Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants
Publication year
2016Source
Scientific Reports, 6, (2016), article 36603ISSN
Publication type
Article / Letter to editor

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Organization
Laboratory Medicine
Paediatrics - OUD tm 2017
Journal title
Scientific Reports
Volume
vol. 6
Subject
Radboudumc 4: lnfectious Diseases and Global Health RIMLS: Radboud Institute for Molecular Life SciencesAbstract
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 of disease in individual patients remains challenging at the first visit to the pediatric wards and RSV infections may rapidly progress to severe disease. In this study we investigate whether there exists a genomic signature that can accurately predict the course of RSV. We used early blood microarray transcriptome profiles from 39 hospitalized infants that were followed until recovery and of which the level of disease severity was determined retrospectively. Applying support vector machine learning on age by sex standardized transcriptomic data, an 84 gene signature was identified that discriminated hospitalized infants with eventually less severe RSV infection from infants that suffered from most severe RSV disease. This signature yielded an area under the receiver operating characteristic curve (AUC) of 0.966 using leave-one-out cross-validation on the experimental data and an AUC of 0.858 on an independent validation cohort consisting of 53 infants. A combination of the gene signature with age and sex yielded an AUC of 0.971. Thus, the presented signature may serve as the basis to develop a prognostic test to support clinical management of RSV patients.
This item appears in the following Collection(s)
- Academic publications [202801]
- Electronic publications [100888]
- Faculty of Medical Sciences [80020]
- Open Access publications [69615]
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