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• W3032907950 abstract "•Bioinformatics method ReMIx identify differential microRNA response rlements (MRE)•Tumor-specific MREs frequency observed in triple-negative breast cancer (TNBC)•MRE analysis identify MAPK signaling genes as therapeutic target for TNBC•MREs frequency can be used to identify pathologically relevant pathways Complex 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. Complex 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. Regulatory interactions between coding and non-coding RNAs in cells determine the post-transcriptional availability of protein-coding mRNA transcripts (Chiang et al., 2010Chiang H.R. Schoenfeld L.W. Ruby J.G. Auyeung V.C. Spies N. Baek D. Johnston W.K. Russ C. Luo S. Babiarz J.E. et al.Mammalian microRNAs: experimental evaluation of novel and previously annotated genes.Genes Dev. 2010; 24: 992-1009Crossref PubMed Scopus (631) Google Scholar, Eichhorn et al., 2014Eichhorn S.W. Guo H. McGeary S.E. Rodriguez-Mias R.A. Shin C. Baek D. Hsu S.H. Ghoshal K. Villen J. Bartel D.P. mRNA destabilization is the dominant effect of mammalian microRNAs by the time substantial repression ensues.Mol. Cell. 2014; 56: 104-115Abstract Full Text Full Text PDF PubMed Scopus (333) Google Scholar, Garcia et al., 2011Garcia D.M. Baek D. Shin C. Bell G.W. Grimson A. Bartel D.P. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs.Nat. Struct. Mol. Biol. 2011; 18: 1139-1146Crossref PubMed Scopus (709) Google Scholar, Guo et al., 2010Guo H. Ingolia N.T. Weissman J.S. Bartel D.P. Mammalian microRNAs predominantly act to decrease target mRNA levels.Nature. 2010; 466: 835-840Crossref PubMed Scopus (3108) Google Scholar, Guo et al., 2014Guo L. Zhao Y. Yang S. Zhang H. Chen F. Integrative analysis of miRNA-mRNA and miRNA-miRNA interactions.Biomed. Res. Int. 2014; 2014: 907420PubMed Google Scholar, Lee and Jiang, 2017Lee S. Jiang X. Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients.PLoS One. 2017; 12: e0182666PubMed Google Scholar, Rissland et al., 2017Rissland O.S. Subtelny A.O. Wang M. Lugowski A. Nicholson B. Laver J.D. Sidhu S.S. Smibert C.A. Lipshitz H.D. Bartel D.P. The influence of microRNAs and poly(A) tail length on endogenous mRNA-protein complexes.Genome Biol. 2017; 18: 211Crossref PubMed Scopus (33) Google Scholar, Shin et al., 2010Shin C. Nam J.W. Farh K.K. Chiang H.R. Shkumatava A. Bartel D.P. Expanding the microRNA targeting code: functional sites with centered pairing.Mol. Cell. 2010; 38: 789-802Abstract Full Text Full Text PDF PubMed Scopus (474) Google Scholar, Volinia and Croce, 2013Volinia S. Croce C.M. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer.Proc. Natl. Acad. Sci. U S A. 2013; 110: 7413-7417Crossref PubMed Scopus (127) Google Scholar, Wu and Bartel, 2017Wu X. Bartel D.P. Widespread influence of 3'-end structures on mammalian mRNA processing and stability.Cell. 2017; 169: 905-917.e911Abstract Full Text Full Text PDF PubMed Scopus (76) Google Scholar). MicroRNAs use seed sequences (6–8 bases long) to bind to microRNA response elements (MREs) predominantly located on the 3′UTRs of mRNAs. mRNAs can have one or more distinct MRE sites, thus being targets to multiple microRNAs. Similarly, microRNAs also bind to MRE sites of several different target genes (Krek et al., 2005Krek A. Grun D. Poy M.N. Wolf R. Rosenberg L. Epstein E.J. MacMenamin P. da Piedade I. Gunsalus K.C. Stoffel M. et al.Combinatorial microRNA target predictions.Nat. Genet. 2005; 37: 495-500Crossref PubMed Scopus (3893) Google Scholar, Lim et al., 2005Lim L.P. Lau N.C. Garrett-Engele P. Grimson A. Schelter J.M. Castle J. Bartel D.P. Linsley P.S. Johnson J.M. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.Nature. 