MétaCan
Menu
Retour à la cohorte
Enregistrement W2313750719 · doi:10.1016/j.juro.2015.02.2466

MP68-04 A FIVE—GENE DNA—METHYLATION BIOMARKER PANEL SENSITIVELY DETECTS BLADDER CANCER AND DISCRIMINATES BETWEEN HIGH—GRADE AND LOW—GRADE DISEASE IN VOIDED URINE

2015· article· en· W2313750719 sur OpenAlexaboutno aff
Thomas Hermanns, Ekaterina Olkhov‐Mitsel, Andrea J. Savio, Bethany Gill, Jenna Sykes, Bimal Bhindi, Tristan Juvet, Cynthia Kuk, Aidan P. Noon, Ricardo Rendon, David Waltregny, Theodorus van der Kwast, Antonio Finelli, Neil Fleshner, Kirk Lo, Bharati Bapat, Alexandre R. Zlotta

Notice bibliographique

RevueThe Journal of Urology · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueBladder and Urothelial Cancer Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineBladder cancerBiomarkerUrineCancerGynecologyInternal medicineGeneticsBiology

Résumé

récupéré en direct d'OpenAlex

You have accessJournal of UrologyBladder Cancer: Basic Research IV1 Apr 2015MP68-04 A FIVE—GENE DNA—METHYLATION BIOMARKER PANEL SENSITIVELY DETECTS BLADDER CANCER AND DISCRIMINATES BETWEEN HIGH—GRADE AND LOW—GRADE DISEASE IN VOIDED URINE Thomas Hermanns, Ekaterina Olkhov-Mitsel, Andrea Savio, Bethany Gill, Jenna Sykes, Bimal Bhindi, Tristan Juvet, Cynthia Kuk, Aidan Noon, Ricardo Rendon, David Waltregny, Theodorus H. van der Kwast, Antonio Finelli, Neil E. Fleshner, Kirk Lo, Bharati Bapat, and Alexandre R. Zlotta Thomas HermannsThomas Hermanns More articles by this author , Ekaterina Olkhov-MitselEkaterina Olkhov-Mitsel More articles by this author , Andrea SavioAndrea Savio More articles by this author , Bethany GillBethany Gill More articles by this author , Jenna SykesJenna Sykes More articles by this author , Bimal BhindiBimal Bhindi More articles by this author , Tristan JuvetTristan Juvet More articles by this author , Cynthia KukCynthia Kuk More articles by this author , Aidan NoonAidan Noon More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , David WaltregnyDavid Waltregny More articles by this author , Theodorus H. van der KwastTheodorus H. van der Kwast More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Neil E. FleshnerNeil E. Fleshner More articles by this author , Kirk LoKirk Lo More articles by this author , Bharati BapatBharati Bapat More articles by this author , and Alexandre R. ZlottaAlexandre R. Zlotta More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2466AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Voided urine provides an excellent source of exfoliated cells from the bladder and an ideal medium for detection of bladder cancer (BC) biomarkers. Using two different genome−wide methylation−array profiling platforms in Toronto, CA and Liège, BE, several deferentially methylated genes (TWIST1, NID2, RunX3, Gata4, FoxE1) from low grade (LG) vs. high grade (HG) BC were commonly identified. We investigated methylation of the five genes to non−invasively identify BC in voided urine and discriminate between LG and HG BC METHODS Voided urine from patients with histologically proven LG (n=59) and HG BC (n=64) as well as from BC−free controls (noBC, n=59) was collected. DNA extracted from the urinary cell pellets was analyzed using a highly sensitive, quantitative methylation specific assay (MethyLight) to examine the methylation status of selected candidate genes. Methylation levels (percent methylation reference, PMR) for each sample were obtained from averaging duplicate runs. Associations between PMR and diagnosis of HG vs. LG disease vs. noBC, BC overall vs. noBC and HG vs. LG were performed using the Kruskal−Wallis test or the Mann−Whitney U−test. Univariate and multivariable logistic regression models were used to create ROC curves to evaluate individual biomarker discrimination and combined discrimination, respectively. The Akaike information criterion was used to determine which biomarkers and clinical variables were necessary to include in the final model. RESULTS The median PMRs for each gene were significantly different for HG, LG and noBC (RunX3: p=.0011, all others: p<0.001). The PMRs were significantly higher in BC cases compared to noBC cases for all genes (all p<0.001) and for HG compared to LG BC cases (RunX3: p=.0011, all others: p<0.001). The AUC to predict BC overall was.75 (95%CI:.69−.82) for TWIST1,.75 (.68−.82) for NID2,.70 (.63−.77) for RUNX3,.75 (.68−.81) for Gata4 and.63 (.58−.68) for FoxE1. For the prediction of HG BC the AUC was.72 (.65−.80) for TWIST1,.72 (.63−.81) for NID2,.57 (.49−.66) for RUNX3,.67 (.59−.74) for Gata4 and.68 (.61−.76) for FoxE1. The final model for BC included TWIST, RunX3 Gata 4 and age (AUC:.87 (.81−.92)). The final model for HG versus LG BC included TWIST, Fox E1, NID2 and age (AUC:.83 (.75−.90)). CONCLUSIONS A combination of five epigenetic markers (TWIST1, RunX3, FoxE1, Gata4, NID2) is a very promising non−invasive tool for sensitive and specific BC detection and prognostication. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e859 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Thomas Hermanns More articles by this author Ekaterina Olkhov-Mitsel More articles by this author Andrea Savio More articles by this author Bethany Gill More articles by this author Jenna Sykes More articles by this author Bimal Bhindi More articles by this author Tristan Juvet More articles by this author Cynthia Kuk More articles by this author Aidan Noon More articles by this author Ricardo Rendon More articles by this author David Waltregny More articles by this author Theodorus H. van der Kwast More articles by this author Antonio Finelli More articles by this author Neil E. Fleshner More articles by this author Kirk Lo More articles by this author Bharati Bapat More articles by this author Alexandre R. Zlotta 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)0,001
score de la tête « metaresearch » (Gemma)0,002
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,006

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 Opus0,049
Tête enseignante GPT0,303
Écart entre enseignants0,254 · 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
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueThe Journal of UrologyMême sujetBladder and Urothelial Cancer TreatmentsTravaux en français237 207