Identification of Differentially Expressed Genes in Kawasaki Disease Patients as Potential Biomarkers for IVIG Sensitivity by Bioinformatics Analysis
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.
KeywordsKawasaki disease Differentially expressed genes (DEGs) Biomarker IVIG-related sensitivity
Database for Annotation, Visualization and Integrated Discovery
Differentially expressed genes
Gene expression omnibus
- BH FDR
Hochberg false discovery rate
Kyoto encyclopedia of genes and genomes
National Center for Biotechnology Information
Receiver operating characteristic curve
Supported vector machine
On the completion of my thesis, I should like to express my deepest gratitude to all those whose kindness and advice have made this work possible. I am greatly indebted to my advisor Guoying Huang who gave me valuable instructions and has improved me in language. His effective advice and shrewd comments have kept the thesis in the right direction. I would like to thank my partners for their friendship and constructive suggestions, and they constantly encouraged me when I felt frustrated with this dissertation.
This research received no grant from any funding agency in the public, commercial or not-for-profit sectors.
Compliance with Ethical Standards
Conflict of interest
The authors disclose no conflicts of interest.
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