UroCAD for Detecting Residual Tumor and Predicting Recurrence-Free Survival in Bladder Cancer Patients Post-TURBT

UroCAD for Detecting Residual Tumor and Predicting Recurrence-Free Survival in Bladder Cancer Patients Post-TURBT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article UroCAD for Detecting Residual Tumor and Predicting Recurrence-Free Survival in Bladder Cancer Patients Post-TURBT Wei Chen, Wenhua Xie, Jing Jin, Yifang Cao, Li Guo, Linfeng Lu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9223803/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Transurethral resection of bladder tumor (TURBT) is the standard treatment for non-muscle-invasive bladder cancer (NMIBC), but the high rates of residual tumor and recurrence remain major clinical challenges. This study aimed to evaluate the clinical utility of UroCAD urine test for detecting residual tumor after TURBT and predicting recurrence in high-grade bladder cancer. Methods In this prospective study, 38 patients with high-grade bladder cancer were divided into a single TURBT group (n = 23, followed ≥ 6 months for recurrence) and a second TURBT group (n = 15, repeat resection at 4–6 weeks). Urine samples were collected 7–14 days post-initial TURBT for UroCAD-based chromosomal instability (CIN) analysis. Diagnostic performance was assessed against pathology or recurrence. Results In the single TURBT group, UroCAD showed 80% sensitivity, 88% specificity, area under the ROC curve (AUC) 0.84, negative predictive value (NPV) 88%, and 85% accuracy for predicting recurrence. In the second TURBT group, sensitivity for residual tumor was 0%, specificity 92%, AUC 0.46, NPV 86%, and accuracy 80%. Marked CIN in chromosomes 1,3,7,17 was observed in the single TURBT group and pre-second TURBT subgroup, but significantly decreased after second TURBT. Recurrent copy number variations (CNVs) (5p,20q,10p,12q) were identified in the single TURBT group. Maximum CIN Z-values post-second TURBT were significantly lower than those in the single TURBT group (P < 0.05). Conclusion UroCAD-based CIN detection is a promising non-invasive tool for predicting recurrence in high-grade bladder cancer patients after single TURBT and monitoring treatment response. However, the extremely low sensitivity of UroCAD in detecting residual tumors before second TURBT requires further optimization through larger-scale studies. UroCAD bladder cancer TURBT chromosomal instability recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Bladder cancer ranks among the most prevalent urological malignancies globally, with non-muscle-invasive bladder cancer (NMIBC) accounting for approximately 75% of newly diagnosed cases [ 1 ]. Transurethral resection of bladder tumor (TURBT) remains the first-line treatment for NMIBC due to its minimal invasiveness, low perioperative bleeding risk, and rapid postoperative recovery [ 1 ]. Despite these advantages, TURBT is plagued by substantial clinical challenges: residual tumor rates range from 20% to 40%, and the 5-year recurrence rate can reach up to 70%—particularly in high-grade tumors—significantly compromising patient prognosis and quality of life [ 1 , 2 ]. Major urological guidelines, including those from the European Association of Urology (EAU), consistently recommend second TURBT for high-risk NMIBC patients, such as those with inadequate initial TURBT, absence of muscularis propria in resected specimens, T1 stage, or high-grade (G3) tumors (excluding pure carcinoma in situ) [ 1 ]. However, current clinical decision-making for second TURBT relies heavily on conventional tools like cystoscopy and pathological assessment, which lack sufficient sensitivity to identify subclinical residual tumor. This limitation often leads to either over-treatment (unnecessary second TURBT) or under-treatment (missed residual tumor) [ 1 , 3 ]. Thus, there is an urgent need for a non-invasive, high-sensitivity diagnostic tool to optimize patient selection for second TURBT. Chromosomal instability (CIN)—defined as persistent errors in chromosome segregation during mitosis—is a fundamental hallmark of cancer and a key driver of tumor initiation and progression [ 2 ]. Genomic instability, including CIN, generates the genetic diversity required for tumor evolution, enabling the acquisition of cancer hallmarks such as unlimited replicative potential and invasion [ 2 ]. In bladder cancer, CIN has been linked to aggressive phenotypes: for example, tumors with high CIN exhibit higher rates of progression to muscle-invasive bladder cancer (MIBC) and worse survival outcomes [ 2 ]. Additionally, defects in DNA repair pathways (e.g., ERCC2 mutations) are closely associated with CIN and have been shown to modulate treatment response in bladder cancer, highlighting CIN’s clinical relevance as a prognostic and predictive biomarker [ 4 ]. In recent years, liquid biopsy-based CIN detection has emerged as a promising non-invasive alternative to tissue biopsy, particularly for urinary tract malignancies. Urine-derived biomarkers, in particular, offer unique advantages for bladder cancer monitoring due to their direct proximity to the tumor microenvironment. For instance, Christensen et al. (2023) demonstrated that mutational analysis of urine cell-free DNA (cfDNA) and plasma DNA could predict neoadjuvant chemotherapy (NAC) response and recurrence-free survival (RFS) in MIBC patients, with urine samples showing higher tumor DNA detection rates (85–89%) than plasma (43%) [ 3 ]. Similarly, a cost-effective low-coverage whole-genome sequencing (LC-WGS) assay for urine-exfoliated cells achieved 84.6% sensitivity and 97.9% specificity for urothelial carcinoma detection, outperforming conventional urine cytology (51.2% sensitivity) [ 5 ]. This assay, analogous to UroCAD, leverages LC-WGS to assess genomic alterations, providing a rationale for its application in residual tumor detection post-TURBT. UroCAD, a LC-WGS-based assay targeting urine-exfoliated cells, was developed to quantify CIN by analyzing copy number variations (CNVs) [ 5 ]. Previous studies have validated its utility in urothelial carcinoma detection, but its performance in predicting residual tumor and guiding second TURBT in high-grade NMIBC patients remains unconfirmed [ 5 , 6 ]. The present study aimed to fill this gap by evaluating UroCAD’s ability to detect residual tumor post-first TURBT, predict recurrence in single TURBT patients, and monitor treatment response after second TURBT. By analyzing CIN patterns in urine samples, we sought to determine whether UroCAD could improve personalized decision-making for high-grade bladder cancer patients post-TURBT. 2. Materials and Methods 2.1. Patient Recruitment and Ethical Statement This prospective study enrolled 38 patients with pathologically confirmed urothelial carcinoma at our institution between November 2020 and December 2023. This study was approved by the ethics committee and informed consent was waived. Patients were eligible if they: (1) had high-grade bladder cancer (G3); (2) underwent TURBT; (3) had no prior history of chemotherapy, radiotherapy, or immunotherapy; and (4) provided written informed consent. Exclusion criteria included: (1) muscle-invasive or metastatic bladder cancer; (2) concurrent other malignancies; (3) severe renal/hepatic dysfunction; and (4) inability to provide urine samples. Patients were divided into two groups: Single TURBT group (n = 23): Patients who underwent a single TURBT and were followed up for ≥ 6 months to monitor recurrence. Second TURBT group (n = 15): Patients who underwent second TURBT due to high-risk features (per guidelines) within 4–6 weeks of the first TURBT. 2.2. Sample Collection and Processing Urine samples (10–50 mL) were collected at 7–14 days post-first TURBT (before second TURBT for the second TURBT group) using a Cell Preservation Solution Kit (Macro Yuan Biotechnology, Suzhou, China) to prevent cell degradation. Samples were transported at room temperature to the laboratory within 72 hours. Urine sediments were isolated by centrifugation (3000×g for 10 minutes). Genomic DNA was extracted from sediments using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA concentration and quality were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis. 2.3. Low-Coverage Whole-Genome Sequencing (LC-WGS) and UroCAD Analysis LC-WGS libraries were constructed using the Kapa Hyper Prep Kit (Roche, Basel, Switzerland) with custom-designed adapters (Integrated DNA Technologies, Coralville, IA, USA). DNA input ranged from 50 to 1000 ng (median: 471 ng). A total of 22 libraries were pooled and sequenced on an Illumina HiSeq X10 platform (Illumina, San Diego, CA, USA) with 150-base paired-end reads, generating ≥ 10 million paired reads per sample. Sequencing data were processed using the UroCAD workflow (Macro Yuan Biotechnology): Reads were aligned to the human reference genome hg19 using BWA-MEM [ 7 ]. Genomic coverage was calculated in 200 kb bins using SAMtools mpileup [ 8 ]. Z-scores for each bin were normalized using the formula: $$\:{Z}_{bin}={coverage}_{normalized}=\frac{{coverage}_{raw}-mean\left({coverage}_{controls,\:raw}\right)}{stdev\left({coverage}_{controls,raw}\right)}$$ Z bin Standardized Z-score for a specific genomic bin; coverage raw Raw coverage value of the bin under investigation; coverage controls, raw Raw coverage values from control samples; mean(coverage controls, raw ) Mean raw coverage value across control samples; stdev(coverage controls, raw ) Standard deviation of raw coverage values in control samples. Significant CNVs and genomic breakpoints were identified using the circular binary segmentation algorithm in the R package DNACopy [ 9 ]. Samples with a median absolute deviation (MAD) of copy ratios > 0.38 (indicating low-quality data) were excluded. CIN positivity was defined as the presence of ≥ 1 significant CNV (P < 0.05 via DNACopy). 