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. 2009 Apr 10;37(2):133-9.
doi: 10.1152/physiolgenomics.90275.2008. Epub 2009 Jan 27.

Discovery of the gene signature for acute lung injury in patients with sepsis

Judie A Howrylak  1 , Tamas Dolinay, Lorrie Lucht, Zhaoxi Wang, David C Christiani, Jigme M Sethi, Eric P Xing, Michael P Donahoe, Augustine M K Choi
Affiliations

Discovery of the gene signature for acute lung injury in patients with sepsis

Judie A Howrylak et al. Physiol Genomics. .

Abstract

The acute respiratory distress syndrome (ARDS)/acute lung injury (ALI) was described 30 yr ago, yet making a definitive diagnosis remains difficult. The identification of biomarkers obtained from peripheral blood could provide additional noninvasive means for diagnosis. To identify gene expression profiles that may be used to classify patients with ALI, 13 patients with ALI + sepsis and 20 patients with sepsis alone were recruited from the Medical Intensive Care Unit of the University of Pittsburgh Medical Center, and microarrays were performed on peripheral blood samples. Several classification algorithms were used to develop a gene signature for ALI from gene expression profiles. This signature was validated in an independently obtained set of patients with ALI + sepsis (n = 8) and sepsis alone (n = 1). An eight-gene expression profile was found to be associated with ALI. Internal validation found that the gene signature was able to distinguish patients with ALI + sepsis from patients with sepsis alone with 100% accuracy, corresponding to a sensitivity of 100%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 100%. In the independently obtained external validation set, the gene signature was able to distinguish patients with ALI + sepsis from patients with sepsis alone with 88.9% accuracy. The use of classification models to develop a gene signature from gene expression profiles provides a novel and accurate approach for classifying patients with ALI.

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Figures

Fig. 1.
Fig. 1.
Graphical depiction of the algorithm used to develop a gene signature to distinguish patients with acute lung injury (ALI) + sepsis from patients with sepsis alone. SAM, Statistical Analysis of Microarrays; LOOCV, leave-one-out cross-validation.
Fig. 2.
Fig. 2.
Accuracy for each classifier per iteration of gene selection. Starting with the gene with the highest score, as determined by the SAM algorithm, a greedy gene-selection algorithm was used to add the next most highly scored gene for each iteration. This strategy allowed for a process of model selection whereby the most diagnostic genes were selected for a genetic signature to distinguish between patients with ALI + sepsis and sepsis alone. The best performing classifier was the Support Vector Machine, which achieved 100% accuracy with a gene signature composed of the 8 highest scoring genes.
Fig. 3.
Fig. 3.
Heat map for genes present in the gene signature associated with ALI as determined by the optimal classifier. The genes composing the gene signature are listed on the right edge of the heat map. The classes of each of the patients are listed along the bottom edge of the heat map. ARDS, acute respiratory distress syndrome.
Fig. 4.
Fig. 4.
Results of RT-PCR validation of gene expression microarrays.
See this image and copyright information in PMC

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