Chen Hsuan-Yu, Yu Sung-Liang, Chen Chun-Houh, Chang Gee-Chen, Chen Chih-Yi, Yuan Ang, Cheng Chiou-Ling, Wang Chien-Hsun, Terng Harn-Jing, Kao Shu-Fang, Chan Wing-Kai, Li Han-Ni, Liu Chun-Chi, Singh Sher, Chen Wei J, Chen Jeremy J W, Yang Pan-Chyr
National Taiwan University College of Public Health, National Taiwan University College of Medicine, Taipei, Taiwan.
N Engl J Med. 2007 Jan 4;356(1):11-20. doi: 10.1056/NEJMoa060096.
Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC.
We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients.
Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC.
Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.
目前的分期方法不足以预测非小细胞肺癌(NSCLC)的治疗结果。我们开发了一种与NSCLC患者生存密切相关的五基因特征。
我们使用计算机生成的随机数将185个冷冻标本分配用于微阵列分析、实时逆转录聚合酶链反应(RT-PCR)分析或两者。我们研究了125例随机选择的接受NSCLC手术切除患者的肺癌组织冷冻标本中的基因表达,并评估了表达水平与生存之间的关联。我们使用风险评分和决策树分析来开发一个基因表达模型,以预测NSCLC的治疗结果。为了进行验证,我们使用了另外60例患者随机分配的标本。
通过分析微阵列数据和风险评分,确定了16个与NSCLC患者生存相关的基因。我们选择了5个基因(DUSP6、MMD,、STAT1、ERBB3和LCK)进行RT-PCR和决策树分析。五基因特征是无复发生存和总生存的独立预测因子。我们用来自60例NSCLC患者的独立队列数据以及一组来自86例NSCLC患者的已发表微阵列数据对该模型进行了验证。
我们的五基因特征与NSCLC患者的无复发生存和总生存密切相关。