A ten N6-methyladenosine-related long non-coding RNAs signature predicts prognosis of triple-negative breast cancer

• Main Authors: Cai, Y. (Author), Li, M. (Author), Wu, J. (Author), Zhao, G. (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2021
• Subjects: 6 n methyladenosine; adenosine; Adenosine; adult; Adult; aged; Aged; ANKRD10 IT1 gene; Article; biological model; Biomarkers, Tumor; BLACAT1 gene; BVES AS1 gene; cancer prognosis; cell communication; ceRNA network; CIRBP AS1 gene; comparative study; competing endogenous RNA; controlled study; female; Female; gene expression; gene expression regulation; Gene Expression Regulation, Neoplastic; gene regulatory network; Gene Regulatory Networks; gene targeting; genetics; HOXB AS1 gene; human; human cell; human tissue; Humans; Kaplan Meier method; Kaplan-Meier Estimate; LINC00571 gene; LINC00593 gene; long non-coding RNA; long untranslated RNA; major clinical study; medical information; messenger RNA; middle aged; Middle Aged; MIR205HG gene; Models, Genetic; mortality; N6-methyladenosine; N-methyladenosine; oncogene; overall survival; pathogenesis; prediction; prognosis; Prognosis; prognostic signature; receiver operating characteristic; RNA; RNA, Long Noncoding; ROC Curve; SAMD12 AS1 gene; signal transduction; SUCLG2 AS1 gene; triple negative breast cancer; Triple Negative Breast Neoplasms; triple-negative breast cancer; tumor marker; unclassified drug
• Online Access: View Fulltext in Publisher

 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. © 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC