Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants

摘要: Respiratory syncytial virus (RSV) causes infections that range from common cold to severe lower respiratory tract infection requiring high-level medical care. Prediction of the course disease in individual patients remains challenging at first visit pediatric wards and RSV may rapidly progress disease. In this study we investigate whether there exists a genomic signature can accurately predict RSV. We used early blood microarray transcriptome profiles 39 hospitalized infants were followed until recovery which level severity was determined retrospectively. Applying support vector machine learning on age by sex standardized transcriptomic data, an 84 gene identified discriminated with eventually less suffered most This yielded area under receiver operating characteristic curve (AUC) 0.966 using leave-one-out cross-validation experimental data AUC 0.858 independent validation cohort consisting 53 infants. A combination 0.971. Thus, presented serve as basis develop prognostic test clinical management patients.