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      Leukemia inhibitory factor suppresses hepatic de novo lipogenesis and induces cachexia in mice

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          Abstract

          Cancer cachexia is a systemic metabolic syndrome characterized by involuntary weight loss, and muscle and adipose tissue wasting. Mechanisms underlying cachexia remain poorly understood. Leukemia inhibitory factor (LIF), a multi-functional cytokine, has been suggested as a cachexia-inducing factor. In a transgenic mouse model with conditional LIF expression, systemic elevation of LIF induces cachexia. LIF overexpression decreases de novo lipogenesis and disrupts lipid homeostasis in the liver. Liver-specific LIF receptor knockout attenuates LIF-induced cachexia, suggesting that LIF-induced functional changes in the liver contribute to cachexia. Mechanistically, LIF overexpression activates STAT3 to downregulate PPARα, a master regulator of lipid metabolism, leading to the downregulation of a group of PPARα target genes involved in lipogenesis and decreased lipogenesis in the liver. Activating PPARα by fenofibrate, a PPARα agonist, restores lipid homeostasis in the liver and inhibits LIF-induced cachexia. These results provide valuable insights into cachexia, which may help develop strategies to treat cancer cachexia.

          Abstract

          Cancer cachexia is a systemic syndrome characterized by dramatic weight loss and decline in adipose tissue and skeletal muscle mass. Here, the authors show that overexpression of leukemia inhibitory factor (LIF), a secreted cytokine, suppresses de novo lipogenesis and induces cachexia in mice.

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          Most cited references64

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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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            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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              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
                fengzh@cinj.rutgers.edu
                wh221@cinj.rutgers.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 January 2024
                20 January 2024
                2024
                : 15
                : 627
                Affiliations
                [1 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, , Rutgers University, ; New Brunswick, NJ USA
                [2 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Medicine, , Rutgers-Robert Wood Johnson Medical School, ; New Brunswick, NJ USA
                [3 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Rutgers Cancer Institute of New Jersey, , Rutgers University, ; New Brunswick, NJ USA
                [4 ]Department of Pharmacology and Toxicology, Rutgers University, ( https://ror.org/05vt9qd57) Piscataway, NJ USA
                [5 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Environmental and Occupational Health Science Institute, , Rutgers University, ; Piscataway, NJ USA
                [6 ]GRID grid.422069.b, ISNI 0000 0004 0420 0456, Department of Veterans Affairs New Jersey Health Care System, ; East Orange, NJ USA
                [7 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Biostatistics and Epidemiology, , Rutgers School of Public Health, ; Piscataway, NJ USA
                [8 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Biostatistics Shared Resource, Rutgers Cancer Institute of New Jersey, , Rutgers University, ; New Brunswick, NJ USA
                [9 ]Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, ( https://ror.org/05vt9qd57) Piscataway, NJ USA
                [10 ]Metabolomics Core Facility, Rutgers Cancer Institute of New Jersey, ( https://ror.org/0060x3y55) New Brunswick, NJ USA
                [11 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Ludwig Princeton Branch, Ludwig Institute for Cancer Research, , Princeton University, ; Princeton, NJ USA
                Author information
                http://orcid.org/0000-0002-7265-1764
                http://orcid.org/0000-0002-4562-9393
                http://orcid.org/0000-0001-8081-1396
                http://orcid.org/0000-0003-2961-3065
                http://orcid.org/0000-0002-8912-6456
                http://orcid.org/0000-0003-3971-4257
                Article
                44924
                10.1038/s41467-024-44924-w
                10799847
                38245529
                4d4ffd71-d5b2-4b24-a3fe-2230cd8e650e
                © 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
                : 24 May 2023
                : 8 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: R01CA260837
                Award ID: R01CA260838
                Award ID: R01CA227912
                Award ID: R01CA214746
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                cancer metabolism,cancer models,metabolic disorders
                Uncategorized
                cancer metabolism, cancer models, metabolic disorders

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