Development of a Peripheral Blood Transcriptomic Gene Signature to Predict Bronchopulmonary Dysplasia.

BACKGROUND
Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning BPD.


OBJECTIVE
Develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD.


METHODS
Secondary analysis of whole blood microarray data from 97 very low birth weight neonates day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned into a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared to gestational age or birthweight.


RESULTS
Neonates with BPD (n=62) exhibited a lower median gestational age (26.0 weeks vs. 30.0 weeks p<0.01) and birthweight (800 grams vs 1,280 grams, p<0.01) compared to non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birthweight were 87.8% and 87.2%, respectively. The machine learning models revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were most associated with BPD.


CONCLUSIONS
A derived 5-gene whole blood signature can accurately predict BPD in the first week of life.