SynthesisPloS one2012
Microarray meta-analysis identifies acute lung injury biomarkers in donor lungs that predict development of primary graft failure in recipients.
Synthesis in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Research advances in consistency and variability inJournal of thoracic disease · 2026Review
- Prediction of lung overdistension during mechanical ventilation using micro-RNA and gene expression.Intensive care medicine experimental · 2025Article
- OGDH is involved in sepsis induced acute lung injury through the MAPK pathway.Journal of thoracic disease · 2021Article
- Integrative Analysis of Axolotl Gene Expression Data from Regenerative and Wound Healing Limb Tissues.Scientific reports · 2019Article
- Intracellular signal transduction pathways as potential drug targets for ischemia-reperfusion injury in lung transplantation.Journal of thoracic disease · 2018Article
- Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery.Microarrays (Basel, Switzerland) · 2015Review
- A differential network analysis approach for lineage specifier prediction in stem cell subpopulations.NPJ systems biology and applications · 2015Article
- Assessing the quality of studies supporting genetic susceptibility and outcomes of ARDS.Frontiers in genetics · 2014Article
- Evaluation of the inflammatory response in a two-hit acute lung injury model using [Experimental and therapeutic medicine · 2013Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
Abstract
objectivesTo 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.
methodsWe 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.
resultsTwo 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.
conclusionTaken 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.