Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning.

Background: Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD.

Methods: We first identified disease-associated modules in KD using weighted gene co-expression network analysis. Then, we determined shared differentially expressed genes (DEGs) in training datasets for KD (GSE100154) and COVID-19 (GSE225220), performed functional annotation of these shared DEGs, and used Cytoscape plug-ins (MCODE and Cytohubba) to characterize the protein-protein interaction (PPI) network and identify the hub genes. We performed Least Absolute Shrinkage and Selection Operator(LASSO) regression and receiver operating characteristic (ROC) curve analysis to identify the most robust markers, validated these results by analysis of two other datasets (GSE73461 and GSE18606), and then calculated the correlations of these key genes with immune cells.

Results: This analysis identified 26 shared DEGs in COVID-19 and KD. The results from functional annotation showed that the shared DEGs primarily functioned in immune responses, the formation of neutrophil extracellular traps, and NOD-like receptor signaling pathways. There were three key genes (PGLYRP1, DEFA4, RETN), and they had positive correlations with monocytes, M0 macrophages, and dendritic cells, which function as immune infiltrating cells in KD.

Conclusion: The potential immune-associated biomarkers (PGLYRP1, DEFA4, RETN) along with their shared pathways, hold promise for advancing investigations into the underlying pathogenesis of KD and COVID-19.