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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 differential expression between G1 and G3 breast tumors on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(20), V$E2F1_Q4_01(10), V$NRF1_Q6(10) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their differential expression values between G1 and G3 are displayed using box plots.
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Figure 3: Dependency of differential expression between G1 and G3 breast tumors on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(20), V$E2F1_Q4_01(10), V$NRF1_Q6(10) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their differential expression values between G1 and G3 are displayed using box plots.

Mentions: We next investigated how differential expression between G1 and G3 tumors depends on these four sequence features. We divided genes into 16 groups based on patterns of these four sequence features, and differences in distribution of their expression values were examined (see Supplementary Table 1 in Additional file 1). The box plots in Figure 3 summarize the results. For clarity, gene groups of similar distributions were gathered to form one group. These results indicate that these sequence features are additively associated with upregulation of gene expression in G3 populations.


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 differential expression between G1 and G3 breast tumors on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(20), V$E2F1_Q4_01(10), V$NRF1_Q6(10) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their differential expression values between G1 and G3 are displayed using box plots.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Dependency of differential expression between G1 and G3 breast tumors on sequence features. Genes are divided into five groups based on patterns of four sequence features, V$ELK1_02(20), V$E2F1_Q4_01(10), V$NRF1_Q6(10) and JSP$NF_Y(10) (the left red boxes indicates the presence of sequence features). The distributions of their differential expression values between G1 and G3 are displayed using box plots.
Mentions: We next investigated how differential expression between G1 and G3 tumors depends on these four sequence features. We divided genes into 16 groups based on patterns of these four sequence features, and differences in distribution of their expression values were examined (see Supplementary Table 1 in Additional file 1). The box plots in Figure 3 summarize the results. For clarity, gene groups of similar distributions were gathered to form one group. These results indicate that these sequence features are additively associated with upregulation of gene expression in G3 populations.

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