2.4. Clinical Outcome Assessment For the single TURBT group: The gold standard was recurrence (confirmed by cystoscopy and pathology) during ≥ 6 months of follow-up. For the second TURBT group: The gold standard was residual tumor detected in the second TURBT pathological specimen. 2.5. Statistical Analysis Statistical analyses were performed using SPSS 17.0 (IBM, Armonk, NY, USA) and R 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were compared using the chi-square test. Sensitivity, specificity, PPV, NPV, and AUC were calculated via ROC curve analysis. Hierarchical clustering and heatmaps were generated to visualize CNV patterns. Box plots of maximum CIN Z-values (highest absolute Z-value per sample) were compared using one-way ANOVA with post-hoc Tukey’s test. P < 0.05 was considered statistically significant. 3. Results 3.1. Study Design and Patient Baseline Characteristics The STARD (Standards for the Reporting of Diagnostic Accuracy Studies) flowchart for patient recruitment is shown in Fig. 1 . A total of 38 patients were enrolled, with 23 in the single TURBT group and 15 in the second TURBT group. Baseline characteristics are summarized in Table 1 . The mean age of patients was 69 years (range: 38-86years), with 29 (76.3%) patients aged ≥ 60 years. Males accounted for 34 (89.5%) patients, consistent with the male predominance of bladder cancer. There were no significant differences between groups in age (P = 1.000), sex (P = 1.000), smoking history (P = 1.000), hypertension (P = 1.000), diabetes (P = 1.000), or tumor size (P = 0.3973). Missing data were excluded from analyses (e.g., smoking history: n = 13 missing). Table 1 Baseline characteristics Patients (n = 38) Single TURBT Second TURBT P value Age (NA = 1) 1 ≥ 60 years 29 17 12 < 60 years 8 5 3 Sex (NA = 1) 1 Male 34 20 14 Female 3 2 1 Smoking History (NA = 13) 1 No 15 9 6 Yes 10 6 4 Hypertension (NA = 13) 1 No 11 7 4 Yes 14 8 6 Diabetes (NA = 13) 1 No 21 13 8 Yes 4 2 2 Tumour size (NA = 13) 0.3973 ≥ 30 mm 16 11 5 < 30 mm 9 4 5 Table 1 Baseline characteristics of patients. Data are presented as n (%). NA, not available. P-values were calculated using the chi-square test. 3.2. Diagnostic Performance of UroCAD for CIN Detection Table 2 presents the diagnostic performance of UroCAD for residual tumor/recurrence prediction. Table 2 Analysis: Diagnostic Performance of CIN in Single and Second TURBT Single TURBT NA TP TN FP FN NPV PPV Sensitivity (95% CI) Specificity (95% CI) AUC (95% CI) Accuracy 10 4 7 1 1 0.88 0.8 0.8 (0.28–0.99) 0.88 (0.65-1) 0.84 (0.61-1) 0.85 Second session of second TURBT 0 0 12 1 2 0.86 0 0 (0-0.84) 0.92 (0.78–1.07) 0.46 (0.39–0.54) 0.8 Total 10 4 19 2 3 0.86 0.67 0.57 (0.18–0.9) 0.9 (0.78–1.03) 0.74 (0.53–0.95) 0.82 Table 2 Diagnostic performance of UroCAD for CIN detection. TP, true positive; TN, true negative; FP, false positive; FN, false negative; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the ROC curve; CI, confidence interval. Single TURBT group (n = 23, 10 missing data): Among 13 evaluable patients, there were 4 true positives (TP), 7 true negatives (TN), 1 false positive (FP), and 1 false negative (FN). Sensitivity was 0.80 (95% CI: 0.28–0.99), specificity 0.88 (95% CI: 0.65–1.00), PPV 0.80, NPV 0.88, AUC 0.84 (95% CI: 0.61–1.00), and accuracy 0.85. Second TURBT group (n = 15, 0 missing data): There were 0 TP, 12 TN, 1 FP, and 2 FN. Sensitivity was 0.00 (95% CI: 0.00–0.84), specificity 0.92 (95% CI: 0.78–1.07), PPV 0.00, NPV 0.86, AUC 0.46 (95% CI: 0.39–0.54), and accuracy 0.80. Total cohort (n = 38, 10 missing data): Combined results showed sensitivity 0.57 (95% CI: 0.18–0.90), specificity 0.90 (95% CI: 0.78–1.03), PPV 0.67, NPV 0.86, AUC 0.74 (95% CI: 0.53–0.95), and accuracy 0.82. 3.3. Chromosomal Instability Profiles Across TURBT Sessions 3.3.1. Scatter Plots of Chromosomal Alterations (Fig. 2 ) Scatter plots depict CNVs across chromosomes in the three groups (Single TURBT, First session of re-TURBT, Second session of re-TURBT). In the Single TURBT and First session of re-TURBT groups, prominent CIN was observed in chromosomes 1, 3, 7, and 17—regions previously associated with bladder cancer progression (e.g., chr3p deletion, chr7 gain) [ 10 ]. In contrast, the Second session of re-TURBT group showed a marked reduction in CNVs across all chromosomes, indicating decreased genomic instability after repeated resection. 3.3.2. Heatmap of Recurrent CNVs (Fig. 3 ) The heatmap illustrates recurrent CNVs across clinical subgroups. The Single TURBT group exhibited significant CNVs in specific chromosomal regions, including 5p amplification, 20q amplification, 10p deletion, 12q amplification, 8p deletion, 14q deletion, and 10q deletion. These regions harbor genes critical for cancer progression (e.g., 5p: TERT, 20q: AURKA), suggesting their potential role in bladder cancer recurrence [ 11 ]. 3.3.3. Box Plot of Maximum CIN Z-Values (Fig. 4 ) Maximum CIN Z-values (reflecting the most severe genomic alteration per sample) were compared across groups. The Single TURBT group had the highest median Z-value, with outliers indicating extreme genomic instability. The First session of re-TURBT group showed a modest reduction in median Z-value (not statistically significant vs. Single TURBT). No significant difference was observed between the First and Second sessions of re-TURBT. 4. Discussion This study evaluated the utility of UroCAD—a non-invasive LC-WGS-based assay for CIN detection in urine-exfoliated cells—for predicting residual tumor and guiding second TURBT in high-grade bladder cancer patients. Our key findings are threefold: (1) UroCAD exhibits high specificity (0.88) and negative predictive value (NPV, 0.88) for recurrence prediction after single TURBT; (2) After the second transurethral resection of bladder tumor (TURBT), the CIN levels are not significantly reduced compared to those after the first TURBT, but show an overall downward trend, which may be correlated with the improvement of treatment response. ; (3) specific chromosomal regions (chr1, 3, 7, 17, 5p, 20q) are frequently altered in high-risk tumors, potentially serving as complementary prognostic biomarkers. Below, we discuss these findings in the context of existing literature and their clinical implications. 4.1. UroCAD as a Predictor of Recurrence After Single TURBT The high specificity and NPV of UroCAD in the single TURBT group (0.88 each) suggest that CIN-negative patients are at low risk of recurrence and may not require intensive follow-up or second TURBT. This aligns with recent evidence that urine-derived genomic biomarkers can reliably stratify recurrence risk in bladder cancer. For example, Christensen et al. (2023) showed that urine tumor DNA (utDNA) clearance during NAC was associated with a 0% recurrence rate in MIBC patients, highlighting the prognostic value of urine-based genomic assays [ 3 ]. Similarly, urine cfDNA multi-omics analysis has been shown to detect minimal residual disease (MRD) and predict RFS in bladder cancer, with NPVs exceeding 85% [ 6 ]. The performance of UroCAD in our study is consistent with these reports, reinforcing its potential as a non-invasive tool for identifying low-risk patients post-single TURBT. The biological basis for UroCAD’s prognostic utility lies in the role of CIN as a driver of tumor evolution. Genomic instability, including CIN, generates the genetic diversity required for tumor regrowth and progression [ 2 ]. In early-stage bladder cancer, tumors with high CIN often harbor alterations in cell cycle regulators (e.g., TP53, RB1) and DNA repair genes (e.g., ERCC2), which promote aggressive behavior [ 2 , 4 ]. UroCAD captures these alterations via LC-WGS, enabling it to distinguish tumors with inherent recurrence potential from those successfully cleared by TURBT. This is supported by a study showing that low-coverage WGS of urine-exfoliated cells can detect CNVs associated with bladder cancer recurrence, including gains in chr7 and losses in chr9 [ 5 ]—alterations we also observed in the single TURBT group. 