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Review | Open Access

Biomarkers for predicting bladder cancer therapy response

IOANA MARIA MIHAI1GANG WANG1,2( )
Department of Pathology and Laboratory Medicine, British Columbia Cancer Vancouver Centre, Vancouver, BC V5Z 4E6, Canada
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada
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Abstract

The advent of precision medicine has underscored the importance of biomarkers in predicting therapy response for bladder cancer, a malignancy marked by considerable heterogeneity. This review critically examines the current landscape of biomarkers to forecast treatment outcomes in bladder cancer patients. We explore a range of biomarkers, including genetic, epigenetic, proteomic, and transcriptomic indicators, from multiple sample sources, including urine, tumor tissue and blood, assessing their efficacy in predicting responses to chemotherapy, immunotherapy, and targeted therapies. Despite promising developments, the translation of these biomarkers into clinical practice faces significant challenges, such as variability in biomarker performance, the necessity for large-scale validation studies, and the integration of biomarker testing into routine clinical workflows. We also highlight the need for standardized methodologies and robust assays to ensure consistency and reliability. Future directions point towards longitudinal studies and the development of combination biomarker panels to enhance predictive accuracy. This review emphasizes the transformative potential of predictive biomarkers in improving patient outcomes and advocates for continued collaborative efforts to overcome existing barriers in this rapidly evolving field.

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Oncology Research
Pages 533-547

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Cite this article:
MIHAI IM, WANG G. Biomarkers for predicting bladder cancer therapy response. Oncology Research, 2025, 33(3): 533-547. https://doi.org/10.32604/or.2024.055155

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Received: 19 June 2024
Accepted: 08 November 2024
Published: 28 February 2025
© The Author 2024.

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.