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

Simple item page
dc.contributor.authorDong, Chuanpeng
dc.contributor.authorLiu, Jiannan
dc.contributor.authorChen, Steven X.
dc.contributor.authorDong, Tianhan
dc.contributor.authorJiang, Guanglong
dc.contributor.authorWang, Yue
dc.contributor.authorWu, Huanmei
dc.contributor.authorReiter, Jill L.
dc.contributor.authorLiu, Yunlong
dc.contributor.departmentMedical and Molecular Genetics, School of Medicineen_US
dc.date.accessioned2020-06-23T17:29:14Z
dc.date.available2020-06-23T17:29:14Z
dc.date.issued2020
dc.description.abstractBackground: 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.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationDong, C., Liu, J., Chen, S. X., Dong, T., Jiang, G., Wang, Y., Wu, H., Reiter, J. L., & Liu, Y. (2020). Highly robust model of transcription regulator activity predicts breast cancer overall survival. BMC medical genomics, 13(Suppl 5), 49. https://doi.org/10.1186/s12920-020-0688-zen_US
dc.identifier.urihttps://hdl.handle.net/1805/23054
dc.language.isoen_USen_US
dc.publisherBMCen_US
dc.relation.isversionof10.1186/s12920-020-0688-zen_US
dc.relation.journalBMC Medical Genomicsen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourcePMCen_US
dc.subjectBreast canceren_US
dc.subjectTranscription regulatorsen_US
dc.subjectPrognostic modelen_US
dc.titleHighly robust model of transcription regulator activity predicts breast cancer overall survivalen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
12920_2020_Article_688.pdf
Size:
2.22 MB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.99 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Access Policy Articles
Department of Medical and Molecular Genetics Works