Highly robust model of transcription regulator activity predicts breast cancer overall survival

Chuanpeng Dong, Jiannan Liu, Steven X. Chen, Tianhan Dong, Guanglong Jiang, Yue Wang, Huanmei Wu, Jill L. Reiter, Yunlong Liu

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Original languageEnglish (US)
Article number49
JournalBMC Medical Genomics
Volume13
DOIs
StatePublished - Apr 3 2020

Keywords

  • Breast cancer
  • Prognostic model
  • Transcription regulators

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Fingerprint Dive into the research topics of 'Highly robust model of transcription regulator activity predicts breast cancer overall survival'. Together they form a unique fingerprint.

  • Cite this