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

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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.

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Most cited references 48

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limma powers differential expression analyses for RNA-sequencing and microarray studies

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limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Contributors

Jie Wu:

ORCID: https://orcid.org/0000-0003-4245-875X

jiewu82@sjtu.edu.cn

Journal

Journal ID (nlm-ta): J Clin Lab Anal

Journal ID (iso-abbrev): J Clin Lab Anal

Journal ID (doi): 10.1002/(ISSN)1098-2825

Journal ID (publisher-id): JCLA

Title: Journal of Clinical Laboratory Analysis

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 0887-8013

ISSN (Electronic): 1098-2825

Publication date (Electronic): 02 May 2021

Publication date Collection: June 2021

Volume: 35

Issue: 6 ( doiID: 10.1002/jcla.v35.6 )

Electronic Location Identifier: e23779

Affiliations

[ ¹ ] Key Laboratory of Hydrodynamics (Ministry of Education) School of Naval Architecture, Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China

[ ² ] School of Biological Science and Medical Engineering Southeast University Nanjing China

[ ³ ] School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai China

[ ⁴ ] Shanghai Research Center of Biliary Tract Disease Shanghai China

Author notes

[*] [* ] Correspondence

Jie Wu, Department of Engineering Mechanics, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Minhang District, Shanghai 200240, China.

Email: jiewu82@ 123456sjtu.edu.cn

Author information

Jie Wu https://orcid.org/0000-0003-4245-875X

Article

Publisher ID: JCLA23779

DOI: 10.1002/jcla.23779

PMC ID: 8183938

PubMed ID: 33934391

SO-VID: 887ea447-c824-441e-a080-85f0ba7c32af

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 23 March 2021

Date received : 07 February 2021

Date accepted : 24 March 2021

Page count

Figures: 8, Tables: 2, Pages: 10, Words: 5205

Funding

Funded by: National Natural Science Foundation of China , open-funder-registry 10.13039/501100001809;

Award ID: 11572200

Award ID: 11502146

Award ID: 81773043

Custom metadata

source-schema-version-number 2.0

cover-date June 2021

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:07.06.2021

ScienceOpen disciplines: Clinical chemistry

Keywords: cerna network,long non‐coding rna,n6‐methyladenosine,prognostic signature,triple‐negative breast cancer

Data availability:

ScienceOpen disciplines: Clinical chemistry

Keywords: cerna network, long non‐coding rna, n6‐methyladenosine, prognostic signature, triple‐negative breast cancer

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

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Journal of Circulating Biomarkers

Most cited references 48

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

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