Abstract
Abstract Metastasis is a major cause of death in breast cancer patients. Breast cancer cells at the primary site gain metastatic ability through epithelial-mesenchymal transition (EMT). During the process, cancer cells lose cell-cell adhesion mediated by E-cadherin repression and gain motility and invasive properties to penetrate the basement membrane and enter the bloodstream or lymphatic system in order to travel to distant metastatic sites. To investigate the underlying gene regulation mechanism of EMT, we established an EMT gene regulatory network. We first built and validated an EMT signal pathway by Ingenuity Knowledge Base, a repository of expertly curated biological interactions and functional annotations from literatures. Based on gene profiling data of 590 breast cancer patients from The Cancer Genome Atlas (TCGA) and over 370 breast cancer patients from Gene Expression Omnibus (GEO), we modeled the gene regulation mechanism of each gene and its expression regulators in the EMT signal pathway in a stochastic Markov Chain model. In order to identify biomarkers and to determine the dynamical transition process at molecular level during EMT, it is important to identify the corresponding gene expression patterns of epithelial and mesenchymal cells. Several studies have already demonstrated that stable gene expression patterns correspond to stable cell phenotypes. In this study, we reported a cost-efficient computational algorithm developed to identify these stable gene signatures and validate them with gene expression profiles of human mammary epithelial cells and transduced mesenchymal cells. Citation Format: YANG CONG, Ming Zhan, Stephen T. Wong. Network-based identification of gene signatures of epithelial-mesenchymal transition in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3998. doi:10.1158/1538-7445.AM2014-3998
Published Version
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