Identifying Master Regulators of Cancer and Their Downstream Targets by Integrating Genomic and Epigenomic Features
Identifying Master Regulators of Cancer and Their Downstream Targets by Integrating Genomic and Epigenomic Features
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通过整合基因组和表观基因组特征来识别癌症的主调控因子及其下游靶标
DOI：
10.1142/9789814447973_0013
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发表时间：
2012
影响因子：
--
通讯作者：
S. Plevritis
中科院分区：
文献类型：
--
作者：
O. Gevaert;S. Plevritis
表征基因组、表观基因组和转录组的大量分子数据正变得可用于各种癌症。目前的挑战是整合这些不同层次的分子生物学信息，以创建一个更全面的观点的关键生物学过程的癌症。我们开发了一种生物计算算法，该算法整合了拷贝数、DNA甲基化和基因表达数据，以研究癌症的主要调节因子并识别其靶点。我们的算法首先生成一个候选驱动基因的列表的基础上的理由，在一个子集的样品中的多个基因组事件驱动的基因是不太可能被随机解除管制。然后，我们从候选驱动程序中选择主调节器，并通过推断基因表达的潜在调节网络来识别它们的靶标。我们应用我们的生物计算算法来识别多形性胶质母细胞瘤（GBM）和浆液性卵巢癌中的主要调节因子及其靶点。我们的研究结果表明，候选驱动程序的表达更可能受到拷贝数变异的影响，而不是DNA甲基化。接下来，我们选择了主调节器，并使用模块网络分析确定了它们的下游目标。作为概念验证，我们表明GBM和卵巢癌模块网络概括了这些癌症中的已知过程。此外，我们还确定了以前没有报道过的主监管机构，并建议他们可能的作用。总之，关注其表达可以通过其基因组和表观基因组畸变来解释的基因是鉴定癌症的主要调节因子的有希望的策略。
Vast amounts of molecular data characterizing the genome, epigenome and transcriptome are becoming available for a variety of cancers. The current challenge is to integrate these diverse layers of molecular biology information to create a more comprehensive view of key biological processes underlying cancer. We developed a biocomputational algorithm that integrates copy number, DNA methylation, and gene expression data to study master regulators of cancer and identify their targets. Our algorithm starts by generating a list of candidate driver genes based on the rationale that genes that are driven by multiple genomic events in a subset of samples are unlikely to be randomly deregulated. We then select the master regulators from the candidate driver and identify their targets by inferring the underlying regulatory network of gene expression. We applied our biocomputational algorithm to identify master regulators and their targets in glioblastoma multiforme (GBM) and serous ovarian cancer. Our results suggest that the expression of candidate drivers is more likely to be influenced by copy number variations than DNA methylation. Next, we selected the master regulators and identified their downstream targets using module networks analysis. As a proof-of-concept, we show that the GBM and ovarian cancer module networks recapitulate known processes in these cancers. In addition, we identify master regulators that have not been previously reported and suggest their likely role. In summary, focusing on genes whose expression can be explained by their genomic and epigenomic aberrations is a promising strategy to identify master regulators of cancer.
肝细胞生长因子可降低 EGFR-T790M 突变型肺癌对不可逆表皮生长因子受体抑制剂的敏感性。
影响因子：
11.5
作者：
Yamada T;Yano S;et al.
通讯作者：
et al.