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      Molecular Evidence for the Inverse Comorbidity between Central Nervous System Disorders and Cancers Detected by Transcriptomic Meta-analyses

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          Abstract

          There is epidemiological evidence that patients with certain Central Nervous System (CNS) disorders have a lower than expected probability of developing some types of Cancer. We tested here the hypothesis that this inverse comorbidity is driven by molecular processes common to CNS disorders and Cancers, and that are deregulated in opposite directions. We conducted transcriptomic meta-analyses of three CNS disorders (Alzheimer's disease, Parkinson's disease and Schizophrenia) and three Cancer types (Lung, Prostate, Colorectal) previously described with inverse comorbidities. A significant overlap was observed between the genes upregulated in CNS disorders and downregulated in Cancers, as well as between the genes downregulated in CNS disorders and upregulated in Cancers. We also observed expression deregulations in opposite directions at the level of pathways. Our analysis points to specific genes and pathways, the upregulation of which could increase the incidence of CNS disorders and simultaneously lower the risk of developing Cancer, while the downregulation of another set of genes and pathways could contribute to a decrease in the incidence of CNS disorders while increasing the Cancer risk. These results reinforce the previously proposed involvement of the PIN1 gene, Wnt and P53 pathways, and reveal potential new candidates, in particular related with protein degradation processes.

          Author Summary

          A lower-than-expected probability of developing certain types of Cancer has been observed in patients with CNS disorders, including Alzheimer's disease, Parkinson's disease or Schizophrenia. Understanding such a protective effect could be the key to finding novel treatments for both types of conditions, for instance thanks to drug repurposing. However, little is known about the underlying mechanisms for these intriguing inverse comorbidities. Although environmental causes, drug treatments or lower screening surveys might contribute to the inverse comorbidity between complex disorders, we propose that inverse comorbidity is, at least in part, due to genetic factors.

          We observe here that a common set of genes and biological processes are deregulated in opposite directions in CNS disorders and Cancers, i.e. upregulated in CNS disorders and downregulated in Cancers, or vice versa. We propose the alluring hypothesis that the deregulation of these genes and processes could promote CNS disorders and simultaneously lower the initiation or progression of Cancers.

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          Most cited references 34

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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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            affy--analysis of Affymetrix GeneChip data at the probe level.

            The processing of the Affymetrix GeneChip data has been a recent focus for data analysts. Alternatives to the original procedure have been proposed and some of these new methods are widely used. The affy package is an R package of functions and classes for the analysis of oligonucleotide arrays manufactured by Affymetrix. The package is currently in its second release, affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. In this paper, we present the main classes and functions in the package and demonstrate how they can be used to process probe-level data. We also demonstrate the importance of probe-level analysis when using the Affymetrix GeneChip platform.
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              KEGG for linking genomes to life and the environment

              KEGG (http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                February 2014
                20 February 2014
                : 10
                : 2
                Affiliations
                [1 ]Structural Biology and Biocomputing Programme, Spanish National Cancer, Research Centre (CNIO), Madrid, Spain
                [2 ]Department of Medicine, University of Valencia, CIBERSAM, INCLIVA, Valencia, Spain
                [3 ]Aix-Marseille Université, CNRS, I2M, UMR 7373, Marseille, France
                University of Washington, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KI CB RTS AB AV. Performed the experiments: KI CB. Analyzed the data: KI CB. Contributed reagents/materials/analysis tools: KI CB AB. Wrote the paper: AB. Oversaw and reviewed the whole study: AV RTS. Drew figures and tables: KI CB. Network figure: AB.

                Article
                PGENETICS-D-13-02537
                10.1371/journal.pgen.1004173
                3930576

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Pages: 7
                Funding
                This work was supported by a Fellowship from Obra Social la Caixa grant to KI ( http://obrasocial.lacaixa.es/laCaixaFoundation/home_en.html), FPI grant BES-2008-006332 to CB and grant BIO2012 to AV Group. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational biology
                Genetics
                Cancer genetics
                Gene expression
                Gene networks

                Genetics

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