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Background <p>Bladder and urinary tract cancer show poor survival rates and demand novel therapeutic strategies. Advances in the omics domain have expanded genetic analysis, with prediction tools offering potential support. However, their performance may differ by tumor entity.</p> Objective <p>This study aimed to evaluate prediction tool performance using genetic data from bladder and urinary tract cancer.</p> Methods <p>Variant data were obtained from ClinVar and cBioPortal for bladder cancer (<i>n</i> = 441), PanCancer (<i>n</i> = 361), and benign variants (<i>n</i> = 357). Sixteen prediction algorithms were assessed individually and in combinations of two or three; oncogenes and tumor suppressors were compared. A&#xa0;PanCancer dataset of Suybeng et&#xa0;al. was also analyzed.</p> Results <p>Prediction performance varied across datasets. Combinations of three tools achieved the highest sensitivity (100%: MutationTaster/MetaSVM/List-S2) and specificity (97.45%: MutationTaster/DEOGEN2/FATHMM_XF). Entity-specific and gene-type differences were observed.</p> Conclusion <p>Combining prediction tools enhances genetic analysis. Tool selection should depend on cancer entity, gene function, and study objective.</p>

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Evaluation of single-nucleotide variants in bladder cancer using prediction algorithms

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • June Möller,
  • Lancelot Seillier,
  • Axel Fürstberger,
  • Michael Rose,
  • Danny D. Jonigk,
  • Nadina Ortiz-Brüchle,
  • Nadine T. Gaisa

摘要

Background

Bladder and urinary tract cancer show poor survival rates and demand novel therapeutic strategies. Advances in the omics domain have expanded genetic analysis, with prediction tools offering potential support. However, their performance may differ by tumor entity.

Objective

This study aimed to evaluate prediction tool performance using genetic data from bladder and urinary tract cancer.

Methods

Variant data were obtained from ClinVar and cBioPortal for bladder cancer (n = 441), PanCancer (n = 361), and benign variants (n = 357). Sixteen prediction algorithms were assessed individually and in combinations of two or three; oncogenes and tumor suppressors were compared. A PanCancer dataset of Suybeng et al. was also analyzed.

Results

Prediction performance varied across datasets. Combinations of three tools achieved the highest sensitivity (100%: MutationTaster/MetaSVM/List-S2) and specificity (97.45%: MutationTaster/DEOGEN2/FATHMM_XF). Entity-specific and gene-type differences were observed.

Conclusion

Combining prediction tools enhances genetic analysis. Tool selection should depend on cancer entity, gene function, and study objective.