Estrogen receptor α attenuates transforming growth factor-β signaling in breast cancer cells independent from agonistic and antagonistic ligands

Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
Breast Cancer Research and Treatment (Impact Factor: 3.94). 05/2009; 120(2):357-67. DOI: 10.1007/s10549-009-0393-2
Source: PubMed

ABSTRACT To investigate a presumed crosstalk between estrogen receptor alpha (ERalpha) and the TGF-beta signaling pathway in breast cancer, we analyzed the TGF-beta-induced expression of the plasminogen activator inhibitor 1 (PAI-1) gene in ER-positive MCF-7 cells. After siRNA-mediated knock-down of endogenous ERalpha, the transcription level of PAI-1 was upregulated, pointing to an attenuation of TGF-beta signaling by the presence of ERalpha. We verified these findings by a vice versa approach using a primary ER-negative cell model transiently overexpressing either ERalpha or ERbeta. We found that ERalpha, but not ERbeta, led to a strong inhibition of the TGF-beta1 signal, monitored by TGF-beta reporter assays. This attenuation was completely independent of receptor stimulation by beta-estradiol (E2) or inhibition by the pure antagonist ICI 182.780 (ICI). Our results indicate a permanent repression of PAI-1 by ERalpha and suggest a ligand-independent crosstalk between ERalpha and TGF-beta signaling in breast cancer cells.

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Available from: Matthias B Stope, Sep 26, 2015
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    • "Eigengene of tricluster 48 is significantly expressed between 0 and 12 hours, 3 and 12 hours. The KEGG pathway term TGF-beta signaling pathway is meliorated in this tricluster and the crosstalk between TGF-beta signaling pathway and ER α has been reported in a previous study [18]. Genes SKP1, BMPR2 are found to play a role in the enriched pathway. "
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    ABSTRACT: Background Estrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm δ-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ. Results We have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1. Conclusions Thus our integrative approach provides insights into breast cancer prognosis.
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