4.2. CIN Reduction After Second TURBT: Implications for Treatment Response Monitoring Our data show reduction in CIN (via scatter plots and maximum CIN Z-values) after second TURBT, indicating that repeated resection effectively removes genomically unstable tumor clones. This finding aligns with genomic studies of bladder cancer showing that treatment-induced reduction in genomic instability correlates with improved outcomes [ 2 ]. For example, in MIBC, NAC-induced decreases in CIN are associated with pathological complete response and longer OS [ 3 , 4 ]. In our study, the reduction in CNVs (particularly in chr1, 3, 7, 17) after second TURBT suggests that UroCAD could be used to monitor treatment response—an unmet need in current clinical practice. However, UroCAD’s low sensitivity (0.00) for detecting pre-second TURBT residual tumor is a critical limitation. Several factors may explain this: (1) Residual tumor burden after first TURBT may be too low to be detected by LC-WGS, as urine-based assays often require a minimum tumor cell fraction (typically > 0.1%) for reliable CNV calling [ 5 , 6 ]; (2) The current CIN positivity threshold (≥ 1 significant CNV) may be too strict for minimal residual disease, where only small subclones persist; (3) Residual tumor cells may exhibit reduced CIN due to clonal selection, as observed in MIBC after NAC [ 4 ]. To address this, future studies could optimize UroCAD’s workflow—for example, lowering the sequencing depth to improve sensitivity [ 5 ] or integrating mutational analysis of driver genes (e.g., FGFR3, PIK3CA) [ 2 , 12 ] to enhance detection of minimal residual disease. Recent efforts to explore novel genomic biomarkers for NAC response and survival in MIBC also highlight the value of multi-biomarker panels, which could be adapted to UroCAD’s framework [ 7 ]. 4.3. Clinical Significance of Specific Chromosomal Alterations Our study identified recurrent CNVs in chr1, 3, 7, 17, 5p, and 20q—regions with well-documented roles in bladder cancer progression. These alterations are consistent with genomic profiles of high-grade NMIBC reported in Asian cohorts, where FGFR3 mutations (chr4p16.3), TP53 deletions (chr17p13), and TERT amplifications (chr5p15.33) are frequent drivers of recurrence [ 12 ]. For example: chr3p deletion: Loss of tumor suppressor genes (e.g., FHIT) in this region is associated with early bladder cancer development and recurrence [ 2 ]; chr7 gain: Amplification of EGFR (chr7p12) promotes cell proliferation and resistance to therapy, making it a marker of aggressive disease [ 2 ]; 5p amplification: TERT activation extends telomere length, enabling unlimited replicative potential—a key hallmark of recurrent tumors [ 2 , 12 ]; 20q amplification: AURKA (chr20q13.2) overexpression enhances mitotic errors, further driving CIN and progression []. These alterations could serve as complementary biomarkers to CIN, improving UroCAD’s sensitivity for residual tumor detection. For instance, combining CIN detection with TERT amplification (5p) or EGFR gain (chr7) could enhance sensitivity in the second TURBT group, as these mutations are often present in minimal residual disease [ 6 , 12 ]. This is supported by a study showing that multi-biomarker panels (including CNVs and mutations) improve the detection of MRD in urine samples [ 10 ]. Additionally, letter reports on the clinical utility of urine DNA for MRD monitoring in urothelial carcinoma further validate the potential of urine-based multi-marker approaches [ 9 ]. 4.4. Tumor Heterogeneity and Liquid Biopsy Limitations A key challenge in bladder cancer biomarker development is tumor heterogeneity, which can lead to false-negative results in liquid biopsies [ 11 ]. Bladder tumors often harbor multiple subclones with distinct genomic profiles, and residual disease after TURBT may consist of rare subclones not captured by UroCAD’s current CNV-focused approach. For example, genomic studies of MIBC arising after prostate radiotherapy have shown unique mutational signatures (e.g., increased APOBEC activity) that may not be detected by standard LC-WGS [ 8 ]. To address this, future iterations of UroCAD could integrate targeted sequencing of heterogeneous subclones or single-cell genomic analysis, as proposed in studies of tumor heterogeneity in bladder cancer [ 11 ]. 4.5. Strengths and Limitations Strengths Non-invasive approach: UroCAD uses urine samples, reducing patient burden compared to invasive procedures like cystoscopy or tissue biopsy [ 3 , 5 , 6 ]. Technical feasibility: LC-WGS is cost-effective and scalable, with recent studies showing that it can be implemented in clinical laboratories for urine-based testing [ 5 ]. Alignment with guidelines: The study focuses on high-grade patients, a population for whom second TURBT is guideline-recommended but selection remains suboptimal [ 1 ]. Limitations Small sample size and single-center design: The cohort (n = 38) is smaller than those in multi-center studies of urine biomarkers (n > 100) [ 3 , 6 ], limiting generalizability. Future validation in larger, diverse cohorts—including Asian patients with well-characterized genomic profiles [ 12 ]—is needed. Missing data: Loss to follow-up (n = 10) and incomplete clinical data (e.g., smoking history) could introduce bias, as observed in other bladder cancer biomarker studies [ 2 , 3 ]. 4.6. Future Directions To address these limitations and advance UroCAD’s clinical utility, future studies should: Enroll larger, multi-center cohorts: Validate UroCAD’s performance in diverse populations, including Asian patients with genomic profiles distinct from Western cohorts [ 12 ], and integrate findings from studies exploring novel genomic biomarkers [ 7 ]. Optimize sensitivity for minimal residual disease: Adjust the CIN positivity threshold (e.g., lower to 0.5 significant CNVs) or integrate mutational analysis of driver genes (e.g., FGFR3, ERCC2) [ 2 , 4 , 12 ] to detect low-burden residual tumor, leveraging insights from urine cfDNA multi-omics [ 6 ]. Combine with other biomarkers: Explore panels of CIN plus urine cfDNA methylation or protein markers (e.g., NMP22), as multi-omics approaches have shown improved sensitivity for MRD detection [ 6 , 13 ]. Account for tumor heterogeneity: Integrate single-cell sequencing or targeted subclone analysis to capture rare residual tumor cells, addressing challenges highlighted in studies of bladder cancer heterogeneity [ 11 ]. Evaluate long-term outcomes: Assess UroCAD’s ability to predict 5-year recurrence-free survival, a key endpoint in bladder cancer guidelines [ 1 ], and correlate with genomic features of recurrent tumors [ 13 ]. 5. Conclusions UroCAD-based CIN detection is a promising non-invasive tool for predicting recurrence in high-grade bladder cancer patients after single TURBT, with high specificity and NPV. The reduction in CIN after second TURBT supports its role in monitoring treatment response. However, its low sensitivity for pre-second TURBT residual tumor requires optimization—potentially via workflow adjustments, multi-biomarker integration, or accounting for tumor heterogeneity [ 11 ]. With larger-scale validation (including diverse cohorts [ 12 ]) and technical improvements (e.g., integration of novel biomarkers [ 7 ]), UroCAD could become a key tool for personalized second TURBT decision-making, aligning with EAU guidelines [ 1 ] and improving outcomes for high-grade NMIBC patients. Abbreviations TURBT transurethral resection of bladder tumor CIN chromosomal instability CNVs copy number variations NMIBC non-muscle-invasive bladder cancer EAU European Association of Urology MIBC muscle-invasive bladder cancer NAC neoadjuvant chemotherapy RFS recurrence-free survival LC-WGS low-coverage whole-genome sequencing PPV positive predictive value NPV negative predictive value AUC area under the ROC curve MRD minimal residual disease Declarations Funding This study was supported by Jiaxing Medical Key Subject Funding of Zhejiang Province (2023-zc-013) and Jiaxing Key Laboratory of Precise Diagnosis and Treatment of Urological Tumor (2020-mnzdsys). Acknowledgements None. Authors’ contributions Wei Chen, Wenhua Xie, Li Guo, Linfeng Lu and Siyu Lei collected the data. Yanqin Gu, Wei Zhu and Yi He supervised; Wei Chen and Wenhua Xie wrote the main manuscript text. Jing Jin, Yifang Cao and Yi He reviewed and edit the manuscript. Wei Chen and Wenhua Xie contributed equally to the study. All authors have read and agreed to the published version of the manuscript. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study protocol was approved by the ethics committee of Affiliated Hospital of Jiaxing University and informed consent was obtained from all individual participants included in the study. The study was performed under the principles of the Declaration of Helsinki. Competing interests The authors declare no competing interests. References Babjuk M, Burger M, Compérat E, et al. European Association of Urology guidelines on non-muscle-invasive bladder cancer (Ta, T1, and carcinoma in situ). Eur Urol. 2021;79(6):778–800. Prip F, Lamy P, Lindskrog SV, et al. Comprehensive genomic characterization of early-stage bladder cancer. Nat Genet. 2025;57(1):115–25. 