Wang Yulei, Barbacioru Catalin C, Shiffman Dov, Balasubramanian Sriram, Iakoubova Olga, Tranquilli Maryann, Albornoz Gonzalo, Blake Julie, Mehmet Necip N, Ngadimo Dewi, Poulter Karen, Chan Frances, Samaha Raymond R, Elefteriades John A
Applied Biosystems, Foster City, California, United States of America.
PLoS One. 2007 Oct 17;2(10):e1050. doi: 10.1371/journal.pone.0001050.
Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA.
Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set.
This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA.
胸主动脉瘤(TAA)通常无症状,且死亡率高。通过择期手术修复可预防TAA的不良临床结局;然而,识别高危个体却很困难。我们推测外周血细胞中的基因表达模式可能与TAA疾病状态相关。我们的目标是在外周血中识别出一种独特的基因表达特征,以识别TAA高危个体。
分析了94份外周血样本(从58例TAA患者和36例对照中采集)的全基因组基因表达谱。微阵列显著性分析(SAM)确定了表征TAA与正常、升主动脉TAA与降主动脉TAA以及散发性TAA与家族性TAA的潜在特征基因。使用包含36例TAA患者和25例对照的训练集,构建了一个用于检测TAA状态的41基因分类模型,总体准确率达到78±6%。在包含22份TAA样本和11例对照的独立验证集上测试该分类器,总体分类准确率为78%。这41个分类基因通过TaqMan实时PCR检测进一步验证。基于TaqMan数据的分类重复了微阵列结果,在测试集上达到了80%的分类准确率。
本研究在外周血细胞中识别出了可表征TAA状态和TAA亚型的信息丰富的基因表达特征。此外,基于表达特征的41基因分类器可高精度识别TAA患者。外周血中导致这些标志物被识别的转录程序也为主动脉瘤的发病机制提供了见解,并突出了治疗干预的潜在靶点。本研究中通过TaqMan实时PCR验证的分类基因定义了一组有前景的潜在诊断标志物,为基于血液的基因表达检测以促进TAA的早期检测奠定了基础。