Min Sun1,2, Di Wu2, Ke Zhou1, Heng Li1, Xingrui Gong2, Qiong Wei2, Mengyu Du2, Peijie Lei3, Jin Zha2, Hongrui Zhu2, Xinsheng Gu4, Dong Huang5. 1. Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China. 2. Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China. 3. The First Clinical School, Hubei University of Medicine, Shiyan, 442000, China. 4. College of Basic Medical Sciences, Hubei University of Medicine, Shiyan, 442000, China. gu.xinsheng@gmail.com. 5. Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China. hd_814@sohu.com.
Abstract
PURPOSE: To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: To identify a lncRNA signature to predict survival of breast cancer (BRCA) patients. METHODS: 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 BRCApatients 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. RESULTS: 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. CONCLUSIONS: 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.
Entities:
Keywords:
Breast cancer; Competing endogenous RNA network; Prognostic signature; The cancer genome atlas; Weighted gene co-expression network analysis