2005; 433: 769-773Crossref PubMed Scopus (3995) Google Scholar). Thus, 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. Thus, 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 microRNA-target interactions in different cancer types (Jacobsen et al., 2013Jacobsen A. Silber J. Harinath G. Huse J.T. Schultz N. Sander C. Analysis of microRNA-target interactions across diverse cancer types.Nat. Struct. Mol. Biol. 2013; 20: 1325-1332Crossref PubMed Scopus (153) Google Scholar). Alternative polyadenylation of 3′UTRs in bladder cancer can lead to shortened 3′UTR affecting mRNA stability and attenuated protein translation (Han et al., 2018Han S. Kim D. Shivakumar M. Lee Y.J. Garg T. Miller J.E. Kim J.H. Kim D. Lee Y. The effects of alternative splicing on miRNA binding sites in bladder cancer.PLoS One. 2018; 13: e0190708PubMed Google Scholar). Studies have also shown that the presence of single nucleotide polymorphisms (SNPs) in the 3′UTR of transcripts can affect microRNA binding and are associated with multiple cancer subtypes (Pelletier and Weidhaas, 2010Pelletier C. Weidhaas J.B. MicroRNA binding site polymorphisms as biomarkers of cancer risk.Expert Rev. Mol. Diagn. 2010; 10: 817-829Crossref PubMed Scopus (40) Google Scholar). Here we extrapolate TCGA RNA sequencing (RNA-Seq) data to analyze MRE sites to obtain insights into unique interactions between mRNAs and microRNAs at the 3′UTRs of the tumor and normal-adjacent datasets. We developed a new bioinformatics approach called ReMIx (pronounced “remix”)—mRNA-MicroRNA Integration—which leverages RNA-Seq data to quantify MRE sites at the 3′UTR sequence across the transcriptome. ReMIx profiles MRE sites in tumor and matched normal samples separately, which enables the identification of differential frequency of MREs that are statistically significant in tumor samples. Because MRE is the interacting link between mRNAs and microRNAs, ReMIx brings together mRNAs with tumor-specific MREs and microRNAs that have the potential to bind to these MRE sites. ReMIx also reports potential mRNA-microRNA candidates that have unique tumor-specific interactions and potential disease-driving functions. This method can be applied to study any cancer type or complex diseases along with their normal tissue sets. To demonstrate the utility of ReMIx, we applied it to the largest RNA-Seq dataset of breast cancer cases and normal-adjacent tissues from TCGA (Cancer Genome Atlas Network, 2012Cancer Genome Atlas NetworkComprehensive molecular portraits of human breast tumours.Nature. 2012; 490: 61-70Crossref PubMed Scopus (8301) Google Scholar, Ciriello et al., 2015Ciriello G. Gatza M.L. Beck A.H. Wilkerson M.D. Rhie S.K. Pastore A. Zhang H. McLellan M. Yau C. Kandoth C. et al.Comprehensive molecular portraits of invasive lobular breast cancer.Cell. 2015; 163: 506-519Abstract Full Text Full Text PDF PubMed Scopus (1125) Google Scholar). Using this method, we specifically identified MREs in estrogen receptor positive (ER+), ErbB2 overexpressed–HER2 positive (HER2+), triple-negative tumors, and normal-adjacent tissues. Triple-negative breast cancers (TNBC) are highly heterogeneous and one of the most severe forms of breast cancer subtypes with no targeted treatments currently available. In this study, we applied ReMIx and identified mRNA-microRNA candidates unique to the TNBC and not present in ER + or HER2+ subtypes. Analysis of TNBC data identified MAPK signaling pathway targets as a potential disease driver and target. We developed an innovative bioinformatics approach called ReMIx to quantify the expression of MRE sites at the 3′UTRs of mRNAs using RNA-Seq data. ReMIx uses reads aligned to 3′UTRs of genes in a given transcriptome and scans them for evidence of MRE sequences (see Transparent Methods). All known MREs for genes in the reference genome, as reported by TargetScan—human version 7.0 (Agarwal et al., 2015Agarwal V. Bell G.W. Nam J.W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs.Elife. 