一种新的癌症转录组荟萃分析方法揭示了癌细胞中普遍存在的转录网络。

A. Niida, S. Imoto, Masao Nagasaki, R. Yamaguchi, S. Miyano
{"title":"一种新的癌症转录组荟萃分析方法揭示了癌细胞中普遍存在的转录网络。","authors":"A. Niida, S. Imoto, Masao Nagasaki, R. Yamaguchi, S. Miyano","doi":"10.1142/9781848165786_0010","DOIUrl":null,"url":null,"abstract":"Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":"637 1","pages":"121-31"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.\",\"authors\":\"A. Niida, S. Imoto, Masao Nagasaki, R. Yamaguchi, S. Miyano\",\"doi\":\"10.1142/9781848165786_0010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.\",\"PeriodicalId\":73143,\"journal\":{\"name\":\"Genome informatics. International Conference on Genome Informatics\",\"volume\":\"637 1\",\"pages\":\"121-31\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genome informatics. International Conference on Genome Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/9781848165786_0010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome informatics. International Conference on Genome Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/9781848165786_0010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

尽管微阵列技术已经揭示了各种癌症表型的转录组多样性,但控制它们的转录程序尚未得到很好的阐明。为了解码控制癌症转录组的转录程序,我们最近开发了一种称为EEM的计算方法,该方法从由先前生物学知识(如TF结合基序)定义的指定基因集中搜索表达模块。在本文中,我们扩展了EEM方法来预测癌症转录网络。从功能性TF结合基序和EEM识别的表达模块开始,我们预测了包含调控TF、相关GO术语以及TF结合基序之间相互作用的癌症转录网络。为了系统地分析广泛类型癌症的转录程序,我们将基于eem的网络预测方法应用于从公共数据库收集的122个微阵列数据集。数据集包含约15000个不同组织来源的肿瘤样本,包括乳腺、结肠、肺等。这项基于EEM的荟萃分析成功地揭示了在大部分癌症转录组中起作用的普遍癌症转录网络;它们包括细胞周期和免疫相关子网络。本研究证明了EEM的广泛适用性,并为全面了解癌细胞的转录网络开辟了一条道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.
Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Docking-calculation-based method for predicting protein-RNA interactions. Sign: large-scale gene network estimation environment for high performance computing. Linear regression models predicting strength of transcriptional activity of promoters. Database for crude drugs and Kampo medicine. Mechanism of cell cycle disruption by multiple p53 pulses.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1