Multi-analyte network markers for tumor prognosis.
Multi-analyte network markers for tumor prognosis.
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DOI：
10.1371/journal.pone.0052973
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Tan K
中科院分区：
文献类型：
--
作者：
Kim J;Gao L;Tan K
基因表达失调是癌症的标志，由遗传和表观遗传机制引起。各种癌症的表观基因组图谱的快速积累表明了一种新的研究途径，即将表观基因组数据与其他类型的组学数据整合，用于癌症诊断，预后和生物标志物发现。我们引入了MAPIT算法（多分析物通路推理工具），以实现表观基因组，转录组和蛋白质相互作用组数据的原则整合。作为原理证明，我们将MAPIT应用于多形性胶质母细胞瘤（GBM），这是最常见和最具侵袭性的脑肿瘤形式。GBM患者预后的预测标志物报道很少。通过整合mRNA转录组、启动子DNA甲基化组和蛋白质-蛋白质物理相互作用组，我们发现了10个基于表达和3个基于甲基化的网络标记，涉及118个基因。当对额外的GBM患者样本进行测试时，多分析物网络标记物的预后准确性（73.5%）比先前单独基于基因表达或DNA甲基化的预后特征高9.7%和8.6%。我们的研究结果强调了两个新的途径在GBM患者的预后中的关键作用，小GTP酶介导的蛋白运输和泛素化依赖的蛋白降解。更好地了解这两种途径可能会导致GBM患者亚组的个性化治疗。我们的研究表明，整合表观基因组、转录组和相互作用组数据可以提高基于网络的预后标志物的准确性，并导致对癌症的新机制的理解。
Deregulation of gene expression, a hallmark of cancer, is caused by both genetic and epigenetic mechanisms. The rapid accumulation of epigenome maps of various cancers suggests a new avenue of research, namely integrating epigenomic data with other types of omic data for cancer diagnosis, prognosis, and biomarker discovery. We introduce the MAPIT algorithm (Multi Analyte Pathway Inference Tool), to enable principled integration of epigenomic, transcriptomic, and protein interactome data. As a proof-of-principle, we apply MAPIT to glioblastoma multiforme (GBM), the most common and aggressive form of brain tumor. Few predictive markers were reported for the prognosis of GBM patients. By integrating mRNA transcriptome, promoter DNA methylome and protein-protein physical interactome, we find ten expression- and three methylation-based network markers, involving 118 genes. When tested on additional GBM patient samples, the prognostic accuracy of the multi-analyte network markers (73.5%) is 9.7% and 8.6% higher than previous prognostic signatures built on gene expression or DNA methylation alone. Our results highlight the critical role of two novel pathways in the prognosis of GBM patients, small GTPase-mediated protein trafficking and ubiquitination-dependent protein degradation. A better understanding of these two pathways could lead to personalized therapies for subgroups of GBM patients. Our study demonstrates that integrating epigenomic, transcriptomic, and interactomic data can improve the accuracy network-based prognosis markers and lead to novel mechanistic understanding of cancer.
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影响因子：
45.3
作者：
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通讯作者：
Gilbert, Mark R.
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Fouse, Shaun D.;Shen, Yin;Fan, Guoping
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Lefebvre, Celine;Rajbhandari, Presha;Alvarez, Mariano J.;Bandaru, Pradeep;Lim, Wei Keat;Sato, Mai;Wang, Kai;Sumazin, Pavel;Kustagi, Manjunath;Bisikirska, Brygida C.;Basso, Katia;Beltrao, Pedro;Krogan, Nevan;Gautier, Jean;Dalla-Favera, Riccardo;Califano, Andrea
通讯作者：
Califano, Andrea
影响因子：
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作者：
Forbes SA;Bindal N;Bamford S;Cole C;Kok CY;Beare D;Jia M;Shepherd R;Leung K;Menzies A;Teague JW;Campbell PJ;Stratton MR;Futreal PA
通讯作者：
Futreal PA
影响因子：
3
作者：
Kim J;Tan K
通讯作者：
Tan K