Abstract
Incomplete Kawasaki Disease is a complex disease that often occurs in infants and has substantial coronary artery damage. Its pathogenesis is unclear and lacks specific diagnostic markers. The purpose of our study is to research the mechanism of incomplete Kawasaki Disease use of bioinformatic methods and identify potential biomarkers. We performed weighted gene co-expression network analysis to analyze the data set GSE68004 and identified modules and genes which were correlated with the disease. Through functional annotation and enrichment analysis, we determined the biological function and signal pathway of these genes. We further used lasso regression and ROC curve to screen genes and determined that the final candidate gene was HSPB11and hsa-miR-155-5p that regulates its expression. Finally, we validated the screened gene using an independent dataset and construct a TF-miRNA network. Through the relationships of TFs and hsa-miR-155-5p, we found is hsa-miR-155-5p closely related to hypoxia-related transcription factors, which may be a new direction in the research of Kawasaki disease.