Exploring Kawasaki disease-specific hub genes revealing a striking similarity of expression profile to bacterial infections using weighted gene co-expression network analysis (WGCNA) and co-expression modules identification tool (CEMiTool): An integrated bioinformatics and experimental study

Kawasaki disease (KD) has been declared a rare idiopathic condition for a long time. The children age less than five years, as the most susceptible group, are at risk of this disease. Since the cause of the disease is unknown, this study was designed to investigate the cause of KD. We applied metaDE and WGCNA packages in order to perform a meta-analysis and identify network modules of co-expressed genes, respectively, on three expression array datasets and also CEMiTool package to confirm detected modules by WGCNA. Using the Pearson correlation coefficient, the resemblance of KD to other symptomatic-similar diseases, including bacterial infections, viral infections, JIA (juvenile idiopathic arthritis), HSP (Henoch-Schönlein purpura), GAS (group A streptococcal), and HAdV (adenovirus) was accurately estimated. In addition to validation by more three expression array datasets, serum samples of 16 patients and eight control participants have undergone the Real-Time PCR assay so as to evaluate produced bioinformatic results. WGCNA showed 3840 differentially expressed genes (DEGs) in KD in comparison with other diseases accompanying resembling clinical manifestations. Through further bioinformatic analysis and validation, 42 out of DEGs were introduced as hub genes, which the results of Real-Time PCR assay subsequently attested to the majority of them. The DEGs possessed a remarkable commonality with those of bacterial conditions. According to our exhaustive results, the origin of KD has been revealed pertinent to bacterial infections. Another interesting finding in this study is introducing IVIG in combination with particular antibiotics as a novel therapeutic approach, which supported by a score of authentic research studies to overcome KD. © 2020 Elsevier Gmb