Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells.
Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells.
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DOI：
10.1186/1471-2105-9-404
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发表时间：
2008-09-29
影响因子：
3
通讯作者：
Akiyama T
中科院分区：
文献类型：
--
作者：
Niida A;Smith AD;Imoto S;Tsutsumi S;Aburatani H;Zhang MQ;Akiyama T
基因芯片技术揭示了不同表型肿瘤之间转录组的差异，特别是在乳腺肿瘤表型多样性的分子研究方面取得了重大进展。然而，与大量的关于转录组的知识相比，我们对转录组多样性的调控机制知之甚少。为了深入了解推动肿瘤进展的转录程序，我们将乳腺癌的调控序列数据和表达谱整合到贝叶斯网络中，并搜索与给定组织学分级和预后统计学相关的顺式调控基序。我们的分析发现ELK1、E2F、NRF1和NFY结合的基序是与乳腺癌恶性进展正相关的潜在调控基序。结果表明，这4个基序是驱动乳腺癌恶性进展的主要调控基序。我们的方法提供了一个更简洁的描述不同临床表型的乳腺肿瘤之间的转录组多样性。
Microarray technology has unveiled transcriptomic differences among tumors of various phenotypes, and, especially, brought great progress in molecular understanding of phenotypic diversity of breast tumors. However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity. To gain insights into the transcriptional programs that drive tumor progression, we integrated regulatory sequence data and expression profiles of breast cancer into a Bayesian Network, and searched for cis-regulatory motifs statistically associated with given histological grades and prognosis. Our analysis found that motifs bound by ELK1, E2F, NRF1 and NFY are potential regulatory motifs that positively correlate with malignant progression of breast cancer. The results suggest that these 4 motifs are principal regulatory motifs driving malignant progression of breast cancer. Our method offers a more concise description about transcriptome diversity among breast tumors with different clinical phenotypes.
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影响因子：
64.5
作者：
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通讯作者：
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DOI：
10.1073/pnas.0407365101
发表时间：
2004-11-16
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影响因子：
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