Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.
Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.
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DOI：
10.1038/msb.2011.17
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发表时间：
2011-04-26
影响因子：
9.9
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DNA拷贝数异常(CNA)是肿瘤基因组的一个特征。在这项工作中，Rebecka Jörnsten、Sven Nelander和他的同事结合网络建模和实验方法分析了CNAs在胶质母细胞瘤中的系统水平效应。DNA拷贝数异常(CNA)是癌症基因组的一个标志。然而，人们对这种变化如何影响全球基因表达知之甚少。我们开发了一个模型框架，EPOC(癌症内源性扰动分析)，用于(1)检测导致疾病的CNA及其对靶基因表达的影响，以及(2)将癌症患者分为长期和短期生存者。我们的方法通过结合全基因组DNA和RNA水平的数据来构建基因表达的因果网络模型。预测分数是通过网络的奇异值分解获得的。通过将EPOC应用于来自癌症基因组图谱联盟的胶质母细胞瘤数据，我们证明了所产生的网络模型包含已知的与疾病相关的中心基因，揭示了有趣的候选中心，并揭示了患者生存的预测因素。在四个胶质母细胞瘤细胞系中的靶向验证支持选定的预测，并暗示P53相互作用蛋白Necdin抑制胶质母细胞瘤细胞生长。我们的结论是，对CNA对基因表达影响的大规模网络建模可能会为人类癌症的生物学提供洞察力。给出了用MatLab和R编写的免费软件。
DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma. DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.