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Survival curves after radical cystectomy in muscle-invasive bladder cancer according to the level of Nectin-2 (A) and Nectin-4 (B). The cut-offs for the prognostic performance of urine Nectin-2 and Nectin-4 levels were determined using the median level. In the MIBC cohort, disease-free survival, cancer-specific survival, and overall survival from the day of radical cystectomy were obtained using the Kaplan–Meier method and compared using the log-rank test. Abbreviations: NMIBC, non-muscle-invasive bladder cancer; MIBC, muscle-invasive bladder cancer; RC, radical cystectomy.
Source publication
Simple Summary
The clinical utility of urine nectins in bladder cancer (BCa) is unclear. We investigated the potential diagnostic and prognostic values of Nectin-2 and Nectin-4. This study included 122 patients with BCa, including 78 with non-muscle-invasive BCa, 44 with muscle-invasive BCa, and ten healthy controls. The detection sensitivities of...
Citations
... Current studies suggest urinary nectin could serve as a potential diagnostic biomarker for BCa and show a correlation between urine levels, tumor expression, and serum levels in the analysis of nectin-4 (54). The use of urinary nectin biomarkers in combination with EV may yield clinical benefits. ...
Background and Objective: Bladder cancer (BCa) is the sixth most common malignancy in the United States in 2023. Stratified into non-muscle invasive and muscle-invasive types, non-muscle invasive bladder cancer (NMIBC) comprises 70% of cases. Immune checkpoint inhibitors and targeted molecular therapy agents have shown efficacy in locally advanced and metastatic BCa and may be promising in the localized disease setting, especially for Bacillus Calmette-Guerin (BCG)-unresponsive NMIBC. The present article aims to assess the contemporary status of four therapeutic options [pembrolizumab, atezolizumab, erdafitinib, and enfortumab-vedotin (EV)] for NMIBC as systemic and intravesical therapies. Methods: We conducted a non-systematic review using PubMed, Google Scholar, ClinicalTrials.gov, and articles written in English were considered. Key Content and Findings: Pembrolizumab, atezolizumab, erdafitinib, and EV offer alternative treatment strategies for BCG-unresponsive high-risk NMIBC. Pembrolizumab is effective as a systemic therapy via level-one evidence as other trials continue to evaluate the safety and immune responses via intravesical delivery. Atezolizumab shows promise in the treatment of NMIBC but its efficacy as a monotherapy is not yet clinically significant with limited follow-up thus far and ongoing studies are exploring combination therapy with BCG to improve outcomes. Erdafitinib has shown its efficacy and safety as ongoing studies explore its role in combination therapies to enhance efficacy and reduce side effects. EV shows significant efficacy and safety in patients with advanced urothelial carcinoma who failed prior therapy, however, the development of pre-treatment biomarkers is essential to optimize its use in NMIBC treatment. Conclusions: These drugs, with their novel mechanisms of action and targets, offer hope for improved outcomes and may galvanize a paradigm shift for NMIBC treatment in both the BCG-unresponsive and primary settings. Ongoing research and clinical trials are imperative to optimize the utilization of these drugs, define rational combination therapies, identify prognostic biomarkers of treatment efficacy, and thus expand the therapeutic armamentarium.
Purpose
Recurrence is the main factor for poor prognosis of bladder cancer. Therefore, it is necessary to develop new biomarkers to predict the prognosis of bladder cancer. In this study, we used machine learning (ML) methods based on a variety of clinical variables to screen prognostic biomarkers of bladder cancer.
Patients and Methods
A total of 345 bladder cancer patients were participated in this retrospective study and randomly divided into training and testing group. We used five supervised clustering ML algorithms: decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) to obtained prediction information through 34 clinical parameters.
Results
By comparing five ML algorithms, we found that total bilirubin (TBIL) and CA50 had the best performance in predicting the recurrence of bladder cancer. In addition, the combined predictive performance of the two is superior to the performance of any single indicator prediction.
Conclusion
ML technology can evaluate the recurrence of bladder cancer. This study shows that the combination of TBIL and CA50 can improve the prognosis prediction of bladder cancer recurrence, which can help clinicians make decisions and develop personalized treatment strategies.
