Lim Wei Keat, Lyashenko Eugenia, Califano Andrea
Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Columbia University, 1130 Saint Nicholas Avenue, New York, NY 10032, USA.
Pac Symp Biocomput. 2009:504-15.
Computational identification of prognostic biomarkers capable of withstanding follow-up validation efforts is still an open challenge in cancer research. For instance, several gene expression profiles analysis methods have been developed to identify gene signatures that can classify cancer sub-phenotypes associated with poor prognosis. However, signatures originating from independent studies show only minimal overlap and perform poorly when classifying datasets other than the ones they were generated from. In this paper, we propose a computational systems biology approach that can infer robust prognostic markers by identifying upstream Master Regulators, causally related to the presentation of the phenotype of interest. Such a strategy effectively extends and complements other existing methods and may help further elucidate the molecular mechanisms of the observed pathophysiological phenotype. Results show that inferred regulators substantially outperform canonical gene signatures both on the original dataset and across distinct datasets.
在癌症研究中，通过计算识别能够经受住后续验证的预后生物标志物仍然是一个悬而未决的挑战。例如，已经开发了几种基因表达谱分析方法来识别可以对与不良预后相关的癌症亚表型进行分类的基因特征。然而，来自独立研究的特征仅显示出最小的重叠，并且在对除了它们所生成的数据集之外的其他数据集进行分类时表现不佳。在本文中，我们提出了一种计算系统生物学方法，该方法可以通过识别与感兴趣的表型呈现因果相关的上游主调节因子来推断稳健的预后标志物。这种策略有效地扩展和补充了其他现有方法，并可能有助于进一步阐明观察到的病理生理表型的分子机制。结果表明，推断出的调节因子在原始数据集和不同数据集上都大大优于经典基因特征。
