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      Loss of REST in breast cancer promotes tumor progression through estrogen sensitization, MMP24 and CEMIP overexpression

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          Abstract

          Background

          Breast cancer is the most common malignancy in women, and is both pathologically and genetically heterogeneous, making early detection and treatment difficult. A subset of breast cancers express normal levels of REST (repressor element 1 silencing transcription factor) mRNA but lack functional REST protein. Loss of REST function is seen in ~ 20% of breast cancers and is associated with a more aggressive phenotype and poor prognosis. Despite the frequent loss of REST, little is known about the role of REST in the molecular pathogenesis of breast cancer.

          Methods

          TCGA data was analyzed for the expression of REST target genes in breast cancer patient samples. We then utilized gene knockdown in MCF-7 cells in the presence or absence of steroid hormones estrogen and/ progesterone followed by RNA sequencing, as well as chromatin immunoprecipitation and PCR in an attempt to understand the tumor suppressor role of REST in breast cancer.

          Results

          We show that REST directly regulates CEMIP (cell migration-inducing and hyaluronan-binding protein, KIAA1199) and MMP24 (matrix metallopeptidase 24), genes known to have roles in invasion and metastasis. REST knockdown in breast cancer cells leads to significant upregulation of CEMIP and MMP24. In addition, we found REST binds to RE-1 sites (repressor element-1) within the genes and influences their transcription. Furthermore, we found that the estrogen receptor (ESR1) signaling pathway is activated in the absence of REST, regardless of hormone treatment.

          Conclusions

          We demonstrate a critical role for the loss of REST in aggressive breast cancer pathogenesis and provide evidence for REST as an important diagnostic marker for personalized treatment plans.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12885-022-09280-2.

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          Most cited references65

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                vchennathukuzhi@kumc.edu
                Journal
                BMC Cancer
                BMC Cancer
                BMC Cancer
                BioMed Central (London )
                1471-2407
                17 February 2022
                17 February 2022
                2022
                : 22
                : 180
                Affiliations
                [1 ]GRID grid.412016.0, ISNI 0000 0001 2177 6375, Department of Molecular and Integrative Physiology, , University of Kansas Medical Center, ; Kansas City, KS USA
                [2 ]GRID grid.468219.0, ISNI 0000 0004 0408 2680, The University of Kansas Cancer Center, ; Kansas City, KS USA
                [3 ]GRID grid.266515.3, ISNI 0000 0001 2106 0692, University of Kansas, ; Lawrence, KS USA
                [4 ]GRID grid.412016.0, ISNI 0000 0001 2177 6375, Department of Biostatistics, , University of Kansas Medical Center, ; Kansas City, KS USA
                [5 ]GRID grid.412016.0, ISNI 0000 0001 2177 6375, Department of Pathology and Laboratory Medicine, , University of Kansas Medical Center, ; Kansas City, KS USA
                [6 ]GRID grid.412016.0, ISNI 0000 0001 2177 6375, Department of Cancer Biology, , University of Kansas Medical Center, ; Kansas City, KS USA
                [7 ]GRID grid.412016.0, ISNI 0000 0001 2177 6375, Department of Anatomy and Cell Biology, , University of Kansas Medical Center, ; Kansas City, KS USA
                Article
                9280
                10.1186/s12885-022-09280-2
                8851790
                35177031
                295d53d1-ef0c-4645-bedd-066bc0f23040
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 April 2021
                : 8 February 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Oncology & Radiotherapy
                rest,cemip,mmp24,breast cancer,estrogen signaling,tumor progression
                Oncology & Radiotherapy
                rest, cemip, mmp24, breast cancer, estrogen signaling, tumor progression

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