{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["22(1)"],"submitter":["Yang K"],"pubmed_abstract":["Current methods for the early detection and minimal residual disease (MRD) monitoring of urothelial carcinoma (UC) are invasive and/or possess suboptimal sensitivity. We developed an efficient workflow named urine tumor DNA multidimensional bioinformatic predictor (utLIFE). Using UC-specific mutations and large copy number variations, the utLIFE-UC model was developed on a bladder cancer cohort (n = 150) and validated in The Cancer Genome Atlas (TCGA) bladder cancer cohort (n = 674) and an upper tract urothelial carcinoma (UTUC) cohort (n = 22). The utLIFE-UC model could discriminate 92.8% of UCs with 96.0% specificity and was robustly validated in the BLCA_TCGA and UTUC cohorts. Furthermore, compared to cytology, utLIFE-UC improved the sensitivity of bladder cancer detection (p < 0.01). I"],"journal":["Molecular cancer"],"pagination":["25"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9898696"],"repository":["biostudies-literature"],"pubmed_title":["Letter to the Editor: clinical utility of urine DNA for noninvasive detection and minimal residual disease monitoring in urothelial carcinoma."],"pmcid":["PMC9898696"],"pubmed_authors":["Yao L","Ding F","Wu J","Guo Z","Liu L","Yang K","Zhou T","Wang H","Niu Y","Guo J","He Z","Hu H","Zhang X","Yu W","Cao S","Ye D","Wang W","Lou F","Wang Y","Zhu S"],"additional_accession":[]},"is_claimable":false,"name":"Letter to the Editor: clinical utility of urine DNA for noninvasive detection and minimal residual disease monitoring in urothelial carcinoma.","description":"Current methods for the early detection and minimal residual disease (MRD) monitoring of urothelial carcinoma (UC) are invasive and/or possess suboptimal sensitivity. We developed an efficient workflow named urine tumor DNA multidimensional bioinformatic predictor (utLIFE). Using UC-specific mutations and large copy number variations, the utLIFE-UC model was developed on a bladder cancer cohort (n = 150) and validated in The Cancer Genome Atlas (TCGA) bladder cancer cohort (n = 674) and an upper tract urothelial carcinoma (UTUC) cohort (n = 22). The utLIFE-UC model could discriminate 92.8% of UCs with 96.0% specificity and was robustly validated in the BLCA_TCGA and UTUC cohorts. Furthermore, compared to cytology, utLIFE-UC improved the sensitivity of bladder cancer detection (p < 0.01). I","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-05-29T19:43:56.67Z","creation":"2025-05-29T19:43:56.67Z"},"accession":"S-EPMC9898696","cross_references":{"pubmed":["36739413"],"doi":["10.1186/s12943-023-01729-7"]}}