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Comprehensive Collection of Whole-Slide Images and Genomic Profiles for Patients with Bladder Cancer. Sci Data. 2024;11(1):699. Published 2024 Jun 27. Shi ZD, Han XX, Song ZJ, et al. Integrative multi-omics analysis depicts the methylome and hydroxymethylome in recurrent bladder cancers and identifies biomarkers for predicting PD-L1 expression. Biomark Res. 2023;11(1):47. Published 2023 May 3. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9223803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623124959,"identity":"e31a4dc8-be87-4762-878a-b64ee007d5d8","order_by":0,"name":"Wei Chen","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":623124960,"identity":"731ad00c-48b5-41bf-9171-0f2e096a2c7c","order_by":1,"name":"Wenhua Xie","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Wenhua","middleName":"","lastName":"Xie","suffix":""},{"id":623124961,"identity":"1bc3ba56-e781-4bc8-b98c-ad0042020052","order_by":2,"name":"Jing Jin","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jin","suffix":""},{"id":623124963,"identity":"7586f245-5d48-491c-949d-198fb0f1ecae","order_by":3,"name":"Yifang Cao","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Yifang","middleName":"","lastName":"Cao","suffix":""},{"id":623124965,"identity":"4cdb653c-ec51-4c96-8e10-64022e5938c3","order_by":4,"name":"Li Guo","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Guo","suffix":""},{"id":623124966,"identity":"0eaada41-9c68-48a5-aef7-510de87fbad2","order_by":5,"name":"Linfeng Lu","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Linfeng","middleName":"","lastName":"Lu","suffix":""},{"id":623124967,"identity":"e1fb1f78-3278-4d16-9b30-b15a4583fdba","order_by":6,"name":"Yanqin Gu","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Yanqin","middleName":"","lastName":"Gu","suffix":""},{"id":623124969,"identity":"0c034a86-70ef-426f-a83a-f558423e7d31","order_by":7,"name":"Siyu Lei","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Lei","suffix":""},{"id":623124970,"identity":"49ade28d-1ca3-497a-846e-40511227ba25","order_by":8,"name":"Wei Zhu","email":"","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhu","suffix":""},{"id":623124971,"identity":"ae7c5789-1adb-4dac-963a-2b05175b6201","order_by":9,"name":"Yi He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAyA+8IGBDcyRIFrLwRkka2HmgXKI02LO3mN42OYPX+KGA8wHb/Mw2OUR1GLZc8bgcA4Pm7HBAbZkax6G5GLCDruRA9QiwSZncIDHTJqH4UBiA0Et998YHLYwYOMxOMD/jUgtN3gMDjMkgG1hI1LLmbSCgz0H2IwlD7MZW84xSCZCy/HDmz/8+HMsse9488MbbyrsCGthYOAARc0xYOyATSCsHgjYHwCJGqKUjoJRMApGwQgFAMGMOe277AGYAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Jiaxing University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-03-25 13:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9223803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9223803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616363,"identity":"363ac920-206c-4c1d-84a5-a442b885d463","added_by":"auto","created_at":"2026-04-23 09:13:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34738,"visible":true,"origin":"","legend":"\u003cp\u003eDescribes the study design for patients with urothelial carcinoma (N=38), focusing on the collection of urine samples after transurethral resection of bladder tumor (TURBT) and its role in diagnosis and genetic analysis. The study was divided into two groups: (1) Single TURBT group (N=23). These patients underwent a single TURBT procedure and postoperative urine samples were collected directly after the procedure. (2) Second TURBT group (N=15). These patients still need a second TURBT after the first TURBT. The study collected urine samples after the first TURBT and before the second TURBT to evaluate the pathological value of the second TURBT and the changes in CIN (chromosomal instability).\u003c/p\u003e","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9223803/v1/a0656f451b7e316260df14d5.png"},{"id":107616409,"identity":"b6aa19cf-08d7-4fc0-b97b-2aad948975f4","added_by":"auto","created_at":"2026-04-23 09:13:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228294,"visible":true,"origin":"","legend":"\u003cp\u003eChromosomal Instability (CIN) Profiles Across TURBT Sessions. Scatter plots depict chromosomal alterations in Single TURBT, First re-TURBT, and Second re-TURBT sessions.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9223803/v1/c5c5478f90b64fc976692818.png"},{"id":107616264,"identity":"fc077a3d-8598-4c1a-b936-80f698af0018","added_by":"auto","created_at":"2026-04-23 09:13:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23066,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of recurrent copy number variations (CNVs) across TURBT stages. Rows represent chromosomal regions; columns represent patients. Red indicates amplification; blue indicates deletion. The Single TURBT group shows significant recurrent CNVs in 5p, 20q, 10p, 12q, 8p, 14q, and 10q.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9223803/v1/39614959fc5ef4d4e5e47cbc.png"},{"id":107616407,"identity":"190392ff-8b50-4d97-96da-328072ea2577","added_by":"auto","created_at":"2026-04-23 09:13:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10228,"visible":true,"origin":"","legend":"\u003cp\u003eBox Plot of Maximum CIN Z-values Across TURBT Sessions. This box plot compares the maximum absolute Z-values of chromosomal instability (CIN) among three groups: Single TURBT, First session of re-TURBT, and Second session of re-TURBT. The Y-axis represents the highest absolute Z-value detected across all chromosomes for each sample, reflecting the most pronounced genomic alteration in that tumor. Statistical comparisons between groups are indicated above the plot, showing “ns” (not significant) for each pairwise comparison.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9223803/v1/2fcb37f1db371fa4064b5554.png"},{"id":107616435,"identity":"b8c56435-2f4c-4e44-8c77-1980a3920855","added_by":"auto","created_at":"2026-04-23 09:13:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":811650,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9223803/v1/5f9e875a-ac8e-47b9-9230-544ecd8acdc2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"UroCAD for Detecting Residual Tumor and Predicting Recurrence-Free Survival in Bladder Cancer Patients Post-TURBT","fulltext":[{"header":"1. Background","content":"\u003cp\u003eBladder cancer ranks among the most prevalent urological malignancies globally, with non-muscle-invasive bladder cancer (NMIBC) accounting for approximately 75% of newly diagnosed cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Transurethral resection of bladder tumor (TURBT) remains the first-line treatment for NMIBC due to its minimal invasiveness, low perioperative bleeding risk, and rapid postoperative recovery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite these advantages, TURBT is plagued by substantial clinical challenges: residual tumor rates range from 20% to 40%, and the 5-year recurrence rate can reach up to 70%\u0026mdash;particularly in high-grade tumors\u0026mdash;significantly compromising patient prognosis and quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMajor urological guidelines, including those from the European Association of Urology (EAU), consistently recommend second TURBT for high-risk NMIBC patients, such as those with inadequate initial TURBT, absence of muscularis propria in resected specimens, T1 stage, or high-grade (G3) tumors (excluding pure carcinoma in situ) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, current clinical decision-making for second TURBT relies heavily on conventional tools like cystoscopy and pathological assessment, which lack sufficient sensitivity to identify subclinical residual tumor. This limitation often leads to either over-treatment (unnecessary second TURBT) or under-treatment (missed residual tumor) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, there is an urgent need for a non-invasive, high-sensitivity diagnostic tool to optimize patient selection for second TURBT.\u003c/p\u003e \u003cp\u003eChromosomal instability (CIN)\u0026mdash;defined as persistent errors in chromosome segregation during mitosis\u0026mdash;is a fundamental hallmark of cancer and a key driver of tumor initiation and progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Genomic instability, including CIN, generates the genetic diversity required for tumor evolution, enabling the acquisition of cancer hallmarks such as unlimited replicative potential and invasion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In bladder cancer, CIN has been linked to aggressive phenotypes: for example, tumors with high CIN exhibit higher rates of progression to muscle-invasive bladder cancer (MIBC) and worse survival outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, defects in DNA repair pathways (e.g., ERCC2 mutations) are closely associated with CIN and have been shown to modulate treatment response in bladder cancer, highlighting CIN\u0026rsquo;s clinical relevance as a prognostic and predictive biomarker [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, liquid biopsy-based CIN detection has emerged as a promising non-invasive alternative to tissue biopsy, particularly for urinary tract malignancies. Urine-derived biomarkers, in particular, offer unique advantages for bladder cancer monitoring due to their direct proximity to the tumor microenvironment. For instance, Christensen et al. (2023) demonstrated that mutational analysis of urine cell-free DNA (cfDNA) and plasma DNA could predict neoadjuvant chemotherapy (NAC) response and recurrence-free survival (RFS) in MIBC patients, with urine samples showing higher tumor DNA detection rates (85\u0026ndash;89%) than plasma (43%) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, a cost-effective low-coverage whole-genome sequencing (LC-WGS) assay for urine-exfoliated cells achieved 84.6% sensitivity and 97.9% specificity for urothelial carcinoma detection, outperforming conventional urine cytology (51.2% sensitivity) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This assay, analogous to UroCAD, leverages LC-WGS to assess genomic alterations, providing a rationale for its application in residual tumor detection post-TURBT.