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Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells.

Niida A, Smith AD, Imoto S, Tsutsumi S, Aburatani H, Zhang MQ, Akiyama T - BMC Bioinformatics (2008)

Bottom Line: However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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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Related in: MedlinePlus

Dependency of the correlation value with breast cancer prognosis on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(5), V$E2F1_Q4_01(10), V$NRF1_Q6(15) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their correlation value with breast cancer prognosis are displayed using box plots.
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Figure 5: Dependency of the correlation value with breast cancer prognosis on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(5), V$E2F1_Q4_01(10), V$NRF1_Q6(15) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their correlation value with breast cancer prognosis are displayed using box plots.

Mentions: We also examined regulatory programs associated with prognosis, a more direct measure of tumor malignancy. For each gene, correlation values with survival time were calculated using Cox regression models [24]. Then, we searched for cis-regulatory motifs associated with the correlation values using our method. Our analysis selected V$ELK1_02(10), V$E2F1_Q4_01(5), V$NRF1_Q6(15) and JSP$NF_Y(10) as sequence features positively associated with prognosis, similarly to the analysis for histological grade (Figure 4, Supplementary Table 2 in Additional file 1, and Figure 5). A P-value for a combination of these four motifs was calculated as 7.17 × 10-12 for the test data.


Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells.

Niida A, Smith AD, Imoto S, Tsutsumi S, Aburatani H, Zhang MQ, Akiyama T - BMC Bioinformatics (2008)

Dependency of the correlation value with breast cancer prognosis on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(5), V$E2F1_Q4_01(10), V$NRF1_Q6(15) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their correlation value with breast cancer prognosis are displayed using box plots.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2572072&req=5

Figure 5: Dependency of the correlation value with breast cancer prognosis on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(5), V$E2F1_Q4_01(10), V$NRF1_Q6(15) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their correlation value with breast cancer prognosis are displayed using box plots.
Mentions: We also examined regulatory programs associated with prognosis, a more direct measure of tumor malignancy. For each gene, correlation values with survival time were calculated using Cox regression models [24]. Then, we searched for cis-regulatory motifs associated with the correlation values using our method. Our analysis selected V$ELK1_02(10), V$E2F1_Q4_01(5), V$NRF1_Q6(15) and JSP$NF_Y(10) as sequence features positively associated with prognosis, similarly to the analysis for histological grade (Figure 4, Supplementary Table 2 in Additional file 1, and Figure 5). A P-value for a combination of these four motifs was calculated as 7.17 × 10-12 for the test data.

Bottom Line: However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus