Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer.

Kevin J. Thompson,Peter T. Vedell,Asha A. Nair,Krishna R. Kalari,Xiaojia Tang,Subbaya Subramanian

doi:10.1016/j.isci.2020.101249

Kevin J. Thompson, Peter T. Vedell + Show 4 more

Open Access

https://doi.org/10.1016/j.isci.2020.101249

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Abstract

SummaryComplex interactions between mRNAs and microRNAs influence cellular functions. The mRNA-microRNA interactions also determine the post-transcriptional availability of mRNAs and unbound microRNAs. MicroRNAs binds to one or more microRNA response elements (MREs) located on the 3′UTR of mRNAs. In this study, we leveraged MREs and their frequencies in cancer and matched normal tissues to obtain insights into disease-specific interactions between mRNAs and microRNAs. We developed a bioinformatics method “ReMIx” that utilizes RNA sequencing (RNA-Seq) data to quantify MRE frequencies across the transcriptome. We applied ReMIx to triple-negative (TN) breast cancer tumor-normal adjacent pairs and identified MREs specific to TN tumors. ReMIx identified candidate mRNAs and microRNAs in the MAPK signaling cascade. Further analysis of MAPK gene regulatory networks revealed microRNA partners that influence and modulate MAPK signaling. In conclusion, we demonstrate a novel method of using MREs in the identification of functionally relevant mRNA-microRNA interactions in TN breast cancer.

Highlights

We developed a bioinformatics method ‘‘ReMIx’’ that utilizes RNA sequencing (RNA-Seq) data to quantify microRNA response elements (MREs) frequencies across the transcriptome
We demonstrate a novel method of using MREs in the identification of functionally relevant mRNA-microRNA interactions in TN breast cancer
ReMIx: An Automated Bioinformatics Approach for MRE Quantification We developed an innovative bioinformatics approach called ReMIx to quantify the expression of MRE sites at the 30UTRs of mRNAs using RNA-Seq data

Summary

Introduction

Regulatory interactions between coding and non-coding RNAs in cells determine the post-transcriptional availability of protein-coding mRNA transcripts (Chiang et al, 2010; Eichhorn et al, 2014; Garcia et al, 2011; Guo et al, 2010, 2014; Lee and Jiang, 2017; Rissland et al, 2017; Shin et al, 2010; Volinia and Croce, 2013; Wu and Bartel, 2017). Alterations in target gene expression via microRNA binding can affect several cellular processes such as cell proliferation and apoptosis during cancer development, progression, and metastasis. Elucidating critical players among the mRNA-microRNA interacting networks can yield novel therapeutic targets and biomarkers in cancers, especially for cancer subtypes that are least responsive to current modalities of treatment. Expression profiles of microRNAs and mRNAs (Illumina TruSeq libraries enriched for poly(A) RNAs) across many cancer types in The Cancer Genome Atlas (TCGA) were used to infer active and functional microRNAtarget interactions in different cancer types (Jacobsen et al, 2013). Studies have shown that the presence of single nucleotide polymorphisms (SNPs) in the 30UTR of transcripts can affect microRNA binding and are associated with multiple cancer subtypes (Pelletier and Weidhaas, 2010). We extrapolate TCGA RNA sequencing (RNA-Seq) data to analyze MRE sites to obtain insights into unique interactions between mRNAs and microRNAs at the 30UTRs of the tumor and normal-adjacent datasets

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