Research Article

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer

Laboratory of Molecular Genetics of Aging & Tumor, Medical Faculty, Kunming University of Science & Technology, Chenggong Campus, 727 South Jingming Road, Kunming, Yunnan 650500, China

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Hongjun Yuan

Laboratory of Molecular Genetics of Aging & Tumor, Medical Faculty, Kunming University of Science & Technology, Chenggong Campus, 727 South Jingming Road, Kunming, Yunnan 650500, China

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Li Chen

Laboratory of Molecular Genetics of Aging & Tumor, Medical Faculty, Kunming University of Science & Technology, Chenggong Campus, 727 South Jingming Road, Kunming, Yunnan 650500, China

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Miaomiao Sheng

https://orcid.org/0000-0002-1422-0595

Laboratory of Molecular Genetics of Aging & Tumor, Medical Faculty, Kunming University of Science & Technology, Chenggong Campus, 727 South Jingming Road, Kunming, Yunnan 650500, China

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Wenru Tang

*Author for correspondence: Tel.: +86 0871 6592 0753;

E-mail Address: tangkmust_tougao@163.com

https://orcid.org/0000-0002-0769-6448

Laboratory of Molecular Genetics of Aging & Tumor, Medical Faculty, Kunming University of Science & Technology, Chenggong Campus, 727 South Jingming Road, Kunming, Yunnan 650500, China

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Published Online:11 Jan 2021https://doi.org/10.2217/bmm-2020-0505

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Abstract

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan–Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

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Vol. 15, No. 1

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History

Received 13 August 2020

Accepted 2 November 2020

Published online 11 January 2021

Published in print January 2021

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Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/bmm-2020-0505

Author contributions

C Yuan, L Chen, designed the experiments and interpreted the data. C Yuan, H Yuan and M Sheng conducted bioinformatics and statistical analyses. C Yuan and W Tang wrote the manuscript. All authors have read and approved the manuscript for publication.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Availability of data & material

The datasets generated during and/or analyzed during the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and TCGA database (https://portal.gdc.cancer.gov/).

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