the curve (AUC) values and 95% CI were as follows: VIM: 0.914 (0.863–0.966), ITGB2: 0.958 (0.925–0.991), and CLIC1: 0.985 (0.969–1). The validation dataset (GSE73461) yielded similarly robust results, with AUC values and 95% CI: VIM: 0.872 (0.811–0.934), ITGB2: 0.861 (0.795–0.928), and CLIC1: 0.893 (0.837–0.948).ConclusionThis study successfully identified and validated VIM and CLIC1 in monocytes, as well as ITGB2 in NK cells, as novel metabolism-related genes in KD. These findings suggest that BAM and FAM may play crucial roles in KD pathogenesis. Furthermore, these signature genes hold promising potential as diagnostic biomarkers for KD.