Identification of prognostic genes and gene sets for early-stage non-small cell lung cancer using bi-level selection methods.
Identification of prognostic genes and gene sets for early-stage non-small cell lung cancer using bi-level selection methods.
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使用双层选择方法鉴定早期非小细胞肺癌的预后基因和基因集
DOI：
10.1038/srep46164
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发表时间：
2017-04-07
影响因子：
4.6
通讯作者：
Sun J
中科院分区：
文献类型：
--
作者：
Tian S;Wang C;Chang HH;Sun J
与特征选择和基因集合分析不同，双层选择不仅是选择重要基因集合的过程，而且是在这些基因集合中选择重要基因的过程。根据选择的顺序，二级选择方法可以分为三类--正向选择，它首先选择相关的基因组，然后选择相关的单个基因；向后选择，它采取相反的顺序；以及同时选择，它通常借助惩罚回归模型同时执行这两个任务。为了检验非小细胞肺癌(NSCLC)亚型特异性预后基因的存在，我们先前提出了Cox-Filter方法，该方法检验了患者在特定基因诊断后的生存时间、疾病亚型及其相互作用项之间的关系。在本研究中，我们将其进一步扩展到进行向前和向后两个水平的选择。通过仿真和一个NSCLC应用程序，我们证明了在我们的设置下，前向选择算法的性能优于后向选择算法和其他相关算法。这两种方法都很容易理解和解释。因此，对于对探索特定亚型或疾病阶段的基因表达数据的预后价值感兴趣的研究人员来说，它们是有用的工具。
In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting not only important gene sets but also important genes within those gene sets. Depending on the order of selections, a bi-level selection method can be classified into three categories – forward selection, which first selects relevant gene sets followed by the selection of relevant individual genes; backward selection which takes the reversed order; and simultaneous selection, which performs the two tasks simultaneously usually with the aids of a penalized regression model. To test the existence of subtype-specific prognostic genes for non-small cell lung cancer (NSCLC), we had previously proposed the Cox-filter method that examines the association between patients’ survival time after diagnosis with one specific gene, the disease subtypes, and their interaction terms. In this study, we further extend it to carry out forward and backward bi-level selection. Using simulations and a NSCLC application, we demonstrate that the forward selection outperforms the backward selection and other relevant algorithms in our setting. Both proposed methods are readily understandable and interpretable. Therefore, they represent useful tools for the researchers who are interested in exploring the prognostic value of gene expression data for specific subtypes or stages of a disease.