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Enregistrement W2920486402 · doi:10.18632/oncotarget.26678

Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development

2019· article· en· W2920486402 sur OpenAlexaff
Yafang Li, Xiangjun Xiao, Yohan Bossé, Olga Y. Gorlova, Ivan P. Gorlov, Younghun Han, Jinyoung Byun, Natasha B. Leighl, Jakob Sidenius Johansen, Matt P Barnett, Chen Chu, Gary E. Goodman, Angela Cox, Fiona Taylor, Penella J. Woll, H.‐Erich Wichmann, Judith Manz, Thomas Muley, Angela Risch, Albert Rosenberger, Jiali Han, Katherine A. Siminovitch, Susanne M. Arnold, Eric B. Haura, Ciprian Bolca, Ivana Holcátová, Vladimí­r Janout, Milica Kontić, Jolanta Lissowska, Anush Mukeria, Simona Ognjanovic, Tadeusz Orłowski, Ghislaine Scélo, Beata Świątkowska, Давид Заридзе, Per Bakke, Vidar Skaug, Shanbeh Zienolddiny, Eric J. Duell, Lesley M. Butler, Richard S. Houlston, María Soler Artigas, Kjell Grankvist, Mikael Johansson, Frances A. Shepherd, Michael W. Marcus, Hans Brunnström, Jonas Manjer, Olle Melander, David C. Muller, Kim Overvad, Antonia Trichopoulou, ­Rosario ­Tumino, Geoffrey Liu, Stig E. Bojesen, Xifeng Wu, Loı̈c Le Marchand, Demetrius Albanes, Heike Bickeböller, Melinda C. Aldrich, William S. Bush, Adonina Tardón, Gad Rennert, M. Dawn Teare, John K. Field, Lambertus A. Kiemeney, Philip Lazarus, Aage Haugen, Stephen Lam, Matthew B. Schabath, Angeline S. Andrew, Pier Alberto Bertazzi, Angela Cecilia Pesatori, David C. Christiani, Neil E. Caporaso, Mattias Johansson, James McKay, Paul Brennan, Christopher I. Amos

Notice bibliographique

RevueOncotarget · 2019
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueRNA modifications and cancer
Établissements canadiensLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of TorontoBC Cancer AgencyPrincess Margaret Cancer CentreUniversité Laval
Organismes subventionnairesNational Cancer InstituteWorld Health Organization
Mots-clésCarcinogenesisLung cancerGeneBiologyGeneticsCancerCancer researchComputational biologyMedicineBioinformaticsOncology

