A nomogram integrating mutation signatures and clinical features for prognostic stratification in bladder cancer
Abstract Purpose Bladder cancer (BLCA) is a typical malignancy in the urinary tract, with a dismal survival rate and limited therapeutic options. There is a growing demand for clinically relevant biomarkers to effectively stratify prognosis. This study aims to develop a prognostic model utilizing mu...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-03338-w |
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| Summary: | Abstract Purpose Bladder cancer (BLCA) is a typical malignancy in the urinary tract, with a dismal survival rate and limited therapeutic options. There is a growing demand for clinically relevant biomarkers to effectively stratify prognosis. This study aims to develop a prognostic model utilizing mutation signatures and clinical characteristics to inform the clinical management of BLCA. Methods The data of 631 BLCA patients were retrospectively reviewed, including 538 patients from The Cancer Genome Atlas (TCGA) and 93 patients from a Chinese cohort. Univariate and multivariate analyses were performed to identify the independent prognostic factors for overall survival (OS). Results Multivariate Cox regression analysis revealed that a 30-mutated gene signature and age were independent prognostic factors for BLCA. The prognostic nomogram was constructed to predict the probability of 1-, 3-, and 5-year OS, achieving area under the curve (AUC) values of 0.800, 0.749, and 0.731 in the training group, and 0.641, 0.820, and 0.759 in the validation group, respectively. Additionally, high risk scores were associated with poorer outcomes across all clinical subgroups. Patients with high-risk profiles exhibited higher neoantigen burden (p = 0.029), copy number variation (CNV) count (p = 0.013), and DNA damage response (DDR) mutations (p < 0.001) compared to those with low-risk profiles. Conclusions The model incorporating mutation signatures and clinical factors demonstrated accuracy in predicting changing cancer survival risk over time.This suggests that the model has the potential to serve as a valuable prognostic tool for BLCA. |
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| ISSN: | 2730-6011 |