Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.
Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.
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DOI：
10.1155/2016/2491671
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发表时间：
2016
影响因子：
--
通讯作者：
Tian S
中科院分区：
文献类型：
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作者：
Zhang L;Wang L;Du B;Wang T;Tian P;Tian S
在非小细胞肺癌（NSCLC）中，腺癌（AC）和鳞状细胞癌（SCC）是两种主要的组织学亚型，分别约占所有肺癌病例的40%和30%。由于AC和SCC的细胞来源、在肺内的位置和生长模式不同，因此它们被认为是不同的疾病。基因表达特征已被证明是区分AC和SCC的有效工具。基因集分析被认为与基因表达标签的鉴定无关。然而，我们发现，一个特定的基因集分析方法，显着性分析的微阵列基因集减少（SAMGSR），可以直接采用选择相关的功能和构建基因表达标签。在这项研究中，我们将SAMGSR应用于NSCLC基因表达数据集。与LASSO等几种新的特征选择算法相比，SAMGSR在预测能力和模型简约性方面具有相当或更好的性能。因此，SAMGSR实际上是一种特征选择算法。此外，我们将SAMGSR分别应用于AC和SCC亚型，以区分其各自的分期，即II期与I期。这两个基因签名之间几乎没有重叠，说明AC和SCC在技术上是不同的疾病。因此，在构建这两种NSCLC亚型的诊断或预后特征时，建议对亚型进行分层分析。
Among non-small cell lung cancer (NSCLC), adenocarcinoma (AC), and squamous cell carcinoma (SCC) are two major histology subtypes, accounting for roughly 40% and 30% of all lung cancer cases, respectively. Since AC and SCC differ in their cell of origin, location within the lung, and growth pattern, they are considered as distinct diseases. Gene expression signatures have been demonstrated to be an effective tool for distinguishing AC and SCC. Gene set analysis is regarded as irrelevant to the identification of gene expression signatures. Nevertheless, we found that one specific gene set analysis method, significance analysis of microarray-gene set reduction (SAMGSR), can be adopted directly to select relevant features and to construct gene expression signatures. In this study, we applied SAMGSR to a NSCLC gene expression dataset. When compared with several novel feature selection algorithms, for example, LASSO, SAMGSR has equivalent or better performance in terms of predictive ability and model parsimony. Therefore, SAMGSR is a feature selection algorithm, indeed. Additionally, we applied SAMGSR to AC and SCC subtypes separately to discriminate their respective stages, that is, stage II versus stage I. Few overlaps between these two resulting gene signatures illustrate that AC and SCC are technically distinct diseases. Therefore, stratified analyses on subtypes are recommended when diagnostic or prognostic signatures of these two NSCLC subtypes are constructed.