Résumé

récupéré en direct d'OpenAlex

// Yafang Li 1 , Xiangjun Xiao 1 , Yohan Bossé 2 , Olga Gorlova 3 , Ivan Gorlov 3 , Younghun Han 1 , Jinyoung Byun 1 , Natasha Leighl 4 , Jakob S. Johansen 5 , Matt Barnett 6 , Chu Chen 6 , Gary Goodman 7 , Angela Cox 8 , Fiona Taylor 8 , Penella Woll 8 , H. Erich Wichmann 9 , Judith Manz 9 , Thomas Muley 10 , Angela Risch 11,12,13 , Albert Rosenberger 14 , Jiali Han 15 , Katherine Siminovitch 16 , Susanne M. Arnold 17 , Eric B. Haura 18 , Ciprian Bolca 19 , Ivana Holcatova 20 , Vladimir Janout 20 , Milica Kontic 21 , Jolanta Lissowska 22 , Anush Mukeria 23 , Simona Ognjanovic 24 , Tadeusz M. Orlowski 25 , Ghislaine Scelo 26 , Beata Swiatkowska 27 , David Zaridze 23 , Per Bakke 28 , Vidar Skaug 29 , Shanbeh Zienolddiny 29 , Eric J. Duell 30 , Lesley M. Butler 31 , Richard Houlston 32 , María Soler Artigas 33,34 , Kjell Grankvist 35 , Mikael Johansson 36 , Frances A. Shepherd 37 , Michael W. Marcus 38 , Hans Brunnström 39 , Jonas Manjer 40 , Olle Melander 40 , David C. Muller 41 , Kim Overvad 42 , Antonia Trichopoulou 43 , Rosario Tumino 44 , Geoffrey Liu 45 , Stig E. Bojesen 46,47,48 , Xifeng Wu 49 , Loic Le Marchand 50 , Demetrios Albanes 51 , Heike Bickeböller 14 , Melinda C. Aldrich 52 , William S. Bush 53 , Adonina Tardon 54 , Gad Rennert 55 , M. Dawn Teare 56 , John K. Field 38 , Lambertus A. Kiemeney 57 , Philip Lazarus 58 , Aage Haugen 59 , Stephen Lam 60 , Matthew B. Schabath 61 , Angeline S. Andrew 62 , Pier Alberto Bertazzi 63,64 , Angela C. Pesatori 64 , David C. Christiani 65 , Neil Caporaso 51 , Mattias Johansson 45 , James D. McKay 45 , Paul Brennan 45 , Rayjean J. Hung 26 and Christopher I. Amos 66 1 Baylor College of Medicine, Houston, TX, USA 2 Laval University, Quebec, QC, Canada 3 Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA 4 University Health Network, The Princess Margaret Cancer Centre, Toronto, CA, USA 5 Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark 6 Fred Hutchinson Cancer Research Center, Seattle, WA, USA 7 Swedish Medical Group, Seattle, WA, USA 8 Department of Oncology, University of Sheffield, Sheffield, UK 9 Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany 10 Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany 11 Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany 12 German Center for Lung Research (DKFZ), Heidelberg, Germany 13 University of Salzburg and Cancer Cluster, Salzburg, Austria 14 Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany 15 Indiana University, Bloomington, IN, USA 16 University of Toronto, Toronto, ON, Canada 17 University of Kentucky, Markey Cancer Center, Lexington, KY, USA 18 Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 19 Institute of Pneumology “Marius Nasta”, Bucharest, Romania 20 Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic 21 Clinical Center of Serbia, School of Medicine, University of Belgrade, Belgrade, Serbia 22 M. Sklodowska-Curie Cancer Center, Institute of Oncology, Warsaw, Poland 23 Department of Epidemiology and Prevention, N.N. Blokhin Russian Cancer Research Center, Moscow, Russian Federation 24 International Organization for Cancer Prevention and Research, Belgrade, Serbia 25 Department of Surgery, National Tuberculosis and Lung Diseases Research Institute, Warsaw, Poland 26 International Agency for Research on Cancer, World Health Organization, Lyon, France 27 Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland 28 Department of Clinical Science, University of Bergen, Bergen, Norway 29 National Institute of Occupational Health, Oslo, Norway 30 Unit of Nutrition and Cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain 31 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA 32 The Institute of Cancer Research, London, UK 33 Department of Health Sciences, Genetic Epidemiology Group, University of Leicester, Leicester, UK 34 National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, UK 35 Department of Medical Biosciences, Umeå University, Umeå, Sweden 36 Department of Radiation Sciences, Umeå University, Umeå, Sweden 37 Princess Margaret Cancer Centre, Toronto, ON, Canada 38 Institute of Translational Medicine, University of Liverpool, Liverpool, UK 39 Department of Pathology, Lund University, Lund, Sweden 40 Faculty of Medicine, Lund University, Lund, Sweden 41 School of Public Health, St. Mary’s Campus, Imperial College London, London, UK 42 Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark 43 Hellenic Health Foundation, Athens, Greece 44 Molecular and Nutritional Epidemiology Unit CSPO (Cancer Research and Prevention Centre), Scientific Institute of Tuscany, Florence, Italy 45 Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada 46 Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark 47 Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 48 Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark 49 Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 50 Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA 51 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 52 Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA 53 Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA 54 IUOPA, University of Oviedo and CIBERESP, Faculty of Medicine, Campus del Cristo s/n, Oviedo, Spain 55 Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel 56 School of Health and Related Research, University of Sheffield, Sheffield, UK 57 Radboud University Medical Center, Nijmegen, The Netherlands 58 Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA 59 National Institute of Occupational Health, Oslo, Norway 60 British Columbia Cancer Agency, Vancouver, Canada 61 Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 62 Department of Epidemiology, Geisel School of Medicine, Hanover, NH, USA 63 Department of Preventive Medicine, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy 64 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy 65 Department of Epidemiology, Program in Molecular and Genetic Epidemiology Harvard School of Public Health, Boston, MA, USA 66 Biomedical Data Science Department, Dartmouth College, Hanover, NH, USA Correspondence to: Christopher I. Amos, email: Christopher.i.amos@dartmouth.edu Keywords : epistasis; lung cancer; oncogenesis; functional annotation Received: October 27, 2018     Accepted: January 22, 2019     Published: March 05, 2019 Abstract The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterationsand tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10 -11 in overall lung cancer and OR=0.41, p value=9.71x10 -11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10 -12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10 -11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,256
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations34
Publié2019
Routes d'admission1
Résumé présentoui

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