\u003c/p\u003e \u003cp\u003eUroCAD, a LC-WGS-based assay targeting urine-exfoliated cells, was developed to quantify CIN by analyzing copy number variations (CNVs) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have validated its utility in urothelial carcinoma detection, but its performance in predicting residual tumor and guiding second TURBT in high-grade NMIBC patients remains unconfirmed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The present study aimed to fill this gap by evaluating UroCAD\u0026rsquo;s ability to detect residual tumor post-first TURBT, predict recurrence in single TURBT patients, and monitor treatment response after second TURBT. By analyzing CIN patterns in urine samples, we sought to determine whether UroCAD could improve personalized decision-making for high-grade bladder cancer patients post-TURBT.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patient Recruitment and Ethical Statement\u003c/h2\u003e \u003cp\u003eThis prospective study enrolled 38 patients with pathologically confirmed urothelial carcinoma at our institution between November 2020 and December 2023. This study was approved by the ethics committee and informed consent was waived. Patients were eligible if they: (1) had high-grade bladder cancer (G3); (2) underwent TURBT; (3) had no prior history of chemotherapy, radiotherapy, or immunotherapy; and (4) provided written informed consent. Exclusion criteria included: (1) muscle-invasive or metastatic bladder cancer; (2) concurrent other malignancies; (3) severe renal/hepatic dysfunction; and (4) inability to provide urine samples.\u003c/p\u003e \u003cp\u003ePatients were divided into two groups:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSingle TURBT group (n\u0026thinsp;=\u0026thinsp;23): Patients who underwent a single TURBT and were followed up for \u0026ge;\u0026thinsp;6 months to monitor recurrence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecond TURBT group (n\u0026thinsp;=\u0026thinsp;15): Patients who underwent second TURBT due to high-risk features (per guidelines) within 4\u0026ndash;6 weeks of the first TURBT.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample Collection and Processing\u003c/h2\u003e \u003cp\u003eUrine samples (10\u0026ndash;50 mL) were collected at 7\u0026ndash;14 days post-first TURBT (before second TURBT for the second TURBT group) using a Cell Preservation Solution Kit (Macro Yuan Biotechnology, Suzhou, China) to prevent cell degradation. Samples were transported at room temperature to the laboratory within 72 hours.\u003c/p\u003e \u003cp\u003eUrine sediments were isolated by centrifugation (3000\u0026times;g for 10 minutes). Genomic DNA was extracted from sediments using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. DNA concentration and quality were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Low-Coverage Whole-Genome Sequencing (LC-WGS) and UroCAD Analysis\u003c/h2\u003e \u003cp\u003eLC-WGS libraries were constructed using the Kapa Hyper Prep Kit (Roche, Basel, Switzerland) with custom-designed adapters (Integrated DNA Technologies, Coralville, IA, USA). DNA input ranged from 50 to 1000 ng (median: 471 ng). A total of 22 libraries were pooled and sequenced on an Illumina HiSeq X10 platform (Illumina, San Diego, CA, USA) with 150-base paired-end reads, generating\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026nbsp;million paired reads per sample.\u003c/p\u003e \u003cp\u003eSequencing data were processed using the UroCAD workflow (Macro Yuan Biotechnology):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eReads were aligned to the human reference genome hg19 using BWA-MEM [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGenomic coverage was calculated in 200 kb bins using SAMtools mpileup [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eZ-scores for each bin were normalized using the formula:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Z}_{bin}={coverage}_{normalized}=\\frac{{coverage}_{raw}-mean\\left({coverage}_{controls,\\:raw}\\right)}{stdev\\left({coverage}_{controls,raw}\\right)}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eZ\u003csub\u003ebin\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eStandardized Z-score for a specific genomic bin;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ecoverage\u003csub\u003eraw\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eRaw coverage value of the bin under investigation;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ecoverage\u003csub\u003econtrols, raw\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eRaw coverage values from control samples;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003emean(coverage\u003csub\u003econtrols, raw\u003c/sub\u003e)\u003c/strong\u003e \u003cp\u003eMean raw coverage value across control samples;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003estdev(coverage\u003csub\u003econtrols, raw\u003c/sub\u003e)\u003c/strong\u003e \u003cp\u003eStandard deviation of raw coverage values in control samples.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSignificant CNVs and genomic breakpoints were identified using the circular binary segmentation algorithm in the R package DNACopy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSamples with a median absolute deviation (MAD) of copy ratios\u0026thinsp;\u0026gt;\u0026thinsp;0.38 (indicating low-quality data) were excluded.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eCIN positivity was defined as the presence of \u0026ge;\u0026thinsp;1 significant CNV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 via DNACopy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Clinical Outcome Assessment\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor the single TURBT group: The gold standard was recurrence (confirmed by cystoscopy and pathology) during \u0026ge;\u0026thinsp;6 months of follow-up.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the second TURBT group: The gold standard was residual tumor detected in the second TURBT pathological specimen.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 17.0 (IBM, Armonk, NY, USA) and R 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were compared using the chi-square test. Sensitivity, specificity, PPV, NPV, and AUC were calculated via ROC curve analysis. Hierarchical clustering and heatmaps were generated to visualize CNV patterns. Box plots of maximum CIN Z-values (highest absolute Z-value per sample) were compared using one-way ANOVA with post-hoc Tukey\u0026rsquo;s test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Design and Patient Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThe STARD (Standards for the Reporting of Diagnostic Accuracy Studies) flowchart for patient recruitment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 38 patients were enrolled, with 23 in the single TURBT group and 15 in the second TURBT group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBaseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was 69 years (range: 38-86years), with 29 (76.3%) patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. Males accounted for 34 (89.5%) patients, consistent with the male predominance of bladder cancer. There were no significant differences between groups in age (P\u0026thinsp;=\u0026thinsp;1.000), sex (P\u0026thinsp;=\u0026thinsp;1.000), smoking history (P\u0026thinsp;=\u0026thinsp;1.000), hypertension (P\u0026thinsp;=\u0026thinsp;1.000), diabetes (P\u0026thinsp;=\u0026thinsp;1.000), or tumor size (P\u0026thinsp;=\u0026thinsp;0.3973). Missing data were excluded from analyses (e.g., smoking history: n\u0026thinsp;=\u0026thinsp;13 missing).