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

• Published in: BMC medical genomics 2020-04, Vol.13 (Suppl 5), p.49-10, Article 49
• Main Authors: Dong, Chuanpeng, Liu, Jiannan, Chen, Steven X, Dong, Tianhan, Jiang, Guanglong, Wang, Yue, Wu, Huanmei, Reiter, Jill L, Liu, Yunlong
• Format: Article
• Language: English
• Subjects: Binding sites; Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Cancer genetics; Cancer patients; Cancer research; Cancer treatment; Carcinoma; Cell cycle; Clinical outcomes; Computational Biology - methods; Datasets; DNA biosynthesis; DNA replication; FDA approval; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genomes; Genomics; Humans; Medical prognosis; MicroRNAs; Models, Biological; Patient outcomes; Patients; Prediction models; Prognosis; Prognostic model; Promoter Regions, Genetic; Survival analysis; Survival Rate; Transcription; Transcription (Genetics); Transcription factors; Transcription regulators; Transcription, Genetic; Tumors
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-020-0688-z

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. 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. 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. Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.