Kawasaki disease (KD) is a leading cause of acquired heart disease predominantly affecting infants and young children. Intravenous immunoglobulin (IVIG) is applied as the most favorable treatment against KD, but IVIG resistant remains exist. Although several clinical scoring systems have been developed to identify children at highest risk of IVIG resistance, there is a need to identify sufficiently sensitive biomarkers for IVIG treatment. Some differentially expressed genes (DEGs) could be the promising potential biomarkers for IVIG-related sensitivity diagnosis. We employed a systematic and integrative bioinformatics framework to identify such kind of genes. The performance of the candidate genes was evaluated by hierarchical clustering, ROC analysis and literature mining. By analyzing three datasets of KD patients, 34 DEGs of the three groups have been found to be associated with IVIG-related sensitivity. A module of 12 genes could predict resistant group patients with high accuracy, and a module of ten genes could predict responsive group patients effectively with accuracy of 96 %. And three of them are most likely to serve as drug targets or diagnostic biomarkers in the future. Compared with unsupervised hierarchical clustering analysis, our modules could distinct IVIG-resistant patients efficiently. Two groups of DEGs could predict IVIG-related sensitivity with high accuracy, which are potential biomarkers for the clinical diagnosis and prediction of IVIG treatment response in KD patients, improving the prognosis of patients.