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      Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly

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          Abstract

          Disease epidemiology during ageing shows a transition from cancer to degenerative chronic disorders as dominant contributors to mortality in the old. Nevertheless, it has remained unclear to what extent molecular signatures of ageing reflect this phenomenon. Here we report on the identification of a conserved transcriptomic signature of ageing based on gene expression data from four vertebrate species across four tissues. We find that ageing-associated transcriptomic changes follow trajectories similar to the transcriptional alterations observed in degenerative ageing diseases but are in opposite direction to the transcriptomic alterations observed in cancer. We confirm the existence of a similar antagonism on the genomic level, where a majority of shared risk alleles which increase the risk of cancer decrease the risk of chronic degenerative disorders and vice versa. These results reveal a fundamental trade-off between cancer and degenerative ageing diseases that sheds light on the pronounced shift in their epidemiology during ageing.

          Abstract

          Ageing is associated with a pronounced shift in mortality from cancer to degenerative diseases. Here, the authors show that in concordance with this shift, conserved transcriptional alterations during ageing across four vertebrates align with degenerative diseases but are opposite to those in cancer.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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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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              Hallmarks of Cancer: The Next Generation

              The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                c.kaleta@iem.uni-kiel.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                30 January 2018
                30 January 2018
                2018
                : 9
                : 327
                Affiliations
                [1 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Research Group Medical Systems Biology, , Institute of Experimental Medicine, Christian-Albrechts-University Kiel, ; D-24105 Kiel, Germany
                [2 ]ISNI 0000 0001 1939 2794, GRID grid.9613.d, Jena University Language and Information Engineering Lab, , Friedrich-Schiller-University Jena, ; D-07743 Jena, Germany
                [3 ]GerontoSys JenAge Consortium, D-07745 Jena, Germany
                [4 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Genome Analysis Lab, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [5 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Hans Berger Department of Neurology, , Jena University Hospital, ; D-07747 Jena, Germany
                [6 ]ISNI 0000 0001 0143 807X, GRID grid.418398.f, Systems Biology and Bioinformatics Group, , Leibniz Institute for Natural Product Research and Infection Biology–Hans-Knöll-Institute, ; D-07745 Jena, Germany
                [7 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Biology of Ageing Lab, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [8 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Molecular Genetics Lab, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [9 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Imageing Facility, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [10 ]ISNI 0000 0000 8517 6224, GRID grid.275559.9, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), , Jena University Hospital, ; D-07747 Jena, Germany
                [11 ]ISNI 0000 0001 0143 807X, GRID grid.418398.f, Network Modeling, , Leibniz Institute for Natural Product Research and Infection Biology–Hans Knöll Institute, ; D-07745 Jena, Germany
                [12 ]ISNI 0000 0001 2153 9986, GRID grid.9764.c, Institute for Medical Informatics and Statistics, , Christian-Albrechts-University Kiel, ; D-24105 Kiel, Germany
                [13 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Energy Metabolism Laboratory, , Swiss Federal Institute of Technology (ETH) Zurich, ; Schwerzenbach/Zürich, CH-8603 Switzerland
                [14 ]ISNI 0000 0001 1939 2794, GRID grid.9613.d, Department of Bioinformatics, , Friedrich-Schiller-University Jena, ; D-07743 Jena, Germany
                [15 ]ISNI 0000 0004 1757 3729, GRID grid.5395.a, Laboratory of Neurobiology, Scuola Normale Superiore, , University of Pisa, ; I-56100 Pisa, Italy
                [16 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Molecular Biology Lab, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [17 ]ISNI 0000 0001 1939 2794, GRID grid.9613.d, Faculty of Biology and Pharmacy, , Friedrich-Schiller-University Jena, ; D-07743 Jena, Germany
                [18 ]ISNI 0000 0000 9999 5706, GRID grid.418245.e, Biocomputing Lab, , Leibniz Institute on Aging–Fritz-Lipmann-Institute, ; D-07745 Jena, Germany
                [19 ]ISNI 0000 0001 0941 7177, GRID grid.164295.d, Department of Computer Science and Center for Bioinformatics and Computational Biology, , University of Maryland, ; College Park, MD 20742 USA
                Author information
                http://orcid.org/0000-0002-9199-8990
                http://orcid.org/0000-0003-2109-2453
                http://orcid.org/0000-0001-8004-9514
                Article
                2395
                10.1038/s41467-017-02395-2
                5790807
                29382830
                5b7f086d-cd5d-4652-9731-f6ef988e885d
                © The Author(s) 2018

                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/.

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                : 23 February 2017
                : 27 November 2017
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