Cancer Genetic Network Inference Using Gaussian Graphical Models
Cancer Genetic Network Inference Using Gaussian Graphical Models
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DOI：
10.1177/1177932219839402
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发表时间：
2019-04-08
影响因子：
5.8
通讯作者：
Duan, Zhong-Hui
中科院分区：
文献类型：
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作者：
Zhao, Haitao;Duan, Zhong-Hui
癌症基因组图谱（TCGA）提供了丰富的资源，可用于了解基因如何在癌细胞中相互作用，并收集了许多类型的人类癌症的RNA-Seq基因表达数据。然而，挖掘数据以揭示隐藏的基因相互作用模式仍然是一个挑战。高斯图形模型（Gaussian graphical model， GGM）通常用于遗传网络的学习，因为它定义了一个无向的图形结构，揭示了基因的条件依赖性。在这项研究中，我们主要利用RNA-Seq表达数据和图形套索GGM来推断15种特定类型人类癌症中的基因相互作用。我们利用相应的京都基因百科全书和基因组通路图来定义相关基因的子集。从TCGA中提取实体癌性肿瘤和正常组织中相关基因亚群的RNA-Seq表达水平。对基因表达数据集进行清理和格式化，然后使用图形套索GGM推断出每种癌症类型对应的遗传网络。推断的网络揭示了基因在表达水平上稳定的条件依赖性，并证实了磷酸化肌苷3激酶(PI3K)/AKT/mTOR和Ras/Raf/MEK/ERK两个关键信号通路中编码蛋白的基因在人类癌变过程中发挥的重要作用。这些稳定的依赖性阐明了与许多不同的人类癌症有关的基因之间的表达水平相互作用。对推断的遗传网络进行了检查，以进一步确定和表征癌症特有的一系列基因相互作用。我们的研究揭示的跨癌遗传相互作用为癌症生物学家提出强有力的假设提供了另一套知识，从而可以有效地进行进一步的生物学研究。
The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.