4.4 Article

An integrative transcriptomics approach identifies miR-503 as a candidate master regulator of the estrogen response in MCF-7 breast cancer cells

Journal

RNA
Volume 22, Issue 10, Pages 1592-1603

Publisher

COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1261/rna.056895.116

Keywords

estrogen receptor alpha (ER alpha); gene expression dynamics; microRNAs; miR-503; breast cancer; ZNF217

Funding

  1. North Carolina's University Cancer Research Fund
  2. National Institutes of Health [R00-DK091318-02, R00-GM102372-04]

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Estrogen receptor alpha (ER alpha) is an important biomarker of breast cancer severity and a common therapeutic target. In response to estrogen, ER alpha stimulates a dynamic transcriptional program including both coding and noncoding RNAs. We generate a fine-scale map of expression dynamics by performing a temporal profiling of both messenger RNAs (mRNAs) and microRNAs (miRNAs) in MCF-7 cells (an ER+ model cell line for breast cancer) in response to estrogen stimulation. We identified three primary expression trends-transient, induced, and repressed-that were each enriched for genes with distinct cellular functions. Integrative analysis of mRNA and miRNA temporal expression profiles identified miR-503 as the strongest candidate master regulator of the estrogen response, in part through suppression of ZNF217-an oncogene that is frequently amplified in cancer. We confirmed experimentally that miR-503 directly targets ZNF217 and that overexpression of miR-503 suppresses MCF-7 cell proliferation. Moreover, the levels of ZNF217 and miR-503 are associated with opposite outcomes in breast cancer patient cohorts, with high expression of ZNF217 associated with poor survival and high expression of miR-503 associated with improved survival. Overall, these data indicate that miR-503 acts as a potent estrogen-induced candidate tumor suppressor miRNA that opposes cellular proliferation and has promise as a novel therapeutic for breast cancer. More generally, our work provides a systems level framework for identifying functional interactions that shape the temporal dynamics of gene expression.

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