2015; 4: e05005Crossref PubMed Scopus (4379) Google Scholar), are quantified for their level of expression at the 3′UTR of all genes. After quantification, ReMIx normalizes the raw counts of MREs to account for sample library size, 3′UTR length, and 3′UTR GC content per gene. Finally, for every gene and for every conserved microRNA that targets the gene, the normalized MRE counts are reported in a tab-delimited format for each gene-microRNA pair in the transcriptome analyzed. The ReMIx workflow is fully automated and designed to run in a multithreaded cluster environment to analyze paired-end transcriptome samples. A flowchart of the ReMIx approach is shown in Figure 1 (see Transparent Methods). The 3′UTR sequences of individual genes (n = 12,455, TargetScan v7.0 (Agarwal et al., 2015Agarwal V. Bell G.W. Nam J.W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs.Elife. 2015; 4: e05005Crossref PubMed Scopus (4379) Google Scholar)) were obtained using the reference human genome hg19 build. Reads aligned to these 3′UTR sequences were obtained using the TCGA Breast Cancer transcriptome dataset for 13 pairs (Tumor and Normal-Adjacent) from the TNBC subtype, 56 pairs of ER+, and 20 pairs of HER2+ subtypes and were provided as input to the ReMIx workflow (see Transparent Methods). The pre-computed MRE sequences (n = 329, TargetScan 7.0) were also provided as input to ReMIx to count reads mapped to individual MREs located on each gene. The raw MRE counts were then normalized by factoring library size, 3′UTR lengths, and 3′UTR GC content of individual genes. MRE quantification process identified normalized counts of 111,521 MRE sites in tumor and normal adjacent sample sets for each subtype (Data S1, S2, and S3 for TN, ER+, and HER2+, respectively). Next, ReMIx results were used to identify MRE sites that had unique and significant levels of expression (high or low) in TNBC tumors in comparison to ER + tumors, HER2+ tumors as well as TN, ER+, and HER2+ normal-adjacent cases. The Dunnett-Tukey-Kramer (DTK) pairwise multiple comparison statistical test was applied to the tumor and normal-adjacent cases across all subtypes (six groups in total) to highlight MREs that were unique only to TNBC (p value < 0.05) when compared with other two subtypes and all normal-adjacent cases. This resulted in identifying 614 MREs unique to TNBC (Data S4). In addition, the edgeR bioinformatics package (Robinson et al., 2010Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics. 2010; 26: 139-140Crossref PubMed Scopus (20834) Google Scholar) was applied to identify differentially expressed MREs by comparing 13 TN tumor and the respective normal-adjacent cases (FDR <5% and log2FC |2|) and reported 3,053 significant and differentially expressed MREs (Data S5). By adopting the approach of taking the intersection of MREs reported to be statistically significant and differentially expressed by the two complementary approaches, i.e., DTK (n = 614 MREs) and edgeR (n = 3,053 MREs), we identified a common set of 221 TN tumor-specific MRE sites (Figure S1). The 221 TNBC MREs are provided in Data S6. The distinct expression profile of these MRE sites in TNBC with respect to other subtypes and normal-adjacent cases are shown in the heatmap (Figure 2). The unique feature of MRE is that it is the interactive site between mRNA and microRNA. Hence, for MRE sites of interest, we can decode and obtain information about the mRNA and its interacting microRNA by identifying the relevant MREs and decoupling them into their respective mRNA and microRNA pairs. Thus, for the TNBC tumor-specific MRE sites, we deciphered such information for the 221 MREs and obtained a total of 88 mRNAs and 125 microRNAs. Tables listing 88 mRNAs and 125 microRNAs along with their expression levels in TNBC are provided in Data S7 and S8, respectively. With these