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Enregistrement W1996460180 · doi:10.1016/j.juro.2014.02.1121

MP49-20 FINE-MAPPING OF THE KALLIKREIN REGION AND ITS ROLE IN PROSTATE CANCER AGGRESSIVENESS: RESULTS FROM A CANADIAN COHORT AND THE EUROPEAN RANDOMIZED STUDY FOR PROSTATE CANCER SCREENING

2014· article· en· W1996460180 sur OpenAlexaboutno aff
Laurent Briollais, Jingxiong Xu, Maciej Kwiatkowski, Matt Friedlander, Franz Recker, Cynthia Kuk, Sally Hanna, Neil Fleshner, Bharati Bapat, Tristan Juvet, Hong Li, Theodorus van der Kwast, Eleftherios P. Diamandis, Alexandre R. Zlotta, Hilmi Özçelik

Notice bibliographique

RevueThe Journal of Urology · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueProstate Cancer Treatment and Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineProstate cancerCancerCohortRandomized controlled trialGynecologyOncologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

You have accessJournal of UrologyProstate Cancer: Basic Research IV1 Apr 2014MP49-20 FINE-MAPPING OF THE KALLIKREIN REGION AND ITS ROLE IN PROSTATE CANCER AGGRESSIVENESS: RESULTS FROM A CANADIAN COHORT AND THE EUROPEAN RANDOMIZED STUDY FOR PROSTATE CANCER SCREENING Laurent Briollais, Jingxiong Xu, Maciej Kwiatkowski, Matt Friedlander, Franz Recker, Cynthia Kuk, Sally Hanna, Neil E. Fleshner, Bharati Bapat, Tristan Juvet, Hong Li, Theodorus H. van der Kwast, Eleftherios P. Diamandis, Alexandre R. Zlotta, and Hilmi Ozcelik Laurent BriollaisLaurent Briollais More articles by this author , Jingxiong XuJingxiong Xu More articles by this author , Maciej KwiatkowskiMaciej Kwiatkowski More articles by this author , Matt FriedlanderMatt Friedlander More articles by this author , Franz ReckerFranz Recker More articles by this author , Cynthia KukCynthia Kuk More articles by this author , Sally HannaSally Hanna More articles by this author , Neil E. FleshnerNeil E. Fleshner More articles by this author , Bharati BapatBharati Bapat More articles by this author , Tristan JuvetTristan Juvet More articles by this author , Hong LiHong Li More articles by this author , Theodorus H. van der KwastTheodorus H. van der Kwast More articles by this author , Eleftherios P. DiamandisEleftherios P. Diamandis More articles by this author , Alexandre R. ZlottaAlexandre R. Zlotta More articles by this author , and Hilmi OzcelikHilmi Ozcelik More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1121AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives Prostate specific antigen (PSA) or kallikrein 3 (KLK3) is used for early diagnosis of prostate cancer (PCa). Other KLKs, notably KLK2, 4, 5, 6, 10, 11, 13, 14 and 15, have also been associated with PCa. There is a growing need for novel PCa markers that can identify individuals at an increased risk of harbouring aggressive disease. Data from genome-wide association studies have pointed out several candidate single nucleotide polymorphisms (SNPs) in KLK3 and other KLK genes associated with PCa. We aimed to study the role of SNPs in the entire KLK gene region in predicting PCa aggressiveness. Methods The discovery cohort consists of 540 PCa cases accrued from Toronto. The validation cohort included 380 PCa cases from the Swiss arm of the European Randomized Study of Screening for PCa. We genotyped a total of 143 SNPs within the entire kallikrein region (KLK 1-15) and analyzed their association with PCa aggressiveness, stratifying PCa according to Gleason score (GS) or analyzed as a continuous variable. Genotyping was carried out using an Illumina platform. We expanded the analysis by imputing additional SNPs in the region based on the 1,000 genomes reference dataset. The imputed data comprised of 987 SNPs, with approximately 200 that were in complete linkage disequilibrium (LD) with each other. Our statistical analyses included univariate analyses, multivariate analyses with the Group Lasso and Bayesian graphical modeling. Results In Toronto, 223 PCa’s were GS≤6, 275 GS7, 26 GS8, 15 GS9 and 1 was GS10. In the Swiss cohort, 278 PCa’s were GS≤6 whereas 77 were GS7, 18 GS8, 9 GS9 and 1 was GS10. Four SNPs in very strong LD in the KLK6 gene (rs113640578, rs79324425, rs11666929, rs28384475) were strongly associated with GS when used as GS≥7 vs. GS<7 in the Toronto cohort (p= 0.006529, 0.007501, 0.008589 and 0.008589, respectively, OR from 2.618 to 5.247) and in the Swiss validation cohort (all p= 0.0001, all OR= 5.861). Interestingly, the region encompassed by these four SNPs includes another SNP, rs147992404, which corresponds to a rare missense mutation. Another interesting region associated with GS is downstream KLK3 and include the SNPs rs1058205 (OR 1.8, p=0.001), rs2569735 (p=0.004), rs2735839 (p=0.004) and rs62113216 (p=0.004) in the Toronto cohort and the SNP rs55799315 (p=0.009) in the Swiss cohort. Conclusions Our analyses point out interesting variants in the KLK genes, in particular KLK6 and KLK3, associated with PCa aggressiveness and that could have potential clinical applications. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e509 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Laurent Briollais More articles by this author Jingxiong Xu More articles by this author Maciej Kwiatkowski More articles by this author Matt Friedlander More articles by this author Franz Recker More articles by this author Cynthia Kuk More articles by this author Sally Hanna More articles by this author Neil E. Fleshner More articles by this author Bharati Bapat More articles by this author Tristan Juvet More articles by this author Hong Li More articles by this author Theodorus H. van der Kwast More articles by this author Eleftherios P. Diamandis More articles by this author Alexandre R. Zlotta More articles by this author Hilmi Ozcelik More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0,004
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,577
Score d'incertitude au seuil0,842

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,021
Tête enseignante GPT0,288
Écart entre enseignants0,267 · 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'étudeObservationnel
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

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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