Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.

BACKGROUND

Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood.

METHODS

This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).

RESULTS

Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 () and serine-arginine protein kinase 1 () as critical hub genes. In the merged dataset, the area under the curve (AUC) values for and were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for and 0.878 (95% CI: 0.750 to 1.000) for . The CIBERSORT analysis connected and with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls.

CONCLUSIONS

and emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.