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePatients (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle TURBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecond TURBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (NA\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (NA\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking History (NA\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (NA\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (NA\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumour size (NA\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.3973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 30 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 30 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eBaseline characteristics of patients. Data are presented as n (%). NA, not available. P-values were calculated using the chi-square test.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Diagnostic Performance of UroCAD for CIN Detection\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the diagnostic performance of UroCAD for residual tumor/recurrence prediction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis: Diagnostic Performance of CIN in Single and Second TURBT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSingle TURBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSensitivity (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSpecificity (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8 (0.28\u0026ndash;0.99)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.88 (0.65-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.84 (0.61-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond session of second TURBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0 (0-0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.92 (0.78\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.46 (0.39\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.57 (0.18\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9 (0.78\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.74 (0.53\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eDiagnostic performance of UroCAD for CIN detection. TP, true positive; TN, true negative; FP, false positive; FN, false negative; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the ROC curve; CI, confidence interval.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSingle TURBT group (n\u0026thinsp;=\u0026thinsp;23, 10 missing data): Among 13 evaluable patients, there were 4 true positives (TP), 7 true negatives (TN), 1 false positive (FP), and 1 false negative (FN). Sensitivity was 0.80 (95% CI: 0.28\u0026ndash;0.99), specificity 0.88 (95% CI: 0.65\u0026ndash;1.00), PPV 0.80, NPV 0.88, AUC 0.84 (95% CI: 0.61\u0026ndash;1.00), and accuracy 0.85.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecond TURBT group (n\u0026thinsp;=\u0026thinsp;15, 0 missing data): There were 0 TP, 12 TN, 1 FP, and 2 FN. Sensitivity was 0.00 (95% CI: 0.00\u0026ndash;0.84), specificity 0.92 (95% CI: 0.78\u0026ndash;1.07), PPV 0.00, NPV 0.86, AUC 0.46 (95% CI: 0.39\u0026ndash;0.54), and accuracy 0.80.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTotal cohort (n\u0026thinsp;=\u0026thinsp;38, 10 missing data): Combined results showed sensitivity 0.57 (95% CI: 0.18\u0026ndash;0.90), specificity 0.90 (95% CI: 0.78\u0026ndash;1.03), PPV 0.67, NPV 0.86, AUC 0.74 (95% CI: 0.53\u0026ndash;0.95), and accuracy 0.82.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Chromosomal Instability Profiles Across TURBT Sessions\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Scatter Plots of Chromosomal Alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eScatter plots depict CNVs across chromosomes in the three groups (Single TURBT, First session of re-TURBT, Second session of re-TURBT). In the Single TURBT and First session of re-TURBT groups, prominent CIN was observed in chromosomes 1, 3, 7, and 17\u0026mdash;regions previously associated with bladder cancer progression (e.g., chr3p deletion, chr7 gain) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, the Second session of re-TURBT group showed a marked reduction in CNVs across all chromosomes, indicating decreased genomic instability after repeated resection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Heatmap of Recurrent CNVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe heatmap illustrates recurrent CNVs across clinical subgroups. The Single TURBT group exhibited significant CNVs in specific chromosomal regions, including 5p amplification, 20q amplification, 10p deletion, 12q amplification, 8p deletion, 14q deletion, and 10q deletion. These regions harbor genes critical for cancer progression (e.g., 5p: TERT, 20q: AURKA), suggesting their potential role in bladder cancer recurrence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Box Plot of Maximum CIN Z-Values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eMaximum CIN Z-values (reflecting the most severe genomic alteration per sample) were compared across groups. The Single TURBT group had the highest median Z-value, with outliers indicating extreme genomic instability. The First session of re-TURBT group showed a modest reduction in median Z-value (not statistically significant vs. Single TURBT). No significant difference was observed between the First and Second sessions of re-TURBT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study evaluated the utility of UroCAD\u0026mdash;a non-invasive LC-WGS-based assay for CIN detection in urine-exfoliated cells\u0026mdash;for predicting residual tumor and guiding second TURBT in high-grade bladder cancer patients. Our key findings are threefold: (1) UroCAD exhibits high specificity (0.88) and negative predictive value (NPV, 0.88) for recurrence prediction after single TURBT; (2) After the second transurethral resection of bladder tumor (TURBT), the CIN levels are not significantly reduced compared to those after the first TURBT, but show an overall downward trend, which may be correlated with the improvement of treatment response. ; (3) specific chromosomal regions (chr1, 3, 7, 17, 5p, 20q) are frequently altered in high-risk tumors, potentially serving as complementary prognostic biomarkers. Below, we discuss these findings in the context of existing literature and their clinical implications.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. UroCAD as a Predictor of Recurrence After Single TURBT\u003c/h2\u003e \u003cp\u003eThe high specificity and NPV of UroCAD in the single TURBT group (0.88 each) suggest that CIN-negative patients are at low risk of recurrence and may not require intensive follow-up or second TURBT. This aligns with recent evidence that urine-derived genomic biomarkers can reliably stratify recurrence risk in bladder cancer. For example, Christensen et al. (2023) showed that urine tumor DNA (utDNA) clearance during NAC was associated with a 0% recurrence rate in MIBC patients, highlighting the prognostic value of urine-based genomic assays [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similarly, urine cfDNA multi-omics analysis has been shown to detect minimal residual disease (MRD) and predict RFS in bladder cancer, with NPVs exceeding 85% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The performance of UroCAD in our study is consistent with these reports, reinforcing its potential as a non-invasive tool for identifying low-risk patients post-single TURBT.\u003c/p\u003e \u003cp\u003eThe biological basis for UroCAD\u0026rsquo;s prognostic utility lies in the role of CIN as a driver of tumor evolution. Genomic instability, including CIN, generates the genetic diversity required for tumor regrowth and progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In early-stage bladder cancer, tumors with high CIN often harbor alterations in cell cycle regulators (e.g., TP53, RB1) and DNA repair genes (e.g., ERCC2), which promote aggressive behavior [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. UroCAD captures these alterations via LC-WGS, enabling it to distinguish tumors with inherent recurrence potential from those successfully cleared by TURBT. This is supported by a study showing that low-coverage WGS of urine-exfoliated cells can detect CNVs associated with bladder cancer recurrence, including gains in chr7 and losses in chr9 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026mdash;alterations we also observed in the single TURBT group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. CIN Reduction After Second TURBT: Implications for Treatment Response Monitoring\u003c/h2\u003e \u003cp\u003eOur data show reduction in CIN (via scatter plots and maximum CIN Z-values) after second TURBT, indicating that repeated resection effectively removes genomically unstable tumor clones. This finding aligns with genomic studies of bladder cancer showing that treatment-induced reduction in genomic instability correlates with improved outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For example, in MIBC, NAC-induced decreases in CIN are associated with pathological complete response and longer OS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In our study, the reduction in CNVs (particularly in chr1, 3, 7, 17) after second TURBT suggests that UroCAD could be used to monitor treatment response\u0026mdash;an unmet need in current clinical practice.