Uncovering Potential Novel Biomarkers in Immunoglobulin-Resistant Kawasaki Disease Using Bioinformatics Analysis

Abstract

Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) is a complicated disorder, which can induce multiple-system damage. The pathogenic factor inducing KD remains unclear. The present study focused on identifying potential novel biomarkers for IVIG-resistant KD using integrated analyses. Eight IVIG-resistant KD samples and twelve IVIG-sensitive KD samples were included in the GSE18606 dataset. A Linear Model for Microarray Data (LIMMA) identified 504 differentially expressed genes (DEGs), An IVIG-resistant KD sample was compared with an IVIG-sensitive KD sample to identify 17 modules through weighted gene co-expression network analysis (WGCNA). A common gene (CG) is the intersection of DEGs and genes in the most significant module. Analysis of functional enrichment revealed that CGs were mainly enriched in TNF signaling pathways and NF-kappa B signaling pathways. Ten of these genes were selected as hub genes because of their high degree of connectivity (KLF1, AHSP, HBQ1, HBA2, HBA1, EPB42, GYPB, UBB, KRT1 and BPIFB2).