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      Androgen drives melanoma invasiveness and metastatic spread by inducing tumorigenic fucosylation

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          Abstract

          Melanoma incidence and mortality rates are historically higher for men than women. Although emerging studies have highlighted tumorigenic roles for the male sex hormone androgen and its receptor (AR) in melanoma, cellular and molecular mechanisms underlying these sex-associated discrepancies are poorly defined. Here, we delineate a previously undisclosed mechanism by which androgen-activated AR transcriptionally upregulates fucosyltransferase 4 ( FUT4) expression, which drives melanoma invasiveness by interfering with adherens junctions (AJs). Global phosphoproteomic and fucoproteomic profiling, coupled with in vitro and in vivo functional validation, further reveal that AR-induced FUT4 fucosylates L1 cell adhesion molecule (L1CAM), which is required for FUT4-increased metastatic capacity. Tumor microarray and gene expression analyses demonstrate that AR-FUT4-L1CAM-AJs signaling correlates with pathological staging in melanoma patients. By delineating key androgen-triggered signaling that enhances metastatic aggressiveness, our findings help explain sex-associated clinical outcome disparities and highlight AR/FUT4 and its effectors as potential prognostic biomarkers and therapeutic targets in melanoma.

          Abstract

          Mechanisms underlying sex associated differences in the role of androgen receptor (AR) in melanoma are unclear. Here the authors show that androgen-activated AR transcriptionally upregulates fucosyltransferase 4, which fucosylates L1CAM and promotes melanoma invasiveness by disrupting adherens junctions.

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          Most cited references103

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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.
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            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.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Contributors
                Eric.Lau@Moffitt.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 February 2024
                7 February 2024
                2024
                : 15
                : 1148
                Affiliations
                [1 ]Department of Tumor Microenvironment and Metastasis, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [2 ]Cancer Biology Ph.D. Program, University of South Florida, ( https://ror.org/032db5x82) Tampa, FL USA
                [3 ]Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [4 ]Proteomics and Metabolomics Core, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [5 ]Analytic Microscopy Core, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [6 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Surgical Oncology, , MD Anderson Cancer Center, ; Houston, TX USA
                [7 ]Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [8 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, Department of Genomic Medicine, , MD Anderson Cancer Center, ; Houston, TX USA
                [9 ]Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, ( https://ror.org/01xf75524) Tampa, FL USA
                [10 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Biochemistry and Molecular Biology, , University of Maryland School of Medicine, ; Baltimore, MD USA
                Author information
                http://orcid.org/0000-0002-7636-1318
                http://orcid.org/0000-0001-5022-5505
                http://orcid.org/0000-0002-3818-1762
                http://orcid.org/0000-0003-3438-7576
                http://orcid.org/0000-0001-7884-7656
                http://orcid.org/0000-0003-3005-3421
                Article
                45324
                10.1038/s41467-024-45324-w
                10850104
                38326303
                182a4573-f94f-4f51-ac90-3bb53b9aa8ff
                © The Author(s) 2024

                Open Access This 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/.

                History
                : 28 July 2023
                : 18 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: R01CA241559
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006827, Florida Department of Health;
                Award ID: 22B02
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                melanoma,metastasis,mechanisms of disease,cell invasion,hormone receptors
                Uncategorized
                melanoma, metastasis, mechanisms of disease, cell invasion, hormone receptors

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