dc.contributor.author
Heijden, Antoine G. van der
dc.contributor.author
Mengual Brichs, Lourdes
dc.contributor.author
Ingelmo-Torres, Mercedes
dc.contributor.author
Lozano Salvatella, Juan José
dc.contributor.author
Rijt-van de Westerlo, Cindy C. M. van
dc.contributor.author
Baixauli, Montserrat
dc.contributor.author
Geavlete, Bogdan
dc.contributor.author
Moldoveanud, Cristian
dc.contributor.author
Ene, Cosmin
dc.contributor.author
Dinney, Colin P.
dc.contributor.author
Czerniak, Bogdan
dc.contributor.author
Schalken, Jack A.
dc.contributor.author
Kiemeney, Lambertus A. L. M.
dc.contributor.author
Ribal, María José
dc.contributor.author
Witjes, J. Alfred
dc.contributor.author
Alcaraz Asensio, Antonio
dc.date.issued
2019-06-07T13:45:03Z
dc.date.issued
2019-06-07T13:45:03Z
dc.date.issued
2018-05-30
dc.date.issued
2019-06-07T13:45:03Z
dc.identifier
https://hdl.handle.net/2445/134785
dc.description.abstract
Background: Current standard methods used to detect and monitor bladder cancer (BC) are invasive or have low sensitivity. This study aimed to develop a urine methylation biomarker classifier for BC monitoring and validate this classifier in patients in follow-up for bladder cancer (PFBC). Methods: Voided urine samples (N = 725) from BC patients, controls, and PFBC were prospectively collected in four centers. Finally, 626 urine samples were available for analysis. DNA was extracted from the urinary cells and bisulfite modificated, and methylation status was analyzed using pyrosequencing. Cytology was available from a subset of patients (N = 399). In the discovery phase, seven selected genes from the literature (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) were studied in 111 BC and 57 control samples. This training set was used to develop a gene classifier by logistic regression and was validated in 458 PFBC samples (173 with recurrence). Results: A three-gene methylation classifier containing CFTR, SALL3, and TWIST1 was developed in the training set (AUC 0.874). The classifier achieved an AUC of 0.741 in the validation series. Cytology results were available for 308 samples from the validation set. Cytology achieved AUC 0.696 whereas the classifier in this subset of patients reached an AUC 0.768. Combining the methylation classifier with cytology results achieved an AUC 0.86 in the validation set, with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively. Conclusions: The combination of the three-gene methylation classifier and cytology results has high sensitivity and high negative predictive value in a real clinical scenario (PFBC). The proposed classifier is a useful test for predicting BC recurrence and decrease the number of cystoscopies in the follow-up of BC patients. If only patients with a positive combined classifier result would be cystoscopied, 36% of all cystoscopies can be prevented.
dc.format
application/pdf
dc.publisher
BioMed Central
dc.relation
Reproducció del document publicat a: https://doi.org/10.1186/s13148-018-0496-x
dc.relation
Clinical Epigenetics, 2018, vol. 10, p. 71
dc.relation
https://doi.org/10.1186/s13148-018-0496-x
dc.rights
cc-by (c) Heijden, Antoine G. van der et al., 2018
dc.rights
http://creativecommons.org/licenses/by/3.0/es
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Biomedicina)
dc.subject
Càncer de bufeta
dc.subject
Expressió gènica
dc.subject
Bladder cancer
dc.subject
Gene expression
dc.title
Urine cell-based DNA methylation classifier for monitoring bladder cancer
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion