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Review
. 2026 Jan 5;16(1):25.
doi: 10.3390/jpm16010025.

Emerging Molecular and Computational Biomarkers in Urothelial Carcinoma: Innovations in Diagnosis, Prognosis, and Therapeutic Response Prediction

Fernando Alberca-Del Arco  1   2   3 , Rocío Santos-Perez de la Blanca  1   2   3 , Elisa Maria Matas-Rico  2   4 , Bernardo Herrera-Imbroda  1   2   3   5 , Félix Guerrero-Ramos  3   6
Affiliations
Review

Emerging Molecular and Computational Biomarkers in Urothelial Carcinoma: Innovations in Diagnosis, Prognosis, and Therapeutic Response Prediction

Fernando Alberca-Del Arco et al. J Pers Med. .

Abstract

Bladder cancer (BC) represents a major global health issue with high recurrence and significant mortality rates in cases of advanced disease. Currently, the development of molecular profiling, liquid biopsy technologies, and artificial intelligence (AI) software has resulted in unprecedented opportunities to improve diagnosis, prognostic assessment, and treatment selection. Recent multicenter studies have identified emerging metabolomic, proteomic, and genomic biomarkers with high sensitivity and specificity that may help replace or complement invasive approaches. AI-driven models that combine multi-omics datasets with radiomics and clinical parameters have demonstrated improved accuracy for predicting both therapeutic response and long-term outcomes, compared to standard approaches for risk stratification. Additionally, the incremental clinical usefulness of liquid biopsy platforms has been demonstrated for the monitoring of non-muscle-invasive bladder cancer and minimal disease detection. As these innovations converge, they herald the advent of a new era of personalized management of urothelial carcinoma; however, broad-based clinical implementation will require large-scale validation, standardization, regulatory harmonization, and economic analyses. Background: Bladder cancer continues to be a global health problem, particularly in the advanced disease setting where treatment options are limited, and mortality remains high. The exciting advances in precision medicine, including breakthrough molecular profiling techniques, liquid biopsy, and opportunities to apply AI to interpret these molecular data, hold unprecedented promise in improving the accuracy of diagnosis, prognostic stratification, and therapeutic decision-making.

Keywords: artificial machine learning; biomarkers; bladder cancer; immune checkpoint inhibitors; liquid biopsy; multi-omics; tumor microenvironment; urothelial carcinoma.

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Conflict of interest statement

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic representation of tumor heterogeneity in bladder cancer. Cellular, genomic, and epigenetic diversity drives tumor progression, metastasis, therapeutic resistance, and immune evasion, ultimately influencing clinical outcomes and therapeutic decision-making.
Figure 2
Figure 2
Applicability of LB in DNA sequencing and clinical outcomes.
See this image and copyright information in PMC

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