Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Xiaomei Li,Buu Truong,Taosheng Xu,Thuc D Le,Lin Liu,Jiuyong Li

doi:10.1186/s12859-021-04215-3

Abstract

BackgroundAccurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.ResultsIn this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.ConclusionsThe experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Highlights

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer
Performance of the breast cancer subtyping methods based on multiple levels of transcriptomic data We applied the cancer subtyping methods to different types of expression data, e.g. miRNA, long non-coding RNA (lncRNA) and messenger RNA (mRNA), and combinations of them to explore whether miRNA/ lncRNA data helps improve the performance of the methods
Using miRNA expression data improves the performance of the breast cancer subtyping methods Our experimental results show that the majority of methods perform better when using miRNA expression data alone or matched miRNA and mRNA expression data in comparison with using mRNA expression data alone

Summary

Introduction

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. Traditional breast cancer diagnosis and prognosis are based on clinicopathological variables, such as histologic tumour grade, lymph node status, and tumour size [2,3,4] These methods alone are not sufficient to guide the choice of effective treatment because breast cancer is a disease that is pathologically and clinically diverse, and biologically different [5]. With the advent of new sequencing technologies, researchers have extensively used genomic data to identify molecular subtypes of breast cancer [6,7,8,9,10,11,12,13,14,15] and gene signatures for prognosis [15,16,17,18,19,20,21,22,23] These methods have been successful in stratifying patients into several subtypes, each of them with distinct biological and clinical characteristics. The molecular-based subtypes and the gene signatures for prognosis are being translated into clinical practice in recent years [15,16,17, 23]

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