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      ZMIZ1 enhances ERα-dependent expression of E2F2 in breast cancer

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          Abstract

          The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1–2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1–ER–E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER–ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance.

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          Most cited references48

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

                Author and article information

                Journal
                J Mol Endocrinol
                J Mol Endocrinol
                JME
                Journal of Molecular Endocrinology
                Bioscientifica Ltd (Bristol )
                0952-5041
                1479-6813
                25 April 2024
                02 April 2024
                01 July 2024
                : 73
                : 1
                : e230133
                Affiliations
                [1 ]Department of Biology , University of York, York, UK
                [2 ]CRUK Cambridge Institute , University of Cambridge, Cambridge, UK
                [3 ]Department of Cancer Immunology and Virology , Dana-Farber Cancer Institute, Smith Building, Boston, Massachusetts, USA
                [4 ]The Institute of Metabolism and Systems Research (IMSR) , University of Birmingham, College of Medical and Dental Sciences, Birmingham, UK
                [5 ]Chris O’Brien Lifehouse , Sydney, New South Wales, Australia
                [6 ]Department of Pharmacy and Biotechnology , University of Bologna, Bologna, Italy
                [7 ]York Biomedical Research Institute , University of York, York, UK
                [8 ]The Alan Turing Institute , Kings Cross, London, UK
                Author notes
                Correspondence should be addressed to F Markowetz or A N Holding: florian.markowetz@ 123456cruk.cam.ac.uk or Andrew.Holding@ 123456york.ac.uk
                Author information
                http://orcid.org/0000-0002-8459-7048
                Article
                JME-23-0133
                10.1530/JME-23-0133
                11103680
                38564418
                e15520f0-3fc3-4df3-bf63-b8c7a62c0136
                © the author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 07 November 2023
                : 02 April 2024
                Funding
                Funded by: Alan Turing Institute, doi http://dx.doi.org/10.13039/100012338;
                Funded by: Royal Society, doi http://dx.doi.org/10.13039/501100000288;
                Categories
                Research

                Endocrinology & Diabetes
                breast cancer,cancer,co-factors transcription nuclear receptors signalling patient outcome,e2f2,estrogen receptor,zmiz1

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