Sample-level enrichment analysis unravels shared stress phenotypes among multiple cancer types.
Sample-level enrichment analysis unravels shared stress phenotypes among multiple cancer types.
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DOI：
10.1186/gm327
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发表时间：
2012-03-29
期刊：
影响因子：
12.3
通讯作者：
Lopez-Bigas N
中科院分区：
文献类型：
--
作者：
Gundem G;Lopez-Bigas N
适应肿瘤微环境中的应激信号是迈向致癌表型的关键一步。细胞为承受不同类型的损伤而获得的适应性改变统称为癌症的应激表型。在这篇文章中，我们探讨了多种癌症类型中不同应激表型的相互关系，并询问这些表型是否可以用来解释肿瘤样本之间的预后差异。我们提出了一种新的方法的基础上富集分析的样品水平（样品级富集分析- SLEA）的表达谱数据集。在不使用关于样品的先验表型信息的情况下，SLEA使用z检验计算每个基因组每个样品的富集分数。该评分用于确定不同患者组中相应途径或模块的相对重要性。我们的分析表明，肿瘤显著上调与染色体不稳定性相关的基因与乳腺癌的预后不良密切相关。此外，在多种肿瘤类型中，这些肿瘤上调衰老旁路转录程序并表现出相似的应激表型。使用SLEA，我们能够发现多种癌症类型的应激表型途径之间的关系。此外，我们表明，SLEA能够识别与临床特征（如生存率）相关的基因集，以及识别不同癌症亚组病理学基础的生物学途径/过程。
Adaptation to stress signals in the tumor microenvironment is a crucial step towards carcinogenic phenotype. The adaptive alterations attained by cells to withstand different types of insults are collectively referred to as the stress phenotypes of cancers. In this manuscript we explore the interrelation of different stress phenotypes in multiple cancer types and ask if these phenotypes could be used to explain prognostic differences among tumor samples. We propose a new approach based on enrichment analysis at the level of samples (sample-level enrichment analysis - SLEA) in expression profiling datasets. Without using a priori phenotypic information about samples, SLEA calculates an enrichment score per sample per gene set using z-test. This score is used to determine the relative importance of the corresponding pathway or module in different patient groups. Our analysis shows that tumors significantly upregulating genes related to chromosome instability strongly correlate with worse prognosis in breast cancer. Moreover, in multiple tumor types, these tumors upregulate a senescence-bypass transcriptional program and exhibit similar stress phenotypes. Using SLEA we are able to find relationships between stress phenotype pathways across multiple cancer types. Moreover we show that SLEA enables the identification of gene sets in correlation with clinical characteristics such as survival, as well as the identification of biological pathways/processes that underlie the pathology of different cancer subgroups.
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