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New Analysis Framework Incorporating Mixed Mutual Information and Scalable Bayesian Networks for Multimodal High Dimensional Genomic and Epigenomic Cancer Data

结合混合互信息和可扩展贝叶斯网络的多模态高维基因组和表观基因组癌症数据的新分析框架

  • 影响因子:3.60
  • DOI:10.3389/fgene.2020.00648
  • 作者列表:"Xichun Wang","Sergio Branciamore","Grigoriy Gogoshin","Shuyu Ding","Andrei S. Rodin
  • 发表时间:2020-06-18
Abstract

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.) and (ii) a flexible boundary between the variable selection and network modeling stages — the boundary that can be adjusted in accordance with the investigators’ BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.

摘要

我们提出了一种新的两阶段分析策略,在大型多模态基因组癌症数据库,如癌症基因组图谱 (TCGA) 中发现与特定癌症结局相关的候选基因。在第一阶段,我们使用混合互信息进行变量选择; 在第二阶段,我们使用可扩展贝叶斯网络 (BN) 建模来识别候选基因及其相互作用。所提出的方法的两个关键特征是 (i) 处理混合数据类型 (连续和离散,基因组,表观基因组等) 的能力和 (ii)变量选择和网络建模阶段之间的灵活边界-可以根据调查人员的 BN 软件可扩展性和硬件实现进行调整的边界。这两个方面导致所提出的分析框架具有很高的普遍性。我们将上述策略应用于三个不同的 TCGA 数据集 (LGG,脑低级胶质瘤; HNSC,头颈部鳞状细胞癌; STES,胃和食管癌),将多模式分子信息 (SNPs 、 mRNA 表达、 DNA 甲基化) 与两个临床结局变量 (肿瘤状态和患者生存率) 联系起来。我们确定了 11 个候选基因,其中 6 个已经直接与癌症文献有关。我们的分析提示的一个新的 LGG 预后因子,TMPRSS11F II 型跨膜丝氨酸蛋白酶的甲基化,为后续研究提供了有趣的方向。

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影响因子:4.88
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影响因子:6.50
发表时间:2020-03-31
来源期刊:Cancer letters
DOI:10.1016/j.canlet.2019.12.039
作者列表:["Zhou Z","Zhou Q","Wu X","Xu S","Hu X","Tao X","Li B","Peng J","Li D","Shen L","Cao Y","Yang L"]

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