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基因共表达网络分析揭示了不同癌症类型中预后基因的共同系统水平特性。

Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types.

作者信息

Yang Yang, Han Leng, Yuan Yuan, Li Jun, Hei Nainan, Liang Han

机构信息

1] Division of Biostatistics, The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas 77030, USA [2] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.

出版信息

Nat Commun. 2014;5:3231. doi: 10.1038/ncomms4231.

DOI:10.1038/ncomms4231
PMID:24488081
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3951205/
Abstract

Prognostic genes are key molecules informative for cancer prognosis and treatment. Previous studies have focused on the properties of individual prognostic genes, but have lacked a global view of their system-level properties. Here we examined their properties in gene co-expression networks for four cancer types using data from 'The Cancer Genome Atlas'. We found that prognostic mRNA genes tend not to be hub genes (genes with an extremely high connectivity), and this pattern is unique to the corresponding cancer-type-specific network. In contrast, the prognostic genes are enriched in modules (a group of highly interconnected genes), especially in module genes conserved across different cancer co-expression networks. The target genes of prognostic miRNA genes show similar patterns. We identified the modules enriched in various prognostic genes, some of which show cross-tumour conservation. Given the cancer types surveyed, our study presents a view of emergent properties of prognostic genes.

摘要

预后基因是对癌症预后和治疗具有重要信息价值的关键分子。以往的研究主要集中在单个预后基因的特性上，但缺乏对其系统层面特性的整体认识。在此，我们利用来自“癌症基因组图谱”的数据，研究了四种癌症类型的基因共表达网络中预后基因的特性。我们发现，预后mRNA基因往往不是枢纽基因（具有极高连接性的基因），且这种模式在相应的癌症类型特异性网络中是独特的。相比之下，预后基因在模块（一组高度相互连接的基因）中富集，尤其是在不同癌症共表达网络中保守的模块基因中。预后miRNA基因的靶基因也表现出类似的模式。我们确定了富含各种预后基因的模块，其中一些显示出跨肿瘤的保守性。鉴于所调查的癌症类型，我们的研究展示了预后基因的新兴特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/e64fe2288f08/nihms555419f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/1ca4df52b212/nihms555419f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/82d8fa3d36f6/nihms555419f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/3bc57d105dd4/nihms555419f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/e64fe2288f08/nihms555419f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/1ca4df52b212/nihms555419f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/82d8fa3d36f6/nihms555419f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/3bc57d105dd4/nihms555419f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7e/3951205/e64fe2288f08/nihms555419f4.jpg

相似文献

[1]
Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types.

Nat Commun. 2014

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[10]
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本文引用的文献

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