LncRNAs are involved in carcinogenesis by performing their multiple biological functions. Scientists have revealed a number of mechanisms of lncRNAs in multiple malignant tumors including breast cancer. For example, lncRNAs act as a signal or decoy to promote or suppress gene expression . The lncRNAs regulate the translation of mRNAs and control their stability via forming double-stranded RNA with mRNAs or regulate protein stability by binding . The expression of more and more lncRNAs are abnormal in cancers, and their potential as possible prognostic biomarkers can be used as therapeutic targets [15, 24].
In this study, we firstly screened out the differentially expressed lncRNAs, and next identified three prognostic lncRNAs: CYTOR, MIR4458HG and MAPT-AS1. CYTOR is also called LINC00152, previous studies showed that the expression of lncRNA CYTOR was up-regulated in multiple malignant diseases, such as gastrointestinal cancer, liver cancer, lung adenocarcinoma and esophageal squamous cell carcinoma [25-28]. Yue et al.  showed that CYTOR promoted metastasis of colon cancer through Wnt/β-catenin signal pathway. Reon et al.  reported that CYTOR promoted invasion via a 3'-hairpin structure, and as a effective biomarker for predicting survival of glioblastoma. As far as we knew, there were just a few of studies on CYTOR in breast cancer. It might be involved in the EGFR/mammalian target of rapamycin pathway, which promoted triple negative breast cancer progression by affecting the stability of PTEN protein . Previous study indicated the high expression of MAPT-AS1 was correlated with better survival in breast cancer . However, we have not found any report about MIR4458HG related to breast cancer. Our data showed above three lncRNAs were associated with OS in breast cancer. Furthermore, MAPT-AS1 and MIR4458HG were lower expression in the high-risk group compared with the low-risk group, respectively, while CYTOR was higher expression in the high-risk group, indicated that our results were consistent with previous study. However, the studies about the three lncRNAs are still rare reports in breast cancer, so they are worth to further study as the good targets in the future.
We also structured a prognostic signature with three lncRNAs according to the coefficient from the multivariate cox regression analysis and the expression profiles. We calculated the RS for each patient in selected 565 cases of breast cancer and divided them into two groups of low-risk and high-risk. We found that patients in the high-risk group showed significantly shorter OS than those in the low-risk group, it might be used as an independent predictor for OS. Moreover, the ROC curve analysis suggested that three lncRNAs revealed high sensitivity and specificity of survival prediction in our model. As far as we knew, this was the first investigation to establish a prognostic signature with RNA-sequence, for the survival evaluation of 565 cases with breast cancer basing on TCGA data. Previous studies explored a few of prognostic signatures with lncRNAs from another database-Gene Expression Omnibus (GEO). For instance, Sun et al.  analyzed lncRNA expression profiles in breast cancer via repurposing microarray probes from three GEO datasets (GSE25066, GSE4922, and GSE1456). Their results showed that an expression pattern of nine lncRNAs was associated with metastasis-free survival (MFS), including RP11-482H16.1, AC010729.1, RP11-983P16.4, FOXD3-AS1, LINC01249, AC096574.4, AC015971.2, AC012487.2 and RP11-15A1.2. Univariate analysis demonstrated that the nine-lncRNA signature was a prognostic biomarker to predict MFS of breast cancer. The ROC curve analysis showed the AUC of prognostic signature was 0.693. Similarly, Zhou et al.  identified a twelve –lncRNA prognostic signature in recurrence-free survival (RFS) of breast cancer from GEO datasets. These lncRNAs included RP1-34M23.5, RP11-202K23.1, RP11-560G2.1, RP4-613B23.1, RP11-360F5.1, CTD-2031P19.5 RP4-591L5.2, RP13-104F24.2, RP11-506D12.5, ERVH48-1, RP11-247A12.8 and SNHG7. However, they selected MFS and RFS as research endpoint, respectively, while we used OS as endpoint in this study, because OS directly reflected the survival of cancer patients. In addition, the sensitivity and specificity of our model were superior to the study conducted by Sun et al. Moreover, they only conducted univariate analysis without multivariate analysis, which might affect the final prognostic value. Zhou et al. only mined data from microarray without making in-depth analysis, such as co-expression network analysis with mRNA, enrichment analysis and prediction of biological function, and so on. We have made further analysis and screened out mRNAs, which were associated with CYTOR, MIR4458HG and MAPT-AS1 by co-expression network analysis. We also applied GO terms and KEGG pathways to predict potential biological processes of the three lncRNAs with mRNAs, and obtained 1110 terms enrichment, including 954 BP terms, 24 KEGG pathways, and so on. There were a number of functions, including post-translational protein phosphorylation, positive regulation of cell migration, regulation of apoptotic signaling pathway, positive regulation of cell adhesion, etc. in relationship with important biological processes and signaling pathways in cancer. Their functions above were associated with the three lncRNAs, therefore, three lncRNAs which we screened out might be as the targets to influence the onset and progress of breast cancer. Sun et al. and Zhou et al. applied additional independent GEO datasets to validate their lncRNA signatures. We also attempted to validate the prognostic value of the three-lncRNA signature from GEO database. Regrettably, insufficient cases of these three lncRNAs could be provided from these GEO datasets, therefore, the prognostic value of the three-lncRNA signature needs to be further validated with other methods, and confirmed with a larger sample size in the future.