Gene expression analysis in Kawasaki disease; bioinformatics and experimental approach

Abstract Kawasaki disease (KD) is an inflammatory condition in children, which has unknown etiology with an insufficiently described genetic mechanism. There is no accurate molecular diagnostic test for KD, but some genetic factors have been proposed in previous studies. In this study, we investigated the underlying molecular alterations based on both in silico and wet lab analysis to candidate transcriptional biosignature and biomarker. metaQC, metaDE, and metapath packages have been used in the bioinformatic analysis for the assessment of quality control, investigation of differentially expressed genes, and enrichment of detected genes, respectively. In the next step, miRNA array was analyzed by biobase, GEOquery, and limma packages. All bioinformatic studies were conducted with the R software platform. Finally, Real-Time PCR was performed on patient samples for the evaluation of the bioinformatic results. The results of bioinformatic analysis led to the introduction of 28 genes with the highest difference in gene expression, and 14 miRNAs with the highest difference in expression after microRNA array analysis. Real-time PCR results validated candidate genes and miRNAs as KD transcriptional biosignatures and biomarkers, respectively. Our studies have shown MyD88, KREMEN1, TLR5, ALPK1, IRAK4, PFKFB3, HK3, CREB, CR1, SLC2A14, and FPR1 as the most important genes involved in KD. Also, hsa-miR-575, hsa-miR-483-5p, hsa-miR-4271, hsa-miR-4327 as the most likely miRNAs to interfere with KD. The altered expression levels of the aforementioned genes and miRNAs can be studied further for therapeutic targets.