LAUSR

OBJECTIVE Kawasaki disease (KD) is an acute vasculitis in young children, with ambiguous etiology. Early diagnosis will decrease the risk of coronary artery aneurysms and contributes to the favourable prognosis. This work aimed to identify disease modules that accurately predict clinical outcome using a systemic module tracking method. PATIENTS AND METHODS Based on the transcriptional data and protein-protein interaction (PPI) data, we constructed the differential co-expression network for KD. Then, a systemic module tracking method was performed to extract KD-related modules that accurately predict clinical outcome from the differential co-expression network, according to two steps: key genes identification and module inference by key gene expansion. RESULTS 16 key genes were identified based on their importance in differential co-expression network and most of them were ribosomal protein-related genes. With each key gene as initial gene, we identified 10 disease modules with high predictive accuracy. Function analysis found that these disease modules were related to one common pathway-ribosome pathway. CONCLUSIONS Our study for the first time indicated that disturbed ribosome-related disease modules might contribute to the development of KD. These modules could be considered as novel contributors to the progression of KD, and potential diagnostic biomarkers for predicting the clinical outcome.