title: A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients.
abstract: Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Jie
authors.initials: J
authors.lastname: Sun
authors.affiliation: Department of General Surgery, The Fourth Affiliated Hospital, Harbin medical university, Harbin 150081, China.
authors.forename: Xihai
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Zhenzhen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Maoni
authors.initials: M
authors.lastname: Guo
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Shi
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Liang
authors.initials: L
authors.lastname: Cheng
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Meng
authors.initials: M
authors.lastname: Zhou
mesh_terms: D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality* / Q000473:pathology; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D053208:Kaplan-Meier Estimate; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D062085:RNA, Long Noncoding; D015203:Reproducibility of Results; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor; D062085:RNA, Long Noncoding