Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model

Ying Cai, Ruben Nogales-Cadenas, Quanwei Zhang, Jhih Rong Lin, Wen Zhang, Kelly O'Brien, Cristina Montagna, Zhengdong D. Zhang

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

Background: Malignant breast cancer with complex molecular mechanisms of progression and metastasis remains a leading cause of death in women. To improve diagnosis and drug development, it is critical to identify panels of genes and molecular pathways involved in tumor progression and malignant transition. Using the PyMT mouse, a genetically engineered mouse model that has been widely used to study human breast cancer, we profiled and analyzed gene expression from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma) during which malignant transition occurs. Results: We found remarkable expression similarity among the four stages, meaning genes altered in the later stages showed trace in the beginning of tumor progression. We identified a large number of differentially expressed genes in PyMT samples of all stages compared with normal mammary glands, enriched in cancer-related pathways. Using co-expression networks, we found panels of genes as signature modules with some hub genes that predict metastatic risk. Time-course analysis revealed genes with expression transition when shifting to malignant stages. These may provide additional insight into the molecular mechanisms beyond pathways. Conclusions: Thus, in this study, our various analyses with the PyMT mouse model shed new light on transcriptomic dynamics during breast cancer malignant progression.

Original languageEnglish (US)
Article number185
JournalBMC Genomics
Volume18
Issue number1
DOIs
StatePublished - Feb 17 2017

Keywords

  • Breast cancer
  • PyMT mouse model
  • RNA-sequencing

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Fingerprint Dive into the research topics of 'Transcriptomic dynamics of breast cancer progression in the MMTV-PyMT mouse model'. Together they form a unique fingerprint.

  • Cite this