Research Papers:

Gene regulatory pattern analysis reveals essential role of core transcriptional factors’ activation in triple-negative breast cancer

Li Min, Cheng Zhang, Like Qu, Jialiang Huang, Lan Jiang, Jiafei Liu, Luca Pinello, Guo-Cheng Yuan and Chengchao Shou _

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Oncotarget. 2017; 8:21938-21953. https://doi.org/10.18632/oncotarget.15749

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Li Min1,2,3,*, Cheng Zhang1,*, Like Qu1, Jialiang Huang2,3, Lan Jiang2,3, Jiafei Liu1, Luca Pinello2,3, Guo-Cheng Yuan2,3, Chengchao Shou1

1Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Departments of Biochemistry and Molecular Biology, Peking University Cancer Hospital & Institute, Beijing 100036, P. R. China

2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA

3Harvard T. H. Chan School of Public Heath, Boston, MA 02115, USA

*These authors have contributed equally to this work

Correspondence to:

Chengchao Shou, email: [email protected]

Guo-Cheng Yuan, email: [email protected]

Keywords: gene regulatory pattern, network analysis, transcriptional factors, TNBC

Received: October 27, 2016     Accepted: January 10, 2017     Published: February 27, 2017


Background: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. Genome-scale molecular characteristics and regulatory mechanisms that distinguish TNBC from other subtypes remain incompletely characterized.

Results: By combining gene expression analysis and PANDA network, we defined three different TF regulatory patterns. A core TNBC-Specific TF Activation Driven Pattern (TNBCac) was specifically identified in TNBC by computational analysis. The essentialness of core TFs (ZEB1, MZF1, SOX10) in TNBC was highlighted and validated by cell proliferation analysis. Furthermore, 13 out of 35 co-targeted genes were also validated to be targeted by ZEB1, MZF1 and SOX10 in TNBC cell lines by real-time quantitative PCR. In three breast cancer cohorts, non-TNBC patients could be stratified into two subgroups by the 35 co-targeted genes along with 5 TFs, and the subgroup that more resembled TNBC had a worse prognosis.

Methods: We constructed gene regulatory networks in breast cancer by Passing Attributes between Networks for Data Assimilation (PANDA). Co-regulatory modules were specifically identified in TNBC by computational analysis, while the essentialness of core translational factors (TF) in TNBC was highlighted and validated by in vitro experiments. Prognostic effects of different factors were measured by Log-rank test and displayed by Kaplan-Meier plots.

Conclusions: We identified a core co-regulatory module specifically existing in TNBC, which enabled subtype re-classification and provided a biologically feasible view of breast cancer.

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