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Enregistrement W2140087277 · doi:10.18632/oncotarget.5142

Prognostic role and implications of mutation status of tumor suppressor gene ARID1A in cancer: a systematic review and meta-analysis

2015· review· en· W2140087277 sur OpenAlexaffabout
Claudio Luchini, Nicola Veronese, Marco Solmi, Hanbyoul Cho, Jae‐Hoon Kim, Angela Chou, Anthony J. Gill, Sheila F. Faraj, Alcides Chaux, George J. Netto, Kentaro Nakayama, Satoru Kyo, Soo Young Lee, Duck-Woo Kim, George M. Yousef, Andreas Scorilas, Gregg Nelson, Martin Köbel, Steve E. Kalloger, David F. Schaeffer, Hai-Bo Yan, Feng Liu, Yoshihito Yokoyama, Xianyu Zhang, Da Pang, Zsuzsanna Lichner, Giuseppe Sergi, Enzo Manzato, Paola Capelli, Laura D. Wood, Aldo Scarpa, Christoph U. Correll

Notice bibliographique

RevueOncotarget · 2015
Typereview
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueChromatin Remodeling and Cancer
Établissements canadiensUniversity of British ColumbiaUniversity of CalgarySt. Michael's Hospital
Organismes subventionnairesNational Institute of Mental HealthH. Lundbeck A/SGenentechAssociazione Italiana per la Ricerca sul CancroNational Alliance for Research on Schizophrenia and DepressionSunovionPfizerTeva Pharmaceutical IndustriesEli Lilly and CompanyBristol-Myers Squibb
Mots-clésARID1AMedicineConfoundingHazard ratioOncologyCancerInternal medicineConfidence intervalMeta-analysisGeneMutationGeneticsBiology

Résumé

récupéré en direct d'OpenAlex

// Claudio Luchini 1, 2 , Nicola Veronese 3 , Marco Solmi 4 , Hanbyoul Cho 5 , Jae-Hoon Kim 5 , Angela Chou 6, 7 , Anthony J. Gill 6 , Sheila F. Faraj 2 , Alcides Chaux 2, 8 , George J. Netto 2 , Kentaro Nakayama 9 , Satoru Kyo 9 , Soo Young Lee 10 , Duck-Woo Kim 11 , George M. Yousef 12 , Andreas Scorilas 13 , Gregg S. Nelson 14 , Martin Köbel 15 , Steve E. Kalloger 16 , David F. Schaeffer 16 , Hai-Bo Yan 17 , Feng Liu 17 , Yoshihito Yokoyama 18 , Xianyu Zhang 19 , Da Pang 19 , Zsuzsanna Lichner 20 , Giuseppe Sergi 3 , Enzo Manzato 3 , Paola Capelli 1 , Laura D. Wood 2 , Aldo Scarpa 1 , Christoph U. Correll 21, 22, 23, 24 1 Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy 2 Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA 3 Department of Medicine, Geriatrics Division, University of Padova, Padova, Italy 4 Department of Neurosciences, University of Padova, Padova, Italy 5 Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea 6 Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, St. Leonards, Australia, Sydney Vital Translational Research Centre St. Leonards Australia and University of Sydney, Sydney, NSW, Australia 7 Department of Anatomical Pathology, SYDPATH St. Vincent’s Hospital, Sydney, NSW, Australia 8 Centro para el Desarrollo de la Investigación Científica (CEDIC), Asunción, Paraguay 9 Department of Obstetrics and Gynecology, Shimane University School of Medicine, Shimane, Japan 10 Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon, South Korea 11 Department of Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea 12 Department of Laboratory Medicine and Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada 13 Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Athens, Athens, Greece 14 Department of Gynecologic Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada 15 Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada 16 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada 17 Department of Systems Biology for Medicine of School of Basic Medical Sciences, and Institutes of Biomedical Sciences, Fudan University, Shanghai, China 18 Department of Obstetrics and Gynecology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan 19 Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China 20 Department of Laboratory Medicine and Keenan Research Centre, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, Ontario, Canada 21 The Zucker Hillside Hospital, Psychiatry Research, North Shore - Long Island Jewish Health System, Glen Oaks, New York, USA 22 Hofstra North Shore LIJ School of Medicine, Hempstead, New York, USA 23 The Feinstein Institute for Medical Research, Manhasset, New York, USA 24 Albert Einstein College of Medicine, Bronx, New York, USA Correspondence to: Claudio Luchini, e-mail: claudio.luchini@katamail.com , claudio.luchini@univr.it Keywords: ARID1A, SWI/SNF, chromatin remodeling, targeted therapy, tumor suppressor gene Received: July 07, 2015      Accepted: August 27, 2015      Published: September 08, 2015 ABSTRACT Loss of the tumor suppressor gene AT-rich interactive domain-containing protein 1A (ARID1A) has been demonstrated in several cancers, but its prognostic role is unknown. We aimed to investigate the risk associated with loss of ARID1A (ARID1A-) for all-cause mortality, cancer-specific mortality and recurrence of disease in subjects with cancer. PubMed and SCOPUS search from database inception until 01/31/2015 without language restriction was conducted, contacting authors for unpublished data. Eligible were prospective studies reporting data on prognostic parameters in subjects with cancer, comparing participants with presence of ARID1A (ARID1A+) vs. ARID1A-, assessed either via immunohistochemistry (loss of expression) or with genetic testing (presence of mutation). Data were summarized using risk ratios (RR) for number of deaths/recurrences and hazard ratios (HR) for time-dependent risk related to ARID1A- adjusted for potential confounders. Of 136 hits, 25 studies with 5,651 participants (28 cohorts; ARID1A-: n = 1,701; ARID1A+: n = 3,950), with a mean follow-up period of 4.7 ± 1.8 years, were meta-analyzed. Compared to ARID1A+, ARID1A- significantly increased cancer-specific mortality (studies = 3; RR = 1.55, 95% confidence interval (CI) = 1.19–2.00, I 2 = 31%). Using HRs adjusted for potential confounders, ARID1A- was associated with a greater risk of cancer-specific mortality (studies = 2; HR = 2.55, 95%CI = 1.19–5.45, I 2 = 19%) and cancer recurrence (studies = 10; HR = 1.93, 95%CI = 1.22–3.05, I 2 = 76%). On the basis of these results, we have demonstrated that loss of ARID1A shortened time to cancer-specific mortality, and to recurrence of cancer when adjusting for potential confounders. For its role, this gene should be considered as an important potential target for personalized medicine in cancer treatment.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,949
Score d'incertitude au seuil0,652

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0030,000
Bibliométrie0,0000,000
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)0,0000,000

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 Opus0,052
Tête enseignante GPT0,367
Écart entre enseignants0,315 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

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

En bref

Citations78
Publié2015
Routes d'admission2
Résumé présentoui

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