ArticleDiagnostics (Basel, Switzerland)2024
Hypermethylation Loci of ZNF671, IRF8, and OTX1 as Potential Urine-Based Predictive Biomarkers for Bladder Cancer.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- DNA Methylation in Bladder Cancer: Diagnostic and Therapeutic Perspectives-A Narrative Review.International journal of molecular sciences · 2025Review
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
- Recent advances on gene-related DNA methylation in cancer diagnosis, prognosis, and treatment: a clinical perspective.Clinical epigenetics · 2025Review
- DNA Methylation in Urine and Feces Indicative of Eight Major Human Cancer Types Globally.Life (Basel, Switzerland) · 2025Review
- Epigenetic Biomarkers as a New Diagnostic Tool in Bladder Cancer-From Early Detection to Prognosis.Journal of clinical medicine · 2024Review
- DNA methylation in human diseases.Heliyon · 2024Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings.
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Registered trials
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