An eight-lncRNA signature predicts survival of breast cancer patients: a comprehensive study based on weighted gene co-expression network analysis and competing endogenous RNA network

  • Min Sun
  • Di Wu
  • Ke Zhou
  • Heng Li
  • Xingrui Gong
  • Qiong Wei
  • Mengyu Du
  • Peijie Lei
  • Jin Zha
  • Hongrui Zhu
  • Xinsheng GuEmail author
  • Dong HuangEmail author
Preclinical study



To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients.


A total of 1222 BRCA case and control datasets were downloaded from the TCGA database. The weighted gene co-expression network analysis of differentially expressed mRNAs was performed to generate the modules associated with BRCA overall survival status and further construct a hub on competing endogenous RNA (ceRNA) network. LncRNA signatures for predicting survival of BRCA patients were generated using univariate survival analyses and a multivariate Cox hazard model analysis and validated and characterized for prognostic performance measured using receiver operating characteristic (ROC) curves.


A prognostic score model of eight lncRNAs signature was identified as Prognostic score = (0.121 × EXPAC007731.1) + (0.108 × EXPAL513123.1) + (0.105 × EXPC10orf126) + (0.065 × EXPWT1-AS) + (− 0.126 × EXPADAMTS9-AS1) + (− 0.130 × EXPSRGAP3-AS2) + (0.116 × EXPTLR8-AS1) + (0.060 × EXPHOTAIR) with median score 1.088. Higher scores predicted higher risk. The lncRNAs signature was an independent prognostic factor associated with overall survival. The area under the ROC curves (AUC) of the signature was 0.979, 0.844, 0.99 and 0.997 by logistic regression, support vector machine, decision tree and random forest models, respectively, and the AUCs in predicting 1- to 10-year survival were between 0.656 and 0.748 in the test dataset from TCGA database.


The eight-lncRNA signature could serve as an independent biomarker for prediction of overall survival of BRCA. The lncRNA-miRNA-mRNA ceRNA network is a good tool to identify lncRNAs that is correlated with overall survival of BRCA.


Breast cancer The cancer genome atlas Competing endogenous RNA network Prognostic signature Weighted gene co-expression network analysis 



Long noncoding RNAs


Competing endogenous RNA


Breast cancer


The cancer genome atlas


Differentially expressed mRNAs


Differentially expressed miRNAs


Differentially expressed lncRNAs


Weighted gene co-expression network analysis


Overall survival




microRNA response element


Pearson’s correlation coefficient


Protein–protein interaction network


Gene ontology


Kyoto encyclopedia of genes and genomes


Akaike information criterion


Receiver operating characteristic


The area under the respective ROC curves




Estrogen receptor


Renal cell carcinoma


Hazard ratio


Principal component analysis



The authors gratefully acknowledge The Cancer Genome Atlas pilot project (established by NCI and NHGRI), which made the genomic data and clinical data of BRCA available.

Author contributions

Min Sun, Dong Huang and Xinsheng Gu participated in research design. Min Sun, Di Wu, Mengyu Du and Jin Zha performed data analysis. Min Sun, Mengyu Du and Xinsheng Gu wrote or contributed to the writing of the manuscript.


This research was supported by the Natural Science Foundation of Hubei Provincial Department of Education (Q20182105), Natural Science Foundation of Hubei Province of China (2016CFB530) and Faculty Development Foundation of Hubei University of Medicine (2014QDJZR01), Chen Xiao-ping Foundation for the development of science and technology of Hubei Provincial (CXPJJH11800001-2018333) and Innovation and entrepreneurship training program (201810929009, 201810929068 and 201813249010).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest in this work.

Supplementary material

10549_2019_5147_MOESM1_ESM.docx (7.7 mb)
Supplementary material 1 (DOCX 7900 KB)
10549_2019_5147_MOESM2_ESM.xlsx (51 kb)
Supplementary material 2 (XLSX 51 KB)


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Authors and Affiliations

  1. 1.Department of General Surgery, Taihe HospitalHubei University of MedicineShiyanChina
  2. 2.Department of Anesthesiology, Institute of Anesthesiology, Taihe HospitalHubei University of MedicineShiyanChina
  3. 3.The First Clinical SchoolHubei University of MedicineShiyanChina
  4. 4.College of Basic Medical SciencesHubei University of MedicineShiyanChina

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