ReMIx analyses, we deduce that over half of the mRNAs (48 out of 88) were used repeatedly and these mRNAs had multiple MREs that were used as interactive sites by different microRNAs. Unsupervised hierarchical clustering of 221 MREs based on expression profiles of these MREs showed that the TN cases clustered within their tumor and normal-adjacent groups. The differential expression pattern for 221 MREs are shown in Figure 3. Notably, unsupervised clustering of the corresponding 88 mRNAs and 125 microRNAs also showed a separation of TNBC into tumor and normal-adjacent groups (Figure 3). Next, we evaluated the mRNAs and microRNAs identified by ReMIx using a standalone approach to analyze their predominance in terms of differential expression within the respective RNA and microRNA expression datasets of 13 TNBC tumor and normal-adjacent pairs and identified canonical pathways that were associated with 88 mRNAs and 125 microRNAs. The differential expression analysis using RNA-Seq data for 13 TNBC tumor and normal-adjacent pairs showed that a total of 2,250 genes were differentially expressed (edgeR package (Robinson et al., 2010Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics. 2010; 26: 139-140Crossref PubMed Scopus (20834) Google Scholar); statistical significance threshold at FDR <5% and log2FC |2|). Notably, out of the 88 mRNAs identified by the ReMIx analysis, we found that 68 (77%) were also differentially expressed at the gene level between TNBC cases. This indicated a high likelihood of microRNA-mediated gene expression regulation resulting in their differential expression in TNBC tumors compared with their normal-adjacent counterparts. Notably, these 68 mRNAs found in the 13 paired TNBC cases were also consistently differential expressed in a larger cohort of 120 TCGA-TNBC and 13 normal-adjacent samples (Figure S2). This suggests that mRNA expression observed in a smaller sample size is potentially reflective of mRNA expression in a larger cohort. Out of 68 mRNAs, 41 had multiple MREs targeted by different microRNAs. A table listing these 68 mRNAs that are both differentially expressed and have interacting MRE sites are given in Data S9. Next, using the microRNA expression data for 13 TNBC pairs, we found that out of a total of 2,245 microRNAs that were quantified for expression in the tumor and normal-adjacent cases, 778 microRNAs were differentially expressed in tumors (limma package (Ritchie et al., 2015Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43: e47Crossref PubMed Scopus (15346) Google Scholar); adjusted p value < 0.05). Examining the number of microRNAs identified by ReMIx that were also differentially expressed between TNBC tumor and normal-adjacent, we found that 64 out of 125 microRNAs (51%) were statistically different in expression (FDR <5%). A table and heatmap listing 64 microRNAs that are both differentially expressed and participate in MRE-mediated gene expression regulation can be found in the Data S10 and Figure S3. Finally, using RNA-Seq and microRNA differential expression results, the magnitude and direction of change for 221 MREs and their associated genes and microRNAs in 13 TNBC tumor and normal-adjacent pairs were combined (Data S11). We observed that the majority of MREs follow the direction as their parent genes, with very few exceptions, likely due to the nature of TNBC sequencing libraries (Illumina TruSeq). Further analysis of 125 microRNAs using the TAM 2.0 tool for microRNA set enrichment analysis revealed that these microRNAs were associated with cancer pathways as shown in Table S1. Specifically, 14 out of 125 microRNAs are also reported in other TNBC studies and are upregulated with an FDR <2.87 × 10−5. Similarly, 55/125 microRNAs are reported in breast carcinoma studies (FDR <8.18 × 10−18) and 34/125 in breast neoplasms (FDR <6.12 × 10−13). Information