A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma. Issue 10 (16th May 2019)

• Record Type: Journal Article
• Authors: He, Yuting; Li, Xingsong; Meng, Yuhuan; Fu, Shuying; Cui, Ying; Shi, Yong; Du, Hongli
• Is Part Of: Journal of cellular biochemistry. Volume 120:Issue 10(2019)
• Page Start: 16692
• Page End: 16702
• Publication Date: 2019-05-16
• Subjects: breast invasive carcinoma -- long noncoding RNA -- overall survival -- prognosis -- The Cancer Genome Atlas database; Cytochemistry -- Periodicals; 572
• DOI: 10.1002/jcb.28927
• Languages: English
• ISSNs: 0730-2312

Abstract

Abstract: Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non‐coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis‐related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood‐based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk‐score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high‐/low‐risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1 . Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer. Abstract : This article is aimed to identify a long non‐coding RNA (lncRNA) signature which could be a prognostic biomarker for breast invasive carcinoma (BRCA). We collected 1052 BRCA patients samples, applied a series of analysis and finally developed an 11‐lncRNA signature, which was validated. And we identified that these lncRNAs could effectively separate clinical patient samples with longer overall survival (OS) than those with shorter OS, providing further insights into the role of lncRNAs in the development of BRCA to us.