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

Authors

Alvaro Moreira
Miriam Tovar
Alisha M Smith
Grace C Lee
Justin A Meunier
Zoya Cheema
Axel Moreira
Caitlyn Winter
Shamimunisa B Mustafa
Steven Seidner
Tina Findley
Joe G N Garcia
Bernard Thébaud
Przemko Kwinta
Sunil K Ahuja

Publication Date

1-1-2023

Journal

American Journal of Physiology

DOI

10.1152/ajplung.00250.2022

PMID

36472344

PMCID

PMC9829478

PubMedCentral® Posted Date

12-6-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Infant, Newborn, Humans, Bronchopulmonary Dysplasia, Birth Weight, Transcriptome, Artificial Intelligence, Infant, Premature, Gestational Age

Abstract

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 chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on 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 to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with 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 birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

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