Highly robust model of transcription regulator activity predicts breast cancer overall survival
Open Access
- 3 April 2020
- journal article
- conference paper
- Published by Springer Science and Business Media LLC in BMC Medical Genomics
- Vol. 13 (5), 1-10
- https://doi.org/10.1186/s12920-020-0688-z
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.Keywords
This publication has 39 references indexed in Scilit:
- The Cancer Genome Atlas Pan-Cancer analysis projectNature Genetics, 2013
- PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancerBreast Cancer Research and Treatment, 2013
- NCBI GEO: archive for functional genomics data sets—updateNucleic Acids Research, 2012
- MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibitionThe Journal of Experimental Medicine, 2012
- Estrogen Receptor (ESR1) mRNA Expression and Benefit From Tamoxifen in the Treatment and Prevention of Estrogen Receptor–Positive Breast CancerJournal of Clinical Oncology, 2011
- Heterogeneity in breast cancerJCI Insight, 2011
- Molecular signatures database (MSigDB) 3.0Bioinformatics, 2011
- Gene-Expression Signatures in Breast CancerNew England Journal of Medicine, 2009
- Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profilesProceedings of the National Academy of Sciences of the United States of America, 2005
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002