KDmarkers: A biomarker database for investigating epigenetic methylation and gene expression levels in Kawasaki disease.

Abstract

Kawasaki disease (KD) is a form of acute systemic vasculitis that primarily affects children and has become the most common cause of acquired heart disease. While the etiopathogenesis of KD remains unknown, the diagnostic criteria of KD have been well established. Nevertheless, the diagnosis of KD is currently based on subjective clinical symptoms, and no molecular biomarker is yet available. We have previously performed and combined methylation array (Illumina HumanMethylation450 BeadChip) and transcriptome array (Affymetrix GeneChip Human Transcriptome Array 2.0) to identify genes that are differentially methylated/expressed in KD patients compared with control subjects. We have found that decreased methylation levels combined with elevated gene expression can indicate genes (e.g., toll-like receptors and CD177) involved in the disease mechanisms of KD. In this study, we constructed a database called KDmarkers to allow researchers to access these valuable potential KD biomarkers identified via methylation array and transcriptome array. KDmarkers provides three search modes. First, users can search genes differentially methylated and/or differentially expressed in KD patients compared with control subjects. Second, users can check the KD patient groups in which a given gene is differentially methylated and/or differentially expressed. Third, users can explore the DNA methylation levels and gene expression levels in all samples (KD patients and controls) for a particular gene of interest. We further demonstrated that the results in KDmarkers are strongly associated with KD immune responses. All analysis results can be downloaded for downstream experimental designs. KDmarkers is available online at https://cosbi.ee.ncku.edu.tw/KDmarkers/.

• Authors: Wei-Sheng Wu, Tzu-Hsien Yang, Kuang-Den Chen, Po-Heng Lin, Guan-Ru Chen, Ho-Chang Kuo
• Journal: Computational and structural biotechnology journal (Comput Struct Biotechnol J) Vol. 20 Pg. 1295-1305 ( 2022) ISSN: 2001-0370 [Print] Netherlands
• PMID: 35356542 (Publication Type: Journal Article)
• Copyright: © 2022 The Author(s).