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ARTICLE
Association of urinary tract infection and low albumin/globulin ratio with chemoresistance to gemcitabine-cisplatin in advanced urothelial carcinoma
1 Department of Urology, Capital Medical University, Beijing Friendship Hospital, Beijing, 100050, China
2 Institute of Urology, Beijing Municipal Health Commission, Beijing, 100050, China
* Corresponding Authors: Ye Tian. Email: ; Xinyi Hu. Email:
# These two authors contributed equally to this work
Canadian Journal of Urology 2025, 32(5), 411-422. https://doi.org/10.32604/cju.2025.066758
Received 16 April 2025; Accepted 14 July 2025; Issue published 30 October 2025
Abstract
Objective: Urothelial carcinoma (UC) remains a prevalent malignancy with high recurrence and chemoresistance rates despite gemcitabine-cisplatin (GC) chemotherapy. The study aimed to identify clinical risk factors for chemoresistance in advanced UC patients and develop a predictive model. Method: A retrospective analysis was conducted on 375 UC patients who received postoperative GC chemotherapy between 2013 and 2024. Patients were categorized into chemotherapy-resistant (CR, n = 91) and non-chemotherapy resistant (NCR, n = 284) groups based on tumor progression. Clinical, pathological, and laboratory variables were compared using t-tests and chi-square tests. Kaplan-Meier assessed overall survival (OS), and binary logistic regression identified independent predictors of chemoresistance. Result: Overall survival (OS) was significantly lower in the CR group than in the NCR group urinary tract infection (OR: 54.60; 95% CI: [21.19, 140.67]) and low A/G (OR: 0.18;95% CI: [0.03, 0.94]). The prediction model was: Logit(P)=−3.69+0.96×multifocal tumor+1.05×Tstage+4.00×long-termurinary tract infection(UTI)−1.73×A/G. Conclusion: Multifocality, high T stage, persistent UTI, and low A/G ratio are significantly associated with chemoresistance to GC in UC. These routine clinical indicators may support early identification of high-risk patients and guide treatment decisions.Keywords
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Copyright © 2025 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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