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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

Schema of our method. We first calculate correlations between phenotypes and expression values as meta-expression values, while preparing a sequence feature table by searching promoter sequences for cis-regulatory motifs. Cis-regulatory motif data are prepared from two different sources: already known motifs, which are downloaded from databases, and de novo identified motifs, which were discovered by an ab initio motif finder program, DME. Then, associations between sequence features and meta-expression values were inferred by structure learning of Bayesian networks.
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Figure 1: Schema of our method. We first calculate correlations between phenotypes and expression values as meta-expression values, while preparing a sequence feature table by searching promoter sequences for cis-regulatory motifs. Cis-regulatory motif data are prepared from two different sources: already known motifs, which are downloaded from databases, and de novo identified motifs, which were discovered by an ab initio motif finder program, DME. Then, associations between sequence features and meta-expression values were inferred by structure learning of Bayesian networks.

Mentions: To elucidate transcriptional programs in cancer cells, we used a bioinformatics method based on Bayesian networks. We integrated regulatory sequences and global expression profiling data, and searched for cis-regulatory motifs statistically associated with clinical annotation accompanying the expression profiling data (Fig. 1).


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)

Schema of our method. We first calculate correlations between phenotypes and expression values as meta-expression values, while preparing a sequence feature table by searching promoter sequences for cis-regulatory motifs. Cis-regulatory motif data are prepared from two different sources: already known motifs, which are downloaded from databases, and de novo identified motifs, which were discovered by an ab initio motif finder program, DME. Then, associations between sequence features and meta-expression values were inferred by structure learning of Bayesian networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Schema of our method. We first calculate correlations between phenotypes and expression values as meta-expression values, while preparing a sequence feature table by searching promoter sequences for cis-regulatory motifs. Cis-regulatory motif data are prepared from two different sources: already known motifs, which are downloaded from databases, and de novo identified motifs, which were discovered by an ab initio motif finder program, DME. Then, associations between sequence features and meta-expression values were inferred by structure learning of Bayesian networks.
Mentions: To elucidate transcriptional programs in cancer cells, we used a bioinformatics method based on Bayesian networks. We integrated regulatory sequences and global expression profiling data, and searched for cis-regulatory motifs statistically associated with clinical annotation accompanying the expression profiling data (Fig. 1).

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