肿瘤科
肺癌
基因签名
医学
内科学
生存分析
微阵列分析技术
微阵列
比例危险模型
基因表达谱
基因
基因表达
生物信息学
生物
遗传学
作者
H.-Y. Chen,Sung‐Liang Yu,Chieh‐Hung Chen,G.-C. Chang,C.-Y. Chen,Ang Yuan,Ching-Hsiang Cheng,C.-H. Wang,Harn-Jing Terng,Shu-Fang Kao,Wing‐Kai Chan,H.-N. Li,C.-C. Liu,Seema Singh,W.J. Chen,Jeremy J.W. Chen,Pan‐Chyr Yang
摘要
BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.
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