Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.
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DOI:
10.1038/msb.2012.37
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发表时间:
2012
影响因子:
9.9
通讯作者:
--
中科院分区:
生物学1区
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--
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大规模的癌症基因组学项目正在多个分子层上分析数百种肿瘤,包括拷贝数,mRNA和miRNA表达,但这些层之间的机制关系通常被排除在计算模型之外。我们开发了一个监督学习框架,用于整合分子谱与调控序列信息,以揭示癌症中的调控程序,包括miRNA介导的调控。我们将我们的方法应用于320个胶质母细胞瘤谱,并确定了关键的miRNA和转录因子作为表达变化的常见或亚型特异性驱动因素。我们证实了预测的前神经亚型调节因子的基因表达特征与PDGF驱动的小鼠模型中的体内表达变化一致。我们测试了两个预测的前神经驱动因子,miR-124和miR-132,两者在前神经肿瘤中低表达,通过在神经球中过表达,并观察到相应的肿瘤表达变化的部分逆转。通过计算分析miRNA在癌症中的作用,可能最终导致针对亚型或个体定制的小RNA治疗。
Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.
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