A ten N6‐methyladenosine‐related long non‐coding RNAs signature predicts prognosis of triple‐negative breast cancer. Issue 6 (2nd May 2021)

• Record Type: Journal Article
• Authors: Wu, Jie; Cai, Yan; Zhao, Gaiping; Li, Maolan
• Is Part Of: Journal of clinical laboratory analysis. Volume 35:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-02
• Subjects: ceRNA network -- long non‐coding RNA -- N6‐methyladenosine -- prognostic signature -- triple‐negative breast cancer; Diagnosis, Laboratory -- Periodicals; Medical laboratory technology -- Periodicals; 616
• DOI: 10.1002/jcla.23779
• Languages: English
• ISSNs: 0887-8013

Abstract

Abstract: Background: Patients with triple‐negative breast cancer (TNBC) face a major challenge of the poor prognosis, and N6‐methyladenosine‐(m6A) mediated regulation in cancer has been proposed. Therefore, this study aimed to explore the prognostic roles of m6A‐related long non‐coding RNAs (LncRNAs) in TNBC. Methods: Clinical information and expression data of TNBC samples were collected from TCGA and GEO databases. Pearson correlation, univariate, and multivariate Cox regression analysis were employed to identify independent prognostic m6A‐related LncRNAs to construct the prognostic score (PS) risk model. Receiver operating characteristic (ROC) curve was used to evaluate the performance of PS risk model. A competing endogenous RNA (ceRNA) network was established for the functional analysis on targeted mRNAs. Results: We identified 10 independent prognostic m6A‐related LncRNAs ( SAMD12 ‐ AS1, BVES ‐ AS1, LINC00593, MIR205HG, LINC00571, ANKRD10 ‐ IT1, CIRBP ‐ AS1, SUCLG2 ‐ AS1, BLACAT1, and HOXB ‐ AS1 ) and established a PS risk model accordingly. Relevant results suggested that TNBC patients with lower PS had better overall survival status, and ROC curves proved that the PS model had better prognostic abilities with the AUC of 0.997 and 0.864 in TCGA and GSE76250 datasets, respectively. Recurrence and PS model status were defined as independent prognostic factors of TNBC. These ten LncRNAs were all differentially expressed in high‐risk TNBC compared with controls. The ceRNA network revealed the regulatory axes for nine key LncRNAs, and mRNAs in the network were identified to function in pathways of cell communication, signaling transduction and cancer. Conclusion: Our findings proposed a ten‐m6A‐related LncRNAs as potential biomarkers to predict the prognostic risk of TNBC. Abstract : Our study firstly investigated the prognostic roles of m6A‐related LncRNAs in TNBC patients. In this study, ten m6A‐related LncRNAs were newly identified to be related with the prognosis of TNBC patients, and these candidate LncRNAs showed abilities to predict the prognostic risk of TNBC.