Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction
Prognostic model of pulmonary adenocarcinoma by expression profiling of eight genes as determined by quantitative real-time reverse transcriptase polymerase chain reaction
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DOI：
10.1200/jco.2004.04.109
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发表时间：
2004-03-01
影响因子：
45.3
通讯作者：
Mitsudomi, T
中科院分区：
文献类型：
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作者：
Endoh, H;Tomida, S;Mitsudomi, T
最近，一些表达谱实验表明，腺癌可以分为亚组，也反映了病人的生存。在这项研究中，我们检查了44个基因的表达模式，这些研究中选择，以测试他们的表达模式是否与预后在我们的队列，以及建立一个预后模型适用于临床实践。患者和方法表达水平测定85例腺癌患者的定量逆转录聚合酶链反应。进行聚类分析，并建立预后模型的比例风险模型，使用逐步的方法。结果分层聚类分为三个主要组的情况下，和组B，包括21例，有显着较差的生存（P = 0.0297）。接下来，我们试图识别出较少数量的具有特定预测价值的基因，并选择了8个基因（PTK7、CIT、SCNN1A、PGES、ERO1L、ZWINT和2个EST）。然后，我们计算了一个风险指数，该指数被定义为基因表达值的线性组合，并由其估计的回归系数加权。多变量分析显示，风险指数是一个显著的独立预后因素（P = .0021）。此外，该模型的鲁棒性得到了证实，使用一个独立的一组21例患者（P = .0085）。结论通过分析一个合理的小数目的基因，腺癌患者可以分层，根据他们的预后。该预后模型可用于未来的治疗决策。(C)2004年，美国临床肿瘤学会。
Purpose Recently, several expression-profiling experiments have shown that adenocarcinoma can be classified into subgroups that also reflect patient survival. In this study, we examined the expression patterns of 44 genes selected by these studies to test whether their expression patterns were relevant to prognosis in our cohort as well, and to create a prognostic model applicable to clinical practice,Patients and Methods Expression levels were determined in 85 adenocarcinoma patients by quantitative reverse transcriptase polymerase chain reaction. Cluster analysis was performed, and a prognostic model was created by the proportional hazards model using a stepwise method.Results Hierarchical clustering divided the cases into three major groups, and group B, comprising 21 cases, had significantly poor survival (P = .0297). Next, we tried to identify a smaller number of genes of particular predictive value, and eight genes (PTK7, CIT, SCNN1A, PGES, ERO1L, ZWINT, and two ESTs) were selected. We then calculated a risk index that was defined as a linear combination of gene expression values weighted by their estimated regression coefficients. The risk index was a significant independent prognostic factor (P = .0021) by multivariate analysis. Furthermore, the robustness of this model was confirmed using an independent set of 21 patients (P = .0085).Conclusion By analyzing a reasonably small number of genes, patients with adenocarcinoma could be stratified according to their prognosis. The prognostic model could be applicable to future decisions concerning treatment.(C) 2004 by American Society of Clinical Oncology.