Howrylak Judie A, Dolinay Tamas, Lucht Lorrie, Wang Zhaoxi, Christiani David C, Sethi Jigme M, Xing Eric P, Donahoe Michael P, Choi Augustine M K
Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA.
Physiol Genomics. 2009 Apr 10;37(2):133-9. doi: 10.1152/physiolgenomics.90275.2008. Epub 2009 Jan 27.
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.
急性呼吸窘迫综合征(ARDS)/急性肺损伤(ALI)在30年前就已被描述,但做出明确诊断仍然困难。从外周血中获取生物标志物可提供额外的非侵入性诊断方法。为了识别可用于对ALI患者进行分类的基因表达谱,从匹兹堡大学医学中心医疗重症监护病房招募了13例ALI + 脓毒症患者和20例单纯脓毒症患者,并对外周血样本进行了微阵列检测。使用了几种分类算法从基因表达谱中开发出ALI的基因特征。该特征在一组独立获得的ALI + 脓毒症患者(n = 8)和单纯脓毒症患者(n = 1)中进行了验证。发现一种八基因表达谱与ALI相关。内部验证发现,该基因特征能够以100%的准确率区分ALI + 脓毒症患者和单纯脓毒症患者,对应灵敏度为100%,特异性为100%,阳性预测值为100%,阴性预测值为100%。在独立获得的外部验证集中,该基因特征能够以88.9%的准确率区分ALI + 脓毒症患者和单纯脓毒症患者。使用分类模型从基因表达谱中开发基因特征为ALI患者的分类提供了一种新颖且准确的方法。