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      A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer’s Disease, Glioblastoma and Lung cancer

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          Abstract

          Epidemiological studies indicate that patients suffering from Alzheimer’s disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer’s disease and lung cancer, and with similar patterns of expression in Alzheimer’s disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analysis of the sets of deregulated genes points to the immune system, up-regulated in both Alzheimer’s disease and glioblastoma, as a potential link between these two diseases. Mitochondrial metabolism is regulated oppositely in Alzheimer’s disease and lung cancer, indicating that it may be involved in the inverse co-morbidity between these diseases. Finally, oxidative phosphorylation is a good candidate to play a dual role by decreasing or increasing the risk of lung cancer and glioblastoma in Alzheimer’s disease.

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          Most cited references52

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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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            Discovery of drug mode of action and drug repositioning from transcriptional responses.

            A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).
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              Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets

              Adaikalavan Ramasamy and colleagues outline seven key issues and suggest a stepwise approach in conducting a meta-analysis of microarray datasets.
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                Author and article information

                Contributors
                anais.baudot@univ-amu.fr
                valencia@cnio.es
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 June 2017
                30 June 2017
                2017
                : 7
                : 4474
                Affiliations
                [1 ]ISNI 0000 0000 8700 1153, GRID grid.7719.8, , Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), ; Madrid, 28029 Spain
                [2 ]ISNI 0000 0000 8700 1153, GRID grid.7719.8, , Clinical Research Programme, Spanish National Cancer Research Centre (CNIO), ; Madrid, 28029 Spain
                [3 ]ISNI 0000 0000 8970 9163, GRID grid.81821.32, , Bioinformatics section, Institute of Medical and Molecular Genetics (INGEMM), Hospital Universitario La Paz, ; Madrid, 28046 Spain
                [4 ]Department of Medicine, Hospital HM Sanchinarro, Madrid, 28050 Spain
                [5 ]ISNI 0000 0001 2173 938X, GRID grid.5338.d, Department of Medicine, , University of Valencia, CIBERSAM, INCLIVA, ; Valencia, 46010 Spain
                [6 ]ISNI 0000 0001 2176 4817, GRID grid.5399.6, , Aix-Marseille Université, CNRS, Centrale Marseille, ; I2M UMR7373 Marseille, France
                [7 ]ISNI 0000 0004 0387 1602, GRID grid.10097.3f, , Barcelona Supercomputing Center (BSC), ; Barcelona, 08034 Spain
                [8 ]ISNI 0000 0000 9601 989X, GRID grid.425902.8, , ICREA, ; Barcelona, 08010 Spain
                Author information
                http://orcid.org/0000-0003-0885-7933
                Article
                4400
                10.1038/s41598-017-04400-6
                5493619
                28667284
                8b762bbd-1c3f-477c-8c69-e0e4045a2dbc
                © The Author(s) 2017

                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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                : 25 August 2016
                : 25 May 2017
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