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      Fatty acid metabolism in aggressive B-cell lymphoma is inhibited by tetraspanin CD37

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          Abstract

          The importance of fatty acid (FA) metabolism in cancer is well-established, yet the mechanisms underlying metabolic reprogramming remain elusive. Here, we identify tetraspanin CD37, a prognostic marker for aggressive B-cell lymphoma, as essential membrane-localized inhibitor of FA metabolism. Deletion of CD37 on lymphoma cells results in increased FA oxidation shown by functional assays and metabolomics. Furthermore, CD37-negative lymphomas selectively deplete palmitate from serum in mouse studies. Mechanistically, CD37 inhibits the FA transporter FATP1 through molecular interaction. Consequently, deletion of CD37 induces uptake and processing of exogenous palmitate into energy and essential building blocks for proliferation, and inhibition of FATP1 reverses this phenotype. Large lipid deposits and intracellular lipid droplets are observed in CD37-negative lymphoma tissues of patients. Moreover, inhibition of carnitine palmitoyl transferase 1 A significantly compromises viability and proliferation of CD37-deficient lymphomas. Collectively, our results identify CD37 as a direct gatekeeper of the FA metabolic switch in aggressive B-cell lymphoma.

          Abstract

          Tetraspanin CD37 deficiency has been reported as a prognostic marker for aggressive B-cell lymphoma. Here, the authors show that CD37 interacts with the fatty acid transporter 1 to inhibit palmitate uptake and its deficiency leads to increased fatty acid metabolism which promotes tumorigenesis in B-cell lymphoma.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            NIH Image to ImageJ: 25 years of image analysis

            For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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              Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

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              The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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                Author and article information

                Contributors
                Annemiek.vanspriel@radboudumc.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 September 2022
                13 September 2022
                2022
                : 13
                : 5371
                Affiliations
                [1 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, , Radboud University Medical Centre, ; Nijmegen, The Netherlands
                [2 ]GRID grid.5477.1, ISNI 0000000120346234, Division of Cell Biology, Metabolism & Cancer, Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, , Utrecht University, ; Utrecht, The Netherlands
                [3 ]GRID grid.5477.1, ISNI 0000000120346234, Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular Research, , Utrecht University, ; Utrecht, The Netherlands
                [4 ]GRID grid.10419.3d, ISNI 0000000089452978, Metabolics Group, , Leiden University Medical Centre, ; Leiden, The Netherlands
                [5 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Human Nutrition and Health, , Wageningen University, ; Wageningen, The Netherlands
                [6 ]GRID grid.6936.a, ISNI 0000000123222966, Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Klinikum rechts der Isar, , Technical University Munich, ; Munich, Germany
                [7 ]GRID grid.6936.a, ISNI 0000000123222966, TranslaTUM, Center for Translational Cancer Research, , Technical University Munich, ; Munich, Germany
                [8 ]GRID grid.5963.9, Laboratory of Clinical Biochemistry and Metabolism, Department of General Paediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, , Medical Centre - University of Freiburg, ; 79106 Freiburg, Germany
                [9 ]GRID grid.5963.9, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, , Medical Centre - University of Freiburg, ; 79106 Freiburg, Germany
                Author information
                http://orcid.org/0000-0002-0101-7352
                http://orcid.org/0000-0001-9890-7345
                http://orcid.org/0000-0002-6596-0876
                http://orcid.org/0000-0002-5234-2104
                http://orcid.org/0000-0003-1684-1894
                http://orcid.org/0000-0002-5385-6705
                http://orcid.org/0000-0003-0746-8565
                http://orcid.org/0000-0002-3590-2368
                Article
                33138
                10.1038/s41467-022-33138-7
                9470561
                36100608
                d48458e2-b7ab-4597-af13-de402fb7d509
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 June 2021
                : 30 August 2022
                Funding
                Funded by: NWO project 184.034.019
                Funded by: IMI grant (Hyporesolve) Health Holland (Timid)
                Funded by: ICI-00014 from the Institute for Chemical Immunology, funded by the NWO Gravitation Programme
                Funded by: FundRef https://doi.org/10.13039/501100001826, ZonMw (Netherlands Organisation for Health Research and Development);
                Award ID: 09120012010023
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004622, KWF Kankerbestrijding (Dutch Cancer Society);
                Award ID: 11618/2018
                Award ID: 12949/2020
                Award Recipient :
                Funded by: European Research Council Consolidator Grant (Secret Surface, 724281)
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer metabolism,mechanisms of disease
                Uncategorized
                cancer metabolism, mechanisms of disease

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