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A nomogram integrating mutation signatures and clinical features for prognostic stratification in bladder cancer

  • 0Department of Pulmonary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Reseach Center for Cancer, Tianjin, 300060, China.
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August 15, 2025

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August 15, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a prognostic model for bladder cancer (BLCA) using mutation signatures and clinical data. The model accurately predicts survival, aiding in clinical management of this common urinary tract malignancy.

Area Of Science

  • Oncology
  • Genomics
  • Biomarker Discovery

Background

  • Bladder cancer (BLCA) presents a significant challenge due to poor survival rates and limited treatment options.
  • There is a critical need for reliable biomarkers to improve prognosis stratification in BLCA patients.

Purpose Of The Study

  • To develop and validate a prognostic model for BLCA using a combination of mutation signatures and clinical characteristics.
  • To enhance the clinical management of BLCA by providing accurate survival predictions.

Main Methods

  • Retrospective analysis of 631 BLCA patients from TCGA and a Chinese cohort.
  • Univariate and multivariate Cox regression analyses to identify independent prognostic factors for overall survival (OS).
  • Construction and validation of a prognostic nomogram incorporating a 30-mutated gene signature and age.

Main Results

  • A 30-mutated gene signature and age were identified as independent prognostic factors for BLCA.
  • The prognostic nomogram demonstrated good predictive accuracy for 1-, 3-, and 5-year OS in both training and validation cohorts (AUCs ranging from 0.641 to 0.820).
  • High-risk profiles were associated with increased neoantigen burden, copy number variation (CNV) count, and DNA damage response (DDR) mutations.

Conclusions

  • The developed model accurately predicts bladder cancer survival risk over time.
  • This mutation signature-based prognostic tool shows potential for improving clinical decision-making and patient management in BLCA.

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