Hsa-miR-301a- and SOX10-dependent miRNA-TF-mRNA regulatory circuits in breast cancer

Breast cancer is the most common cancer among women and the molecular pathways that play main roles in breast cancer regulation are still not completely understood. MicroRNAs (miRNAs) and transcription factors (TFs) are important regulators of gene expression.
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Hsa-miR-301a- and SOX10-dependent miRNA-TF-mRNA regulatory circuits in breast cancerTurkish Journal of Biologyhttp://journals.tubitak.gov.tr/biology/Research ArticleTurk J Biol(2018) 42: 103-112© TÜBİTAKdoi:10.3906/biy-1708-17hsa-miR-301a- and SOX10-dependent miRNA-TF-mRNA regulatory circuits inbreast cancerYasemin ÖZTEMUR ISLAKOĞLU, Senem NOYAN, Bala GÜR DEDEOĞLU*Biotechnology Institute, Ankara University, Ankara, TurkeyReceived: 08.08.2017Accepted/Published Online: 13.01.2018Final Version: 27.04.2018Abstract: Breast cancer is the most common cancer among women and the molecular pathways that play main roles in breast cancerregulation are still not completely understood. MicroRNAs (miRNAs) and transcription factors (TFs) are important regulators of geneexpression. It is important to unravel the relation of TFs, miRNAs, and their targets within regulatory networks to clarify the processesthat cause breast cancer and the progression of it. In this study, mRNA and miRNA expression studies including breast tumors andnormal samples were extracted from the GEO microarray database. Two independent mRNA studies and a miRNA study were selectedand reanalyzed. Differentially expressed (DE) mRNAs and miRNAs between breast tumor and normal samples were listed by using BRBArray Tools. CircuitsDB2 analysis conducted with DE miRNAs and mRNAs resulted in 3 significant circuits that are SOX10- and hsamiR-301a-dependent. The following significant circuits were characterized and validated bioinformatically by using web-based tools:SOX10→hsa-miR-301a→HOXA3, SOX10→hsa-miR-301a→KIT, and SOX10→hsa-miR-301a→NFIB. It can be concluded that regulatorymotifs involving miRNAs and TFs may be useful for understanding breast cancer regulation and for predicting new biomarkers.Key words: Breast cancer, miRNA, mRNA, transcription factor, regulatory circuits1. IntroductionBreast cancer is a complex genetic disorder that is notcontrolled by a single factor but is rather controlled bymany factors (http://www.cancer.org/). Although manyfactors (genes, microRNAs (miRNAs), transcriptionfactors, etc.) that cause breast cancer and play a role in itsdevelopment have been identified with the advancement ofhigh-throughput technologies, the molecular mechanismsplaying a role in disease regulation have still not beenrevealed. For this reason, it is important and inevitableto identify the regulatory circuits and networks that willexplain the development and progression of breast cancer.Gene expression regulation is an importantmechanism for controlling biological processes in thecell. Transcriptional factors (TFs) are the regulators thatfunction at the transcriptional level, while miRNAs workat the posttranscriptional level. In view of the fact that thetranscription of mRNA and miRNAs is controlled by TFsand the expression of TFs is regulated by miRNAs, thesetwo important mechanisms cannot be separated from eachother. Therefore, characterization of these combinatorialregulatory mechanisms is important to reveal thebiological processes that take part in breast cancer in detailby constructing networks and circuits.*Correspondence: gurbala@yahoo.commiRNAs are small, noncoding RNA molecules thatregulate gene expression in posttranscriptional stages(Lagos-Quintana et al., 2001; Lee and Ambros, 2001).miRNAs play important roles in biological processessuch as development, cell division, cell differentiation,and programmed cell death. In this context, it is possiblethat altered miRNA expression may contribute to thedevelopment and progression of diseases such as cancer.The presence of abnormal miRNA expression profilingin many diseases, including breast cancer, has beendemonstrated and validated by intensifying miRNAstudies (Iorio et al., 2005; Lowery et al., 2009; Enerly etal., 2011; Romero-Cordoba et al., 2012). The finding thatmiRNA expressions are often dysregulated in cancers hasmade these molecules important candidates for cancermarkers (Calin et al., 2002; Lu et al., 2005). Therefore, thesearch for the relationship of miRNAs with target genes(this may be an mRNA or a TF) has great importance forthe diagnosis and treatment of breast cancer.TFs are essential regulatory elements in thetranscriptional pathway, which work by binding to targetgenes that have specific DNA sequences, usually in thepromoter region (Latchman, 1997). TFs regulate theirtargets at the transcription level by inhibiting or enhancing103ÖZTEMUR ISLAKOĞLU et al. / Turk J Bioltheir expression while miRNAs regulate target mRNAsat the posttranscriptional level in the form of inhibition.miRNAs also undergo the transcription process and theyregulate their target genes like TFs. When all these reasonsare taken into consideration:· Expression of a miRNA may be regulated by atranscription factor;· Expression of a TF may be regulated by a miRNA;· Similarly, the transcription factor and miRNA mayregulate the expression of target genes together.In recent years, many researchers have been workingon TFs, miRNAs, and their regulation mechanism onposttranscriptional and transcriptional levels. Withthe help of system biology approaches, transcriptionfactor-miRNA-target gene relationships were started tobe explored by an increasing number of studies. Thesestudies have mostly used and/or developed bioinformaticsand statistical methods and they have reviewed theexisting information. The tools that analyze miRNA-TFrelationships can be grouped into three subgroups. Thetools in the first group are intended to analyze statisticallythe gene clusters organized by cooperating miRNAs and/or miRNAs using matched miRNA and mRNA expressionprofiles (Yoon and De Micheli, 2005; Joung et al., 2007;Tran et al., 2008; Joung and Fei, 2009; Nam et al., 2009). Thetools in the second group again predict gene expressionrelationships using gene expression data ( ...