MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice

Ruben Nogales-Cadenas, Ying Cai, Jhih Rong Lin, Quanwei Zhang, Wen Zhang, Cristina Montagna, Zhengdong Zhang

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Background: MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated. Methods: In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs). Results: We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes. Conclusions: In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Original languageEnglish (US)
Article number75
JournalBreast Cancer Research
Volume18
Issue number1
DOIs
StatePublished - Jul 22 2016

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Viral Tumor Antigens
Gene Expression Regulation
MicroRNAs
Breast Neoplasms
Neoplasm Metastasis
Messenger RNA
Biological Phenomena
Neoplasms
Genes
Small Untranslated RNA
Estrogen Receptors
Cluster Analysis
Disease Progression

Keywords

  • Breast cancer
  • Cancer progression
  • Metastasis
  • MicroRNA
  • PyMT mouse model
  • Regulatory modules

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice. / Nogales-Cadenas, Ruben; Cai, Ying; Lin, Jhih Rong; Zhang, Quanwei; Zhang, Wen; Montagna, Cristina; Zhang, Zhengdong.

In: Breast Cancer Research, Vol. 18, No. 1, 75, 22.07.2016.

Research output: Contribution to journalArticle

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abstract = "Background: MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated. Methods: In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs). Results: We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes. Conclusions: In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.",
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