of these microRNAs are provided in Table S1. Eighty-eight genes obtained from ReMIx were analyzed to identify their associated signaling pathways. Using gene set enrichment analysis (GSEA) (Subramanian et al., 2005Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (26559) Google Scholar) on KEGG and REACTOME databases, the mitogen-activated protein kinase (MAPK) signaling cascade was identified among the top significant pathways. In addition, application of the signaling pathway impact analysis (SPIA) package also confirmed that the MAPK signaling pathway was activated in TN tumors. The GSEA and SPIA pathway results are provided in Data S12 and S13, respectively. Further examination of genes in the MAPK pathway was conducted by juxtaposing the expression of these genes, obtained from RNA-Seq data of TNBC with the KEGG-based network of the MAPK pathway. Our analysis revealed that oncogenes KRAS, NRAS, AKT, and NFKB were notably activated and tumor suppressor PTEN was repressed. Figure 4 illustrates the KEGG pathview for the MAPK signaling cascade. MAPK signaling pathway is an extensive cascade with connections to several biological pathways downstream such as proliferation, cell cycle, glycolysis, apoptosis, and protein synthesis. Based on MRE results from the ReMIx, we investigated relevant genes that were associated with MAPK signaling in TN tumors. We found 12 out of 294 genes (~4%) in MAPK pathway (https://www.genome.jp/dbget-bin/get_linkdb?-t+genes+path:hsa04010) that have MRE sites with the potential of differential binding of microRNAs. These 12 mRNAs with tumor-specific MRE sites and microRNAs with the potential to bind to these sites are provided in Table 1. Next, we expanded the MAPK gene network in TN tumors by including interacting microRNAs that are essential members of the pathway. Figure 5 shows the MAPK endogenous RNA network that represents the genes identified by ReMIx, interacting microRNAs, and other mRNAs that are likely to interact with each other and regulate expressions of key genes, such as PI3K, AKT, RAS, NFKB, and PTEN. Furthermore, ERK1/2, critical genes in the MAPK/ERK signaling cascade, were also directly associated with 7 of the 12 genes (Figure 5). Taken together, we present an expanded network of MAPK signaling cascade and provide a list of potential mRNA-microRNA candidates that interact with each other and could potentially be therapeutic targets for TN tumors.Table 1Gene-microRNA Pairs with Distinct TN-Specific MRE Sites that Are Part of the MAPK PathwaymRNAsmicroRNAsMAPK Signaling PathwayCACNA2D1hsa-miR-429PPP3CBhsa-miR-330-5p; hsa-miR-486-5pRASGRF1hsa-miR-384IGF1hsa-miR-142-5p; hsa-miR-488-3pHGFhsa-miR-495-3pEFNA5hsa-miR-101-3p.2; hsa-miR-130b-3p; hsa-miR-489-3p; hsa-miR-96-5pPDGFRAhsa-miR-132-3p; hsa-miR-140-5p; hsa-miR-491-5pFOShsa-miR-802TGFBR2hsa-miR-361-5p; hsa-miR-665FLNChsa-miR-377-3pARRB1hsa-miR-140-3p.1; hsa-miR-296-5pPPM1Ahsa-miR-488-3pTable lists genes that are a subset of the 88 genes obtained from ReMIx and that are members of the MAPK signaling pathway. The microRNAs that bind to the MRE sites that were found to have distinct counts in TN tumors are also provided. Related to Figure 5. Open table in a new tab Table lists genes that are a subset of the 88 genes obtained from ReMIx and that are members of the MAPK signaling pathway. The microRNAs that bind to the MRE sites that were found to have distinct counts in TN tumors are also provided. Related to Figure 5. The regulatory interactions between non-coding and protein-coding RNAs have been well recognized, where the mRNA-microRNA interactions are widely studied. Although there are several microRNA target prediction tools such as TargetScan (Agarwal et al., 2015Agarwal V. Bell G.W. Nam J.W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs.Elife. 