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      Evidence for genetic association between chromosome 1q loci and predisposition to colorectal neoplasia

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          Abstract

          Background:

          A substantial fraction of familial colorectal cancer (CRC) and polyposis heritability remains unexplained. This study aimed to identify predisposing loci in patients with these disorders.

          Methods:

          Homozygosity mapping was performed using 222 563 SNPs in 302 index patients with various colorectal neoplasms and 3367 controls. Linkage analysis, exome and whole-genome sequencing were performed in a family affected by microsatellite stable CRCs. Candidate variants were genotyped in 10 554 cases and 21 480 controls. Gene expression was assessed at the mRNA and protein level.

          Results:

          Homozygosity mapping revealed a disease-associated region at 1q32.3 which was part of the linkage region 1q32.2–42.2 identified in the CRC family. This includes a region previously associated with risk of CRC. Sequencing identified the p.Asp1432Glu variant in the MIA3 gene (known as TANGO1 or TANGO) and 472 additional rare, shared variants within the linkage region. In both cases and controls the population frequency was 0.02% for this MIA3 variant. The MIA3 mutant allele showed predominant mRNA expression in normal, cancer and precancerous tissues. Furthermore, immunohistochemistry revealed increased expression of MIA3 in adenomatous tissues.

          Conclusions:

          Taken together, our two independent strategies associate genetic variations in chromosome 1q loci and predisposition to familial CRC and polyps, which warrants further investigation.

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          Most cited references60

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.

            The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications. © 2012 AACR.
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              A new mathematical model for relative quantification in real-time RT-PCR.

              M. Pfaffl (2001)
              Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.
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                Author and article information

                Journal
                Br J Cancer
                Br. J. Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                10 October 2017
                25 July 2017
                10 October 2017
                : 117
                : 8
                : 1215-1223
                Affiliations
                [1 ]Department of Pathology, Leiden University Medical Center, Leiden University , Leiden 2300 RC, The Netherlands
                [2 ]Department of Endocrinology, University of Groningen, University Medical Center Groningen , Groningen 9700 RB, The Netherlands
                [3 ]Department of Clinical Genetics, Leiden University Medical Center, Leiden University , Leiden 2300 RC, The Netherlands
                [4 ]Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden University , Leiden 2300 RC, The Netherlands
                [5 ]Department of Human Genetics, Leiden University Medical Center, Leiden University , Leiden 2300 RC, The Netherlands
                [6 ]Department of Gastroenterology, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona , Barcelona, Catalonia 08036, Spain
                [7 ]Fundación Pública Galega de Medicina Xenómica (FPGMX)-SERGAS, Grupo de Medicina Xenómica-USC, Instituto de Investigación Sanitaria de Santiago (IDIS), Centro de Investigación en Red de Enfermedades Raras (CIBERER) , Santiago de Compostela 15706, Spain
                [8 ]Oxford Centre for Cancer Gene Research, Wellcome Trust Centre for Human Genetics, University of Oxford , Oxford OX3 7BN, UK
                [9 ]Colon Cancer Genetics Group, MRC Human Genetics Unit, The University of Edinburgh, Western General Hospital , Edinburgh EH4 2XU, UK
                [10 ]Institute of Experimental Medicine, Institute of Biology and Medical Genetics , Prague 142 00, Czech Republic
                [11 ]Department of Genetics, University of Groningen, University Medical Centre Groningen , Groningen 9700 RB, The Netherlands
                Author notes
                Article
                bjc2017240
                10.1038/bjc.2017.240
                5589990
                28742792
                773d850e-96b7-4256-a6c2-31a736fbf163
                Copyright © 2017 The Author(s)

                This work is licensed under the Creative Commons Attribution-Non-Commercial-Share Alike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 21 March 2017
                : 31 May 2017
                : 30 June 2017
                Categories
                Genetics & Genomics

                Oncology & Radiotherapy
                hereditary colorectal cancer,colorectal polyps,homozygosity mapping,linkage analysis,exome sequencing,chromosome 1q,mia3

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