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      FBP1 loss disrupts liver metabolism and promotes tumourigenesis through a hepatic stellate cell senescence secretome

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          Abstract

          Crosstalk between deregulated hepatocyte metabolism and cells within the tumour microenvironment, and consequent effects on liver tumourigenesis, are incompletely understood. We show here that hepatocyte-specific loss of the gluconeogenic enzyme fructose 1,6-bisphosphatase 1 (FBP1) disrupts liver metabolic homeostasis and promotes tumour progression. FBP1 is universally silenced in both human and murine liver tumours. Hepatocyte-specific Fbp1 deletion results in steatosis, concomitant with activation and senescence of hepatic stellate cells (HSCs), exhibiting a senescence-associated secretory phenotype (SASP). Depleting senescent HSCs by “senolytic” treatment with dasatinib/quercetin or ABT-263 inhibits tumour progression. We further demonstrate that FBP1-deficient hepatocytes promote HSC activation by releasing HMGB1; blocking its release with the small molecule inflachromene limits FBP1-dependent HSC activation, subsequent SASP development, and tumour progression. Collectively, these findings provide genetic evidence for FBP1 as a metabolic tumour suppressor in liver cancer and establish a critical crosstalk between hepatocyte metabolism and HSC senescence that promotes tumour growth.

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          Most cited references69

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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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            Cancer Statistics, 2017.

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2017, 1,688,780 new cancer cases and 600,920 cancer deaths are projected to occur in the United States. For all sites combined, the cancer incidence rate is 20% higher in men than in women, while the cancer death rate is 40% higher. However, sex disparities vary by cancer type. For example, thyroid cancer incidence rates are 3-fold higher in women than in men (21 vs 7 per 100,000 population), despite equivalent death rates (0.5 per 100,000 population), largely reflecting sex differences in the "epidemic of diagnosis." Over the past decade of available data, the overall cancer incidence rate (2004-2013) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2005-2014) declined by about 1.5% annually in both men and women. From 1991 to 2014, the overall cancer death rate dropped 25%, translating to approximately 2,143,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the cancer death rate was 15% higher in blacks than in whites in 2014, increasing access to care as a result of the Patient Protection and Affordable Care Act may expedite the narrowing racial gap; from 2010 to 2015, the proportion of blacks who were uninsured halved, from 21% to 11%, as it did for Hispanics (31% to 16%). Gains in coverage for traditionally underserved Americans will facilitate the broader application of existing cancer control knowledge across every segment of the population. CA Cancer J Clin 2017;67:7-30. © 2017 American Cancer Society.
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              Mechanisms of NAFLD development and therapeutic strategies

              There has been a rise in the prevalence of nonalcoholic fatty liver disease (NAFLD), paralleling a worldwide increase in diabetes and metabolic syndrome. NAFLD, a continuum of liver abnormalities from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), has a variable course but can lead to cirrhosis and liver cancer. Here we review the pathogenic and clinical features of NAFLD, its major comorbidities, clinical progression and risk of complications and in vitro and animal models of NAFLD enabling refinement of therapeutic targets that can accelerate drug development. We also discuss evolving principles of clinical trial design to evaluate drug efficacy and the emerging targets for drug development that involve either single agents or combination therapies intended to arrest or reverse disease progression.
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                Author and article information

                Journal
                100890575
                21417
                Nat Cell Biol
                Nat. Cell Biol.
                Nature cell biology
                1465-7392
                1476-4679
                31 March 2020
                04 May 2020
                June 2020
                04 November 2020
                : 22
                : 6
                : 728-739
                Affiliations
                [1 ]Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
                [2 ]Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
                [3 ]Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
                [4 ]The Wistar Institute, Philadelphia, Pennsylvania, 19104
                [5 ]Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
                [6 ]RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
                [7 ]Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
                [8 ]Current address: Helen Diller Cancer Center, UCSF, San Francisco, CA 94143-31109
                [9 ]Current address: Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI 48109
                [10 ]Current address: Trinity Partners, LLC, Waltham, MA 02451
                Author notes

                Author contributions

                F.L., B. L. and M.C.S. conceived the project and designed the research studies. F.L. performed most experiments described here. M.B. and K.G. provided help for animal husbandry and technical assistance for mouse experiments. B.L. performed Fbp1 gene targeting and generated chimeric mice. P.H., R.R., J.G. and I.A.B provided help for in vitro assays. K.E.L. and N.L. provided technical assistance for flow cytometry and data analysis. Y.H., P.L. and B.K. provided conceptual advice and helpful discussion. F.L. analyzed data. F.L., B.K., B.L. and M.C.S. wrote the manuscript.

                [* ]Correspondence: M. Celeste Simon, celeste2@ 123456pennmedicine.upenn.edu , Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, 456 BRB II/III, 421 Curie Boulevard, Philadelphia, PA 19104, Bo Li, libo47@ 123456mail.sysu.edu.cn , Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China, RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
                Article
                NIHMS1579974
                10.1038/s41556-020-0511-2
                7286794
                32367049
                efc339e5-c5b9-439d-a8d4-353ecd9911f6

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                Cell biology
                Cell biology

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