2015; 4: e05005Crossref PubMed Scopus (4379) Google Scholar), miRBase (Kozomara et al., 2019Kozomara A. Birgaoanu M. Griffiths-Jones S. miRBase: from microRNA sequences to function.Nucleic Acids Res. 2019; 47: D155-D162Crossref PubMed Scopus (1787) Google Scholar), DIANA (Vlachos et al., 2012Vlachos I.S. Kostoulas N. Vergoulis T. Georgakilas G. Reczko M. Maragkakis M. Paraskevopoulou M.D. Prionidis K. Dalamagas T. Hatzigeorgiou A.G. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways.Nucleic Acids Res. 2012; 40: W498-W504Crossref PubMed Scopus (442) Google Scholar), PicTar (Krek et al., 2005Krek A. Grun D. Poy M.N. Wolf R. Rosenberg L. Epstein E.J. MacMenamin P. da Piedade I. Gunsalus K.C. Stoffel M. et al.Combinatorial microRNA target predictions.Nat. Genet. 2005; 37: 495-500Crossref PubMed Scopus (3893) Google Scholar), miRwayDB (Das et al., 2018Das S.S. Saha P. Chakravorty N. miRwayDB: a database for experimentally validated microRNA-pathway associations in pathophysiological conditions.Database (Oxford). 2018; 2018: bay023Crossref Scopus (28) Google Scholar), miRanda (Betel et al., 2008Betel D. Wilson M. Gabow A. Marks D.S. Sander C. The microRNA.org resource: targets and expression.Nucleic Acids Res. 2008; 36: D149-D153Crossref PubMed Scopus (2037) Google Scholar), PITA (Kertesz et al., 2007Kertesz M. Iovino N. Unnerstall U. Gaul U. Segal E. The role of site accessibility in microRNA target recognition.Nat. Genet. 2007; 39: 1278-1284Crossref PubMed Scopus (1895) Google Scholar), RNA22 (Loher and Rigoutsos, 2012Loher P. Rigoutsos I. Interactive exploration of RNA22 microRNA target predictions.Bioinformatics. 2012; 28: 3322-3323Crossref PubMed Scopus (139) Google Scholar), and miRTar (Hsu et al., 2011Hsu J.B. Chiu C.M. Hsu S.D. Huang W.Y. Chien C.H. Lee T.Y. Huang H.D. miRTar: an integrated system for identifying miRNA-target interactions in human.BMC Bioinformatics. 2011; 12: 300Crossref PubMed Scopus (107) Google Scholar), not many computational tools have been developed that enable the integration of mRNA and microRNA expression datasets. MAGIA is a web-based tool for microRNA and gene integrated analysis that brings together target predictions and gene expression profiles using different functional measures for both matched and unmatched samples (Sales et al., 2010Sales G. Coppe A. Bisognin A. Biasiolo M. Bortoluzzi S. Romualdi C. MAGIA, a web-based tool for miRNA and genes integrated analysis.Nucleic Acids Res. 2010; 38: W352-W359Crossref PubMed Scopus (130) Google Scholar). The tool miRmapper uses mRNA-microRNA predictions and a list of differentially expressed mRNAs to identify top microRNAs and recognizes similarities between microRNAs based on commonly regulated mRNAs (da Silveira et al., 2018da Silveira W.A. Renaud L. Simpson J. Glen Jr., W.B. Hazard E.S. Chung D. Hardiman G. miRmapper: a tool for interpretation of miRNA(-)mRNA interaction networks.Genes (Basel). 2018; 9: 458Crossref Scopus (17) Google Scholar). HisCoM-mimi is a hierarchically structured component analysis method that models biological relationships as structured components to efficiently yield integrated mRNA-microRNA markers (Kim et al., 2018Kim Y. Lee S. Choi S. Jang J.Y. Park T. Hierarchical structural component modeling of microRNA-mRNA integration analysis.BMC Bioinformatics. 2018; 19: 75Crossref PubMed Scopus (13) Google Scholar). These tools use prior knowledge of microRNA target predictions and are developed using unique methodologies to derive mRNA-microRNA interactions. Furthermore, tools such as miRmapper have the ability to highlight key microRNAs based on the number of connections it possesses in a given network. However, the underlying methodologies of all these tools are to use the expression of either mRNAs alone or both mRNAs and microRNAs to model their correlation and derive mRNA-microRNA relationships. With the advent of RNA-Seq technology, profiling