A stratified urine-based molecular diagnostic and prognostic model for non-muscle-invasive bladder cancer management.

backgroundNon-muscle-invasive bladder cancer (NMIBC) is characterized by a high recurrence rate requiring lifelong cystoscopic surveillance. Existing urine-based molecular assays mainly rely on mutations or methylation, which fail to capture large-scale genomic instability. Copy number variation (CNV) profiling offers complementary information on tumor evolution and aggressiveness, but its application in urinary diagnosis remains limited. We aimed to integrate CNV and DNA methylation signals from urinary DNA to establish a noninvasive and biologically informed stratified diagnostic model for NMIBC recurrence surveillance and risk stratification.

methodsUrine samples were prospectively collected from 91 patients (75 evaluable) between June 2021 and August 2023. Shallow whole-genome sequencing (sWGS) was used to detect CNVs at chromosomal arm and focal gene levels, while ONECUT2 promoter methylation was quantified by qPCR. Diagnostic and prognostic performance was evaluated by ROC analysis, Kaplan-Meier survival, and stratified recurrence assessment.

resultsWe evaluated a stratified diagnostic model combining CNV and ONECUT2 methylation testing in a cohort of 79 patients. CNV analysis alone showed high specificity (0.923) for NMIBC diagnosis. A combined model, using CNV as an initial screen followed by ONECUT2 methylation testing in CNV-positive cases, achieved a sensitivity of 0.783, specificity of 0.981, and a negative predictive value (NPV) of 0.911. This approach reduced the number of required ONECUT2 tests by 35% and identified a high proportion of true-negative patients (98.1%), which may help reduce unnecessary cystoscopy procedures. The model also demonstrated significant prognostic value, with the molecularly defined high-risk group showing significantly shorter recurrence-free survival (RFS) than the low-risk group (median RFS: 4.33 months vs. not reached; p < 0.001). Additional, in patients with initially negative cystoscopy after urine sample collection, the model demonstrated a predictive accuracy of 0.922 for recurrence, with molecular positivity observed a median of 9.6 months prior to clinical diagnosis.

conclusionsIntegrating CNV and DNA methylation profiling from urinary DNA provides a powerful and noninvasive molecular framework for NMIBC surveillance. By combining early epigenetic changes with genomic instability signals, this approach enhances recurrence risk assessment and enables earlier detection compared with conventional cystoscopy. It offers a practical route toward personalized and adaptive post-treatment monitoring of NMIBC.

trial registrationNCT04994197.