0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      YAP, CTGF and Cyr61 are overexpressed in tamoxifen-resistant breast cancer and induce transcriptional repression of ERα

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          About 70% of breast cancers overexpress estrogen receptor α (ERα, encoded by ESR1). Tamoxifen, a competitive inhibitor of estrogen that binds to ER, has been widely used as a treatment for ER-positive breast cancer. However, 20–30% of breast cancer is resistant to tamoxifen treatment. The mechanisms underlying tamoxifen resistance remain elusive. We found that Yes-associated protein (YAP; also known as YAP1), connective tissue growth factor (CTGF; also known as CCN2) and cysteine-rich angiogenic inducer 61 (Cyr61; also known as CCN1) are overexpressed, while ERα is downregulated in tamoxifen-resistant breast cancer. Inhibition of YAP, CTGF and Cyr61 restored ERα expression and increased sensitivity to tamoxifen. Overexpression of YAP, CTGF, and Cyr61 led to downregulation of ERα and conferred resistance to tamoxifen in ER-positive breast cancer cells. Mechanistically, CTGF and Cyr61 downregulated ERα expression at the transcriptional level by directly binding to the regulatory regions of the ERα-encoding gene, leading to increased tamoxifen resistance. Also, CTGF induced Glut3 (also known as SLC2A3) expression, leading to increased glycolysis, which enhanced cell proliferation and migration in tamoxifen-resistant cells. Together, these results demonstrate a novel role of YAP, CTGF and Cyr61 in tamoxifen resistance and provide a molecular basis for their function in tamoxifen-resistant breast cancer.

          Related collections

          Most cited references65

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

              The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
                Bookmark

                Author and article information

                Contributors
                Journal
                Journal of Cell Science
                The Company of Biologists
                0021-9533
                1477-9137
                June 01 2021
                June 01 2021
                June 07 2021
                : 134
                : 11
                Affiliations
                [1 ]Department of Life Science, Hanyang University, Seoul 04763, Korea
                [2 ]Department of Breast Cancer Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
                [3 ]Natural Science Institute, Hanyang University, Seoul 04763, Korea
                Article
                10.1242/jcs.256503
                34096606
                b2524661-126f-40d6-a675-639f81ca2cfa
                © 2021

                http://www.biologists.com/user-licence-1-1/

                History

                Comments

                Comment on this article