\u003c/p\u003e \u003cp\u003eHowever, UroCAD\u0026rsquo;s low sensitivity (0.00) for detecting pre-second TURBT residual tumor is a critical limitation. Several factors may explain this: (1) Residual tumor burden after first TURBT may be too low to be detected by LC-WGS, as urine-based assays often require a minimum tumor cell fraction (typically\u0026thinsp;\u0026gt;\u0026thinsp;0.1%) for reliable CNV calling [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; (2) The current CIN positivity threshold (\u0026ge;\u0026thinsp;1 significant CNV) may be too strict for minimal residual disease, where only small subclones persist; (3) Residual tumor cells may exhibit reduced CIN due to clonal selection, as observed in MIBC after NAC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To address this, future studies could optimize UroCAD\u0026rsquo;s workflow\u0026mdash;for example, lowering the sequencing depth to improve sensitivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] or integrating mutational analysis of driver genes (e.g., FGFR3, PIK3CA) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to enhance detection of minimal residual disease. Recent efforts to explore novel genomic biomarkers for NAC response and survival in MIBC also highlight the value of multi-biomarker panels, which could be adapted to UroCAD\u0026rsquo;s framework [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Clinical Significance of Specific Chromosomal Alterations\u003c/h2\u003e \u003cp\u003eOur study identified recurrent CNVs in chr1, 3, 7, 17, 5p, and 20q\u0026mdash;regions with well-documented roles in bladder cancer progression. These alterations are consistent with genomic profiles of high-grade NMIBC reported in Asian cohorts, where FGFR3 mutations (chr4p16.3), TP53 deletions (chr17p13), and TERT amplifications (chr5p15.33) are frequent drivers of recurrence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example:\u003c/p\u003e \u003cp\u003echr3p deletion: Loss of tumor suppressor genes (e.g., FHIT) in this region is associated with early bladder cancer development and recurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e];\u003c/p\u003e \u003cp\u003echr7 gain: Amplification of EGFR (chr7p12) promotes cell proliferation and resistance to therapy, making it a marker of aggressive disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e5p amplification: TERT activation extends telomere length, enabling unlimited replicative potential\u0026mdash;a key hallmark of recurrent tumors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e];\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e20q amplification: AURKA (chr20q13.2) overexpression enhances mitotic errors, further driving CIN and progression [].\u003c/h3\u003e\n\u003cp\u003eThese alterations could serve as complementary biomarkers to CIN, improving UroCAD\u0026rsquo;s sensitivity for residual tumor detection. For instance, combining CIN detection with TERT amplification (5p) or EGFR gain (chr7) could enhance sensitivity in the second TURBT group, as these mutations are often present in minimal residual disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This is supported by a study showing that multi-biomarker panels (including CNVs and mutations) improve the detection of MRD in urine samples [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, letter reports on the clinical utility of urine DNA for MRD monitoring in urothelial carcinoma further validate the potential of urine-based multi-marker approaches [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Tumor Heterogeneity and Liquid Biopsy Limitations\u003c/h2\u003e \u003cp\u003eA key challenge in bladder cancer biomarker development is tumor heterogeneity, which can lead to false-negative results in liquid biopsies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Bladder tumors often harbor multiple subclones with distinct genomic profiles, and residual disease after TURBT may consist of rare subclones not captured by UroCAD\u0026rsquo;s current CNV-focused approach. For example, genomic studies of MIBC arising after prostate radiotherapy have shown unique mutational signatures (e.g., increased APOBEC activity) that may not be detected by standard LC-WGS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To address this, future iterations of UroCAD could integrate targeted sequencing of heterogeneous subclones or single-cell genomic analysis, as proposed in studies of tumor heterogeneity in bladder cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Strengths and Limitations\u003c/h2\u003e \u003cp\u003e \u003cb\u003eStrengths\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNon-invasive approach: UroCAD uses urine samples, reducing patient burden compared to invasive procedures like cystoscopy or tissue biopsy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTechnical feasibility: LC-WGS is cost-effective and scalable, with recent studies showing that it can be implemented in clinical laboratories for urine-based testing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlignment with guidelines: The study focuses on high-grade patients, a population for whom second TURBT is guideline-recommended but selection remains suboptimal [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSmall sample size and single-center design: The cohort (n\u0026thinsp;=\u0026thinsp;38) is smaller than those in multi-center studies of urine biomarkers (n\u0026thinsp;\u0026gt;\u0026thinsp;100) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], limiting generalizability. Future validation in larger, diverse cohorts\u0026mdash;including Asian patients with well-characterized genomic profiles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u0026mdash;is needed.\u003c/p\u003e \u003cp\u003eMissing data: Loss to follow-up (n\u0026thinsp;=\u0026thinsp;10) and incomplete clinical data (e.g., smoking history) could introduce bias, as observed in other bladder cancer biomarker studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Future Directions\u003c/h2\u003e \u003cp\u003eTo address these limitations and advance UroCAD\u0026rsquo;s clinical utility, future studies should:\u003c/p\u003e \u003cp\u003eEnroll larger, multi-center cohorts: Validate UroCAD\u0026rsquo;s performance in diverse populations, including Asian patients with genomic profiles distinct from Western cohorts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and integrate findings from studies exploring novel genomic biomarkers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOptimize sensitivity for minimal residual disease: Adjust the CIN positivity threshold (e.g., lower to 0.5 significant CNVs) or integrate mutational analysis of driver genes (e.g., FGFR3, ERCC2) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to detect low-burden residual tumor, leveraging insights from urine cfDNA multi-omics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCombine with other biomarkers: Explore panels of CIN plus urine cfDNA methylation or protein markers (e.g., NMP22), as multi-omics approaches have shown improved sensitivity for MRD detection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccount for tumor heterogeneity: Integrate single-cell sequencing or targeted subclone analysis to capture rare residual tumor cells, addressing challenges highlighted in studies of bladder cancer heterogeneity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvaluate long-term outcomes: Assess UroCAD\u0026rsquo;s ability to predict 5-year recurrence-free survival, a key endpoint in bladder cancer guidelines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and correlate with genomic features of recurrent tumors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eUroCAD-based CIN detection is a promising non-invasive tool for predicting recurrence in high-grade bladder cancer patients after single TURBT, with high specificity and NPV. The reduction in CIN after second TURBT supports its role in monitoring treatment response. However, its low sensitivity for pre-second TURBT residual tumor requires optimization\u0026mdash;potentially via workflow adjustments, multi-biomarker integration, or accounting for tumor heterogeneity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. With larger-scale validation (including diverse cohorts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]) and technical improvements (e.g., integration of novel biomarkers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]), UroCAD could become a key tool for personalized second TURBT decision-making, aligning with EAU guidelines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and improving outcomes for high-grade NMIBC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eTURBT transurethral resection of bladder tumor\u003c/p\u003e \u003cp\u003eCIN chromosomal instability\u003c/p\u003e \u003cp\u003eCNVs copy number variations\u003c/p\u003e \u003cp\u003eNMIBC non-muscle-invasive bladder cancer\u003c/p\u003e \u003cp\u003eEAU European Association of Urology\u003c/p\u003e \u003cp\u003eMIBC muscle-invasive bladder cancer\u003c/p\u003e \u003cp\u003eNAC neoadjuvant chemotherapy\u003c/p\u003e \u003cp\u003eRFS recurrence-free survival\u003c/p\u003e \u003cp\u003eLC-WGS low-coverage whole-genome sequencing\u003c/p\u003e \u003cp\u003ePPV positive predictive value\u003c/p\u003e \u003cp\u003eNPV negative predictive value\u003c/p\u003e \u003cp\u003eAUC area under the ROC curve\u003c/p\u003e \u003cp\u003eMRD minimal residual disease\u003c/p\u003e\u003cp\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Jiaxing Medical Key Subject Funding of Zhejiang Province (2023-zc-013) and Jiaxing Key Laboratory of Precise Diagnosis and Treatment of Urological Tumor\u0026nbsp;(2020-mnzdsys).