Kawasaki Disease (KD) is a rare systemic vasculitis disorder which affects the medium and small arteries particularly coronary arteries in infants and young children (1). The disease is diagnosed globally with the highest incidence reported for the eastern Asia countries including, in order of magnitude, Japan, South Korea, and Taiwan; in non-Asian countries, however, substantial and meaningful differences in the incidence rate have been demonstrated (1–4). Generally, the difference has been linked with the racial composition of the societies (5). A functional single-nucleotide polymorphism (itpkc-3)in the inositol 1,4,5-trisphosphate 3-kinase C (ITPKC) gene has been associated with enhanced susceptibility to the disease and developing coronary artery aneurisms, so that C allele of itpkc-3 gene can conduce to immune hyper-reactivity in KD (e.g., by increase in interleukin-2 [IL-2] transcript level) (6). Nonetheless, the etiology of KD remains enigmatic (5–7); accordingly, it is required to further explore the molecular mechanisms of the disease to achieve a decisive diagnosis.
The clinical manifestations of KD include a series of symptoms such as fever lasting longer than five days, bilateral non-purulent conjunctivitis and cervical lymphadenopathy, rashes, lip fissures, erythema and edema in oral mucosa (characteristic strawberry tongue) and peripheral extremities, and etc. which are similar to those of other types of neonatal illnesses (8) such as epistaxis, juvenile idiopathic arthritis, scarlet fever, and etc., and this can lead to misdiagnosis of the condition (9). Delayed or missed diagnosis can pose the patient at higher risk of coronary artery abnormalities (10). till now, a large number of biomarkers including those designed for inflammatory, proteomics, gene expression profiles, and micro-RNA characteristics have been failed in diagnostic approaches due to unacceptable sensitivity and specificity (10–13), and so, they may couldn’t suggest confirmation in the diagnostic decision (14). Therefore, introduction of a novel, decisive, prognostic, or diagnostic biomarker would be an inevitable necessity for the KD as a complicated condition requiring immediate diagnosis. The genetic basis known for KD has been interesting for the researchers and clinicians to develop a genetic approach for diagnosis and prediction of prognosis in patients with KD. The genetic approaches might be classified into ‘candidate gene approaches’ and ‘genome-wide approaches’ (14–17). Chaudhary et al. (16) have enlisted the studies conducted on the genetic markers of KD. They, however, have stressed on irreproducibility of the results among different nations. So, one drawback may be concluded from the insufficient genes list is that it doesn’t make possible to accurately diagnose and predict prognosis of the disease.
Over the past decade, substantial pathogenetic clues have been found for various diseases such as immune reactions via the genome-wide approach (18, 19). In this regard, using the DNA microarray method, alteration in expression levels including up- and down-regulation of thousands of genes and also pathological mechanisms can be detected in a single chip such as experiments conducted for KD(20). Weighted gene co-expression network analysis (WGCNA), likewise, empowers the scientists to explore network alterations and basic mechanisms among highly correlated genes and to also help find new biomarkers from disease associated genes cluster (21–23).
In the present study, we investigated the co-expressed genes in KD patients using WGCNA package to explore network modules. Then, four microarray datasets of KD from the Gene Expression Omnibus (GEO) repository were integrated to find the differentially expressed genes (DEGs) in the patient’ samples compared to control groups. By merging the results, we screened 35 genes and then through evaluation of their aberrant expression with symptomatically like diseases including bacterial and viral infections, JIA (juvenile idiopathic arthritis), HSP (Henoch-Schönlein purpura), infection of unknown etiology, GAS (group A streptococcal) infection, human adenovirus (HAdV) infection, and incomplete KD, two genes were selected as potential biomarkers. Eventually, we employed Real-Time Polymerase Chain Reaction (RT-PCR) to substantiate the selected genes.