Microarray meta-analysis identifies acute lung injury biomarkers in donor lungs that predict development of primary graft failure in recipients.
OBJECTIVE
To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.


METHODS
We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.


RESULTS
Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.


CONCLUSIONS
Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of "injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.
The Centre for Applied Genomics, Hospital for Sick Children, Toronto, Ontario, Canada.
Pingzhao
P
Hu
Xinchen
X
Wang
Jack J
JJ
Haitsma
Suleiman
S
Furmli
Hussain
H
Masoom
Mingyao
M
Liu
Yumiko
Y
Imai
Arthur S
AS
Slutsky
Joseph
J
Beyene
Celia M T
CM
Greenwood
Claudia
C
dos Santos
D055371:Acute Lung Injury / Q000235:genetics / Q000378:metabolism*; D000818:Animals; D015415:Biomarkers / Q000378:metabolism*; D002460:Cell Line; D016208:Databases, Factual; D020869:Gene Expression Profiling; D006801:Humans; D008168:Lung / Q000378:metabolism; D016040:Lung Transplantation / Q000009:adverse effects; D046228:Microarray Analysis; D023421:Models, Animal; D020411:Oligonucleotide Array Sequence Analysis; D051381:Rats
D015415:Biomarkers