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  • Acarbose br The interaction network between the


    The interaction network between the lncRNAs which have been obtained from the previous steps, VDR and ESR was depicted using Cytoscape tool. Moreover, LncMAP tool (Li et al., 2018) was used to show transcriptional regulatory effects of two lncRNAs (MALAT1 and TBX5-AS1). The interaction network depicted by the latter tool contained the top 30 lncRNA-transcription factor (TF)-gene triplets.
    The interaction network depicted by Cytoscape tool shows that several lncRNAs regulate expression of both VDR and ESR1 (Fig. 3). Moreover, the transcriptional regulatory effects of MALAT1 and TBX5-AS1 were depicted using LncMAP (Fig. 4, Fig. 5 respectively). These two lncRNAs were chosen for network analysis based on the adequacy of available data and their role in the pathogenesis of cancer.
    Discussion In silico approaches have been increasingly appreciated as effective tools for identification of the expression pattern of genes and the effect of certain mutations/ single nucleotide polymorphisms (SNPs) on miRNA-mRNA interactions in the context of cancer (Gopalakrishnan et al., Acarbose 2014; Bhaumik et al., 2014) or single gene disorders (Kamaraj et al., 2014). Such approaches have been successfully implemented to offer hints for wet-lab researches to design more efficient studies. However, the role of lncRNAs in this regard has been less studied. In the present study, we used a systematic approach for identification of lncRNAs which potentially influence both VDR and ER signaling. In vitro experiments have provided evidences that the 1,25(OH)2D3-bound VDR suppresses ESR1 transcription through direct attachment with a negative vitamin D response Acarbose in the ESR1 gene promoter (Swami et al., 2000). However, we hypothesized that other regulatory mechanism might contribute in the interaction feedback between VDR and ER signaling pathways among them are lncRNAs. We provided two lists of lncRNAs whose expression was positively correlated with ESR1 and negatively correlated with VDR and vice versa. We chose some lncRNAs from each list to explain their functions in more detail. As data regarding their role were more conclusive. Among the first category of lncRNAs is GATA3-AS1. Consistent with our data, a previous assessment of RNA-seq data has revealed highly specific elevation of GATA3-AS1 expression in ER positive breast cancers compared to ER negative cancers and normal breast specimens (Zhang et al., 2016a). MALAT1 was another lncRNA with positive correlation with ESR1 and negative correlation with VDR. Assessment of TCGA data and expression analysis in breast cancer cell lines have shown the highest expression of this lncRNA in luminal subtype of breast cancer and the lowest levels in ER negative patient samples (Jadaliha et al., 2016). Noticeably, assessment of expression profile of lncRNAs in VDR-deleted keratinocytes has shown up-regulation of MALAT1 (Jiang & Bikle, 2014b). The former data suggest the presence of regulatory circuit between VDR and MALAT1 in other contexts as well. Moreover, we depicted the lncRNA-TF-gene triplet for MALAT1 and showed the presence of critical TFs and genes in this network. The second category of lncRNAs includes some lncRNAs whose role in breast cancer pathogenesis has been assessed. For instance, RNA-seq data have revealed over-expression of the small nucleolar RNA host gene 12 (SNHG12) in triple negative breast cancer (TNBC) and correlations between its high expression and tumor size as well as lymph node metastasis (Wang et al., 2017a). In addition, consistent with our results, LINC00511 has been shown to be a TNBC specific lncRNA (Xu et al., 2017a). TBX5-AS1 was one represented lncRNAs from the second category. This lncRNA has interactions with critical cancer-related TFs and genes as depicted in the interaction network. Assessment of lncRNA-miRNA interactions showed possible interactions of lncRNAs with miR-31 and miR-155. These two miRNAs are among oncogenic miRNAs whose elevated plasma levels have been demonstrated in breast cancer patients. Moreover, the expression levels of miR-155, but not miR-31, were inversely correlated with ER and progesterone receptor (PR) expression (Lu et al., 2012). More importantly, GATA3 and MYC as tow transcriptional regulators with role in tumorigenesis have been shown to regulate expression of several lncRNAs in both categories. Such observations along with the observed somatic copy number variations in cancers provide further supports for functional importance of these lncRNAs in breast cancer pathogenesis. In addition, the interaction of the lncRNAs LINC00511, LOC100507487, HDAC2-AS2 and VIM-AS1 with ZNF217 has been recognized. ZNF217 is a TF with oncogenic role in breast cancer whose expression levels are suggested as prognostic factors in this kind of malignancy (Vendrell et al., 2012). Several lncRNAs of both categories have also interactions with PTEN and TP53 tumor suppressors as well as PIK3CA and ERBB2 oncogenes. PIK3CA and TP53 genes have been shown to be frequently mutated in human breast cancers. Most notably, the frequencies of mutations were different among ER positive and ER negative breast cancer samples (Bai et al., 2014).