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

2009 ◽  
Vol 37 (2) ◽  
pp. 133-139 ◽  
Author(s):  
Judie A. Howrylak ◽  
Tamas Dolinay ◽  
Lorrie Lucht ◽  
Zhaoxi Wang ◽  
David C. Christiani ◽  
...  

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.

PLoS ONE ◽  
2010 ◽  
Vol 5 (7) ◽  
pp. e11485 ◽  
Author(s):  
Isabelle Lesur ◽  
Julien Textoris ◽  
Béatrice Loriod ◽  
Cécile Courbon ◽  
Stéphane Garcia ◽  
...  

2006 ◽  
Vol 34 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Ali Mallakin ◽  
Louis W. Kutcher ◽  
Susan A. McDowell ◽  
Sue Kong ◽  
Rebecca Schuster ◽  
...  

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.


2016 ◽  
Vol 77 (9) ◽  
pp. 961-968 ◽  
Author(s):  
Shohei Ogawa ◽  
Mie Okutani ◽  
Takamitsu Tsukahara ◽  
Nobuo Nakanishi ◽  
Yoshihiro Kato ◽  
...  

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