
DNA methylation markers could serve as useful biomarkers, both as markers for progression and for urine-based diagnostic assays.Identify bladder cancer (BCa)-specific methylated DNA sequences for predicting pTa-specific progression and detecting BCa in voided urine.Genome-wide methylation analysis was performed on 44 bladder tumours using the Agilent 244K Human CpG Island Microarray (Agilent Technologies, Santa Clara, CA, USA). Validation was done using a custom Illumina 384-plex assay (Illumina, San Diego, CA, USA) in a retrospective group of 77 independent tumours. Markers for progression were identified in pTa (n = 24) tumours and validated retrospectively in an independent series of 41 pTa tumours by the SNaPshot method (Applied Biosystems, Foster City, CA, USA).The percentage of methylation in tumour and urine samples was used to identify markers for detection and related to the end point of progression to muscle-invasive disease with Kaplan-Meier models and multivariate analysis.In the validation set, methylation of the T-box 2 (TBX2), T-box 3 (TBX3), GATA binding protein 2 (GATA2), and Zic family member 4 (ZIC4) genes was associated with progression to muscle-invasive disease in pTa tumours (p = 0.003). Methylation of TBX2 alone showed a sensitivity of 100%, a specificity of 80%, a positive predictive value of 78%, and a negative predictive value of 100%, with an area under the curve of 0.96 (p<0.0001) for predicting progression. Multivariate analysis showed that methylation of TBX3 and GATA2 are independent predictors of progression when compared to clinicopathologic variables (p = 0.04 and p = 0.03, respectively). The predictive accuracy improved by 23% by adding methylation of TBX2, TBX3, and GATA2 to the European Organisation for Research and Treatment of Cancer risk scores. We further identified and validated 110 CpG islands (CGIs) that are differentially methylated between tumour cells and control urine. The limitation of this study is the small number of patients analysed for testing and validating the prognostic markers.We have identified four methylation markers that predict progression in pTa tumours, thereby allowing stratification of patients for personalised follow-up. In addition, we identified CGIs that will enable detection of bladder tumours in voided urine.
Male, Nerve Tissue Proteins, Kaplan-Meier Estimate, Disease-Free Survival, SDG 3 - Good Health and Well-being, EMC MM-03-49-01, Biomarkers, Tumor, Humans, Genetic Predisposition to Disease, Aged, Neoplasm Staging, Netherlands, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, EMC MM-02-54-03, EMC MM-03-24-01, DNA Methylation, Middle Aged, ONCOL 5: Aetiology, screening and detection, GATA2 Transcription Factor, Multivariate Analysis, Disease Progression, CpG Islands, Female, Genome-Wide Association Study
Male, Nerve Tissue Proteins, Kaplan-Meier Estimate, Disease-Free Survival, SDG 3 - Good Health and Well-being, EMC MM-03-49-01, Biomarkers, Tumor, Humans, Genetic Predisposition to Disease, Aged, Neoplasm Staging, Netherlands, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, EMC MM-02-54-03, EMC MM-03-24-01, DNA Methylation, Middle Aged, ONCOL 5: Aetiology, screening and detection, GATA2 Transcription Factor, Multivariate Analysis, Disease Progression, CpG Islands, Female, Genome-Wide Association Study
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 95 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
