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Background <p>Bladder cancer (BCa) remains the most common malignancy, and high-grade tumor patients have a significant risk of mortality. Importantly, the non-muscle-invasive BCa (NMIBC) has demonstrated a high recurrence in which an aberrant DNA methylation plays a critical role. Therefore, there is an urgent need for early detection and effective monitoring.</p> Methods <p>We integrated whole-genome methylation data from The Cancer Genome Atlas (TCGA) with clinical metadata from the MIMIC-IV database to identify methylated biomarkers for NMIBC. Functional similarity between 114 specific disease genes with differential methylation was performed using Gene Ontology. Graph-based metrics evaluated gene significance in biological pathways. Genes were then clustered using agglomerative hierarchical clustering, and one representative from each cluster was selected. The candidate panel was validated on a clinical cohort of 125 urine samples (43 BCa, 82 controls) collected from three hospitals in Taiwan.</p> Results <p>A logistic regression model used two methylated biomarkers (GALR1 and ZNF154) in urine samples as the final predictive panel. qPCR-based urine methylation detection of selected biomarker panel revealed the average sensitivity and specificity of 76.74 and 81.71%, respectively, based on out-of-fold (OOF) predictions derived from repeated 10 × 5-fold stratified cross-validation. Our logistic regression analysis revealed prominent methylation levels of two genes, GALR1 and ZNF154, suggesting their potential diagnostic utility. This qPCR-based approach offers comparable accuracy while remaining far more cost-effective and operationally simple than urine cytology, UroVysion FISH, and commercial multi-marker assays.</p> Conclusion <p>Our study suggests that GALR1 and ZNF154 may serve as urine-based methylated biomarkers for early detection of non-invasive BCa, which may be validated across datasets and diverse clinical cohorts.</p>

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Integrative Profiling of Differentially Methylated Genomic Biomarkers in Bladder Cancer: Validation in Taiwanese Clinical Cohort

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Jhen-Li Huang,
  • Cing-Han Yang,
  • Huan-Zhi Lin,
  • Yi-Hsuan Tsai,
  • Tun-Wen Pai,
  • Ming-Che Liu

摘要

Background

Bladder cancer (BCa) remains the most common malignancy, and high-grade tumor patients have a significant risk of mortality. Importantly, the non-muscle-invasive BCa (NMIBC) has demonstrated a high recurrence in which an aberrant DNA methylation plays a critical role. Therefore, there is an urgent need for early detection and effective monitoring.

Methods

We integrated whole-genome methylation data from The Cancer Genome Atlas (TCGA) with clinical metadata from the MIMIC-IV database to identify methylated biomarkers for NMIBC. Functional similarity between 114 specific disease genes with differential methylation was performed using Gene Ontology. Graph-based metrics evaluated gene significance in biological pathways. Genes were then clustered using agglomerative hierarchical clustering, and one representative from each cluster was selected. The candidate panel was validated on a clinical cohort of 125 urine samples (43 BCa, 82 controls) collected from three hospitals in Taiwan.

Results

A logistic regression model used two methylated biomarkers (GALR1 and ZNF154) in urine samples as the final predictive panel. qPCR-based urine methylation detection of selected biomarker panel revealed the average sensitivity and specificity of 76.74 and 81.71%, respectively, based on out-of-fold (OOF) predictions derived from repeated 10 × 5-fold stratified cross-validation. Our logistic regression analysis revealed prominent methylation levels of two genes, GALR1 and ZNF154, suggesting their potential diagnostic utility. This qPCR-based approach offers comparable accuracy while remaining far more cost-effective and operationally simple than urine cytology, UroVysion FISH, and commercial multi-marker assays.

Conclusion

Our study suggests that GALR1 and ZNF154 may serve as urine-based methylated biomarkers for early detection of non-invasive BCa, which may be validated across datasets and diverse clinical cohorts.