of the transcriptome is now possible at the base-prevision level. It is a known fact that microRNAs predominantly bind to the 3′UTRs of mRNAs to induce their regulatory effects and thereby impact mRNA expression and protein translation. Studies have shown that shortening of 3′UTR is a frequent phenomenon in cancer to evade oncogenes from microRNA suppression (Xue et al., 2018Xue Z. Warren R.L. Gibb E.A. MacMillan D. Wong J. Chiu R. Hammond S.A. Yang C. Nip K.M. Ennis C.A. et al.Recurrent tumor-specific regulation of alternative polyadenylation of cancer-related genes.BMC Genomics. 2018; 19: 536Crossref PubMed Scopus (16) Google Scholar), repress tumor suppressor genes (Park et al., 2018Park H.J. Ji P. Kim S. Xia Z. Rodriguez B. Li L. Su J. Chen K. Masamha C.P. Baillat D. et al.3' UTR shortening represses tumor-suppressor genes in trans by disrupting ceRNA crosstalk.Nat. Genet. 2018; 50: 783-789Crossref PubMed Scopus (89) Google Scholar), and enhance metastatic burden (Andres et al., 2019Andres S.F. Williams K.N. Plesset J.B. Headd J.J. Mizuno R. Chatterji P. Lento A.A. Klein-Szanto A.J. Mick R. Hamilton K.E. et al.IMP1 3' UTR shortening enhances metastatic burden in colorectal cancer.Carcinogenesis. 2019; 40: 569-579Crossref PubMed Scopus (13) Google Scholar). Therefore, it is important not only to know which mRNAs are differentially expressed between a tumor and normal pair but also to determine which integration sites or microRNA response elements (MREs) are available along the 3′UTRs of the tumor mRNAs. Identification of MREs that are either present/absent/highly expressed/low expressed in the tumor can provide mechanistic insights of tumor progression. Although mRNA-microRNA integration tools exist, and may be applied to the tumor and normal datasets, no tools, to our knowledge, have the ability to precisely report mRNA-microRNA interactions that are solely based on the availability of MREs at the 3′UTRs. MREs are short 6–8 base segments and without appropriate bioinformatics methods, screening RNA-Seq data for MRE sites can yield highly non-specific and erroneous results. This could be a possible reason why this simple, but highly relevant, concept has not been explored to date. In this study, we developed an innovative bioinformatics method “ReMIx” that uses RNA-Seq data to identify and quantify microRNA-binding sites (known as microRNA response elements [MREs]) at 3′UTRs. A hypothetical example of this approach is illustrated in Figure 6. We applied ReMIx to TCGA-paired tumors and normal-adjacent breast cancer cases for TN, ER+, and HER2+ subtypes. Using two complementary statistical approaches, we identified 221 MRE sites that have a distinct expression in TN tumor-normal adjacent pairs. Upon decoupling, we found that the 221 MRE sites corresponded to 88 mRNAs and 125 microRNAs. By reviewing fold-changes of these MREs, mRNAs, and microRNAs, we observed that most of the MREs followed the same direction as their parent gene transcript. We postulate that this was likely driven by the sequencing library preparation kit (Illumina TruSeq). However, we also found MREs with the opposite trend, suggesting an alternative 3′UTR mechanism. Furthermore, we found mRNAs and MREs with positive expression in TNBC tumors but repressed microRNAs, likely denoting the effect of competing endogenous RNAs" @default.
• W3032907950 created "2020-06-12" @default.
• W3032907950 creator A5010174341 @default.
• W3032907950 creator A5044404099 @default.
• W3032907950 creator A5070464408 @default.
• W3032907950 creator A5076441673 @default.
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• W3032907950 date "2020-06-01" @default.
• W3032907950 modified "2023-09-25" @default.
• W3032907950 title "Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer" @default.
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