Research Paper|Volume 13, Issue 1|pp 1153—1175

Construction of an mRNA-miRNA-lncRNA network prognostic for triple-negative breast cancer

Yuan Huang¹, Xiaowei Wang², Yiran Zheng³, Wei Chen⁴, Yabing Zheng¹, Guangliang Li¹, Weiyang Lou⁵, Xiaojia Wang¹

¹Department of Breast Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Zhejiang Province, Hangzhou 310022, China
²Department of Colorectal Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang Province, Hangzhou 310016, China
³School of Pharmaceutical Sciences, Soochow University, Jiangsu Province, Suzhou 215123, China
⁴Silergy Corporation, Zhejiang Province, Hangzhou 310012, China
⁵Department of Breast Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Province, Hangzhou 310003, China

* Equal contribution

Received: May 4, 2020Accepted: November 13, 2020Published: January 3, 2021

https://doi.org/10.18632/aging.202254

Copyright: © 2020 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The aim of this study was to establish a novel competing endogenous RNA (ceRNA) network able to predict prognosis in patients with triple-negative breast cancer (TNBC). Differential gene expression analysis was performed using the GEO2R tool. Enrichr and STRING were used to conduct protein-protein interaction and pathway enrichment analyses, respectively. Upstream lncRNAs and miRNAs were identified using miRNet and mirTarBase, respectively. Prognostic values, expression, and correlational relationships of mRNAs, lncRNAs, and miRNAs were examined using GEPIA, starBase, and Kaplan-Meier plotter. It total, 860 upregulated and 622 downregulated differentially expressed mRNAs were identified in TNBC. Ten overexpressed and two underexpressed hub genes were screened. Next, 10 key miRNAs upstream of these key hub genes were predicted, of which six upregulated miRNAs were significantly associated with poor prognosis and four downregulated miRNAs were associated with good prognosis in TNBC. NEAT1 and MAL2 were selected as key lncRNAs. An mRNA-miRNA-lncRNA network in TNBC was constructed. Thus, we successfully established a novel mRNA-miRNA-lncRNA regulatory network, each component of which is prognostic for TNBC.