Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients

• Main Authors: Jian-ying Ma, Shao-hua Liu, Jie Chen, Qin Liu
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Bioengineered
• Subjects: breast cancer; metabolism; recurrence-free survival; risk score
• Online Access: http://dx.doi.org/10.1080/21655979.2021.1953216

Metabolism affects the development, progression, and prognosis of various cancers, including breast cancer (BC). Our aim was to develop a metabolism-related long non-coding RNA (lncRNA) signature to assess the prognosis of BC patients in order to optimize treatment. Metabolism-related genes between breast tumors and normal tissues were screened out, and Pearson correlation analysis was used to investigate metabolism-related lncRNAs. In total, five metabolism-related lncRNAs were enrolled to establish prognostic signatures. Kaplan-Meier plots and the receiver operating characteristic (ROC) curves demonstrated good performance in both training and validation groups. Further analysis demonstrated that the signature was an independent prognostic factor for BC. A nomogram incorporating risk score and tumor stage was then constructed to evaluate the 3 – and 5-year recurrence-free survival (RFS) in patients with BC. In conclusion, this study identified a metabolism-related lncRNA signature that can predict RFS of BC patients and established a prognostic nomogram that helps guide the individualized treatment of patients at different risks.