Pathologie (Heidelberg, Germany) · 2025
Evaluation of single-nucleotide variants in bladder cancer using prediction algorithms.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Möller June, Seillier Lancelot, Fürstberger Axel, Rose Michael, Jonigk Danny D, Ortiz-Brüchle Nadina, Gaisa Nadine T
Аннотация
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.
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.