Research Paper Volume 11, Issue 18 pp 7525—7536

Identification of a novel microRNA recurrence-related signature and risk stratification system in breast cancer

Jianguo Lai1, *, , Bo Chen1, *, , Guochun Zhang1, *, , Yulei Wang1, , Hsiaopei Mok1, , Lingzhu Wen1, , Zihao Pan2,4, , Fengxi Su2,3, , Ning Liao1, ,

  • 1 Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China
  • 2 Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
  • 3 Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
  • 4 Department of Thoracic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
* Equal contribution

Received: December 4, 2018       Accepted: September 5, 2019       Published: September 23, 2019
How to Cite

Copyright © 2019 Lai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Increasing evidence has revealed that microRNAs (miRNAs) play vital roles in breast cancer (BC) prognosis. Thus, we aimed to identify recurrence-related miRNAs and establish accurate risk stratification system in BC patients. A total of 381 differentially expressed miRNAs were confirmed by analyzing 1044 BC tissues and 102 adjacent normal samples from The Cancer Genome Atlas (TCGA). Then, based on the association between each miRNAs and disease-free survival (DFS), we identified miRNA recurrence-related signature to construct a novel prognostic nomogram using Cox regression model. Target genes of the four miRNAs were analyzed via Gene Ontology and KEGG pathway analyses. Time-dependent receiver operating characteristic analysis indicated that a combination of the miRNA signature and tumor-node-metastasis (TNM) stage had better predictive performance than that of TNM stage (0.710 vs 0.616, P<0.0001). Furthermore, risk stratification analysis suggested that the miRNA-based model could significantly classify patients into the high- and low-risk groups in the two cohorts (all P<0.0001), and was independent of other clinical features. Functional enrichment analysis demonstrated that the 46 target genes mainly enrichment in important cell biological processes, protein binding and cancer-related pathways. The miRNA-based prognostic model may facilitate individualized treatment decisions for BC patients.


BC: breast cancer; TNM: tumor-node-metastasis; LVI: lymphovascular invasion; miRNAs: MicroRNAs; DEMs: differentially expressed miRNAs; DFS: disease-free survival; TCGA: The Cancer Genome Atlas; IQR: interquartile range; CPHR: Cox proportional hazards regression; HR: hormone receptor; AUC: the area under the curve; ER: estrogen receptor; Her2: human epithelial growth factor receptor 2; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes analysis; ROC: receiver operating characteristic; HR: hormone receptor; DAVID: the Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Tool.