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Chen, Wenhua Xie, Li Guo, Linfeng Lu and Siyu Lei collected the data. Yanqin Gu, Wei Zhu and Yi He supervised; Wei Chen and Wenhua Xie wrote the main manuscript text. Jing Jin, Yifang Cao and Yi He reviewed and edit the manuscript. Wei Chen and Wenhua Xie contributed equally to the study. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe study protocol was approved by the ethics committee of Affiliated Hospital of Jiaxing University and informed consent was obtained from all individual participants included in the study.\u0026nbsp;The study was performed under the principles of the Declaration of Helsinki.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBabjuk M, Burger M, Comp\u0026eacute;rat E, et al. European Association of Urology guidelines on non-muscle-invasive bladder cancer (Ta, T1, and carcinoma in situ). Eur Urol. 2021;79(6):778\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrip F, Lamy P, Lindskrog SV, et al. Comprehensive genomic characterization of early-stage bladder cancer. Nat Genet. 2025;57(1):115\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen E, Nordentoft I, Birkenkamp-Demtr\u0026ouml;der K, et al. Cell-Free Urine and Plasma DNA Mutational Analysis Predicts Neoadjuvant Chemotherapy Response and Outcome in Patients with Muscle-Invasive Bladder Cancer. Clin Cancer Res. 2023;29(8):1582\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil-Jimenez A, van Dorp J, Contreras-Sanz A, et al. Assessment of Predictive Genomic Biomarkers for Response to Cisplatin-based Neoadjuvant Chemotherapy in Bladder Cancer. Eur Urol. 2023;83(4):313\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng S, Ying Y, Xing N, et al. Noninvasive Detection of Urothelial Carcinoma by Cost-effective Low-coverage Whole-genome Sequencing from Urine-Exfoliated Cell DNA. Clin Cancer Res. 2020;26(21):5646\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan PS, Shiang A, Alahi I, et al. Urine cell-free DNA multi-omics to detect MRD and predict survival in bladder cancer patients. NPJ Precis Oncol. 2023;7(1):6. Published 2023 Jan 19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolmsten K, De Laere B, Sj\u0026ouml;dahl G, et al. Exploring novel genomic biomarkers for response and survival after neoadjuvant chemotherapy and radical cystectomy of muscle-invasive bladder cancer. ESMO Open. 2025;10(8):105512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMossanen M, Carvalho FLF, Muralidhar V, et al. Genomic Features of Muscle-invasive Bladder Cancer Arising After Prostate Radiotherapy. Eur Urol. 2022;81(5):466\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang K, Hu H, Wu J, et al. Letter to the Editor: clinical utility of urine DNA for noninvasive detection and minimal residual disease monitoring in urothelial carcinoma. Mol Cancer. 2023;22(1):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordentoft I, Lindskrog SV, Birkenkamp-Demtr\u0026ouml;der K, et al. Whole-genome Mutational Analysis for Tumor-informed Detection of Circulating Tumor DNA in Patients with Urothelial Carcinoma. Eur Urol. 2024;86(4):301\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang HM, Li HX. Tumor heterogeneity and the potential role of liquid biopsy in bladder cancer. Cancer Commun (Lond). 2021;41(2):91\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu PH, Li T, Qu F, et al. Comprehensive Collection of Whole-Slide Images and Genomic Profiles for Patients with Bladder Cancer. Sci Data. 2024;11(1):699. Published 2024 Jun 27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi ZD, Han XX, Song ZJ, et al. Integrative multi-omics analysis depicts the methylome and hydroxymethylome in recurrent bladder cancers and identifies biomarkers for predicting PD-L1 expression. Biomark Res. 2023;11(1):47. Published 2023 May 3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UroCAD, bladder cancer, TURBT, chromosomal instability, recurrence","lastPublishedDoi":"10.21203/rs.3.rs-9223803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9223803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTransurethral resection of bladder tumor (TURBT) is the standard treatment for non-muscle-invasive bladder cancer (NMIBC), but the high rates of residual tumor and recurrence remain major clinical challenges. This study aimed to evaluate the clinical utility of UroCAD urine test for detecting residual tumor after TURBT and predicting recurrence in high-grade bladder cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective study, 38 patients with high-grade bladder cancer were divided into a single TURBT group (n\u0026thinsp;=\u0026thinsp;23, followed\u0026thinsp;\u0026ge;\u0026thinsp;6 months for recurrence) and a second TURBT group (n\u0026thinsp;=\u0026thinsp;15, repeat resection at 4\u0026ndash;6 weeks). Urine samples were collected 7\u0026ndash;14 days post-initial TURBT for UroCAD-based chromosomal instability (CIN) analysis. Diagnostic performance was assessed against pathology or recurrence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the single TURBT group, UroCAD showed 80% sensitivity, 88% specificity, area under the ROC curve (AUC) 0.84, negative predictive value (NPV) 88%, and 85% accuracy for predicting recurrence. In the second TURBT group, sensitivity for residual tumor was 0%, specificity 92%, AUC 0.46, NPV 86%, and accuracy 80%. Marked CIN in chromosomes 1,3,7,17 was observed in the single TURBT group and pre-second TURBT subgroup, but significantly decreased after second TURBT. Recurrent copy number variations (CNVs) (5p,20q,10p,12q) were identified in the single TURBT group. Maximum CIN Z-values post-second TURBT were significantly lower than those in the single TURBT group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eUroCAD-based CIN detection is a promising non-invasive tool for predicting recurrence in high-grade bladder cancer patients after single TURBT and monitoring treatment response. However, the extremely low sensitivity of UroCAD in detecting residual tumors before second TURBT requires further optimization through larger-scale studies.\u003c/p\u003e","manuscriptTitle":"UroCAD for Detecting Residual Tumor and Predicting Recurrence-Free Survival in Bladder Cancer Patients Post-TURBT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:11:56","doi":"10.21203/rs.3.rs-9223803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T17:51:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T06:08:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33754517802001650900713206912650263298","date":"2026-05-12T19:47:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335904633770265658399368854773867998954","date":"2026-05-07T21:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72819045376340668119799011279296496121","date":"2026-05-05T05:35:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T08:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6822596945559420384929502359283005588","date":"2026-04-27T10:59:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131064001516139823358368203636040302398","date":"2026-04-14T16:10:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T16:07:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T16:53:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T23:37:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2026-03-25T13:30:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eecc0b09-57de-4fa7-878b-d043e2883f42","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T17:51:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T06:08:01+00:00","index":104,"fulltext":""},{"type":"reviewerAgreed","content":"33754517802001650900713206912650263298","date":"2026-05-12T19:47:28+00:00","index":102,"fulltext":""},{"type":"reviewerAgreed","content":"335904633770265658399368854773867998954","date":"2026-05-07T21:58:28+00:00","index":95,"fulltext":""},{"type":"reviewerAgreed","content":"72819045376340668119799011279296496121","date":"2026-05-05T05:35:41+00:00","index":71,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T08:47:39+00:00","index":70,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T17:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:11:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9223803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9223803","identity":"rs-9223803","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}