{"title":"尿中前列腺癌无创检测的utLIFE-PC算法的开发和验证:一项前瞻性观察性研究。","authors":"Sujun Han, Mingshuai Wang, Yong Wang, Junlong Wu, Zhaoxia Guo, Huina Wang, Ranlu Liu, Xiaofu Qiu, Linjun Hu, Jianbin Bi, Weigang Yan, Hengqing An, Gejun Zhang, Yi Zhi, Zhiyuan Chen, Libin Chen, Lei Liu, Huanqing Cheng, Shuaipeng Zhu, Meng Wang, Yanrui Zhang, Xiao Liu, Feng Lou, Shanbo Cao, Dingwei Ye, Yuanjie Niu, Nianzeng Xing","doi":"10.1016/j.xcrm.2024.101870","DOIUrl":null,"url":null,"abstract":"<p><p>Overbiopsy is a serious health issue in prostate cancer (PCa) diagnostics. We have developed a urine tumor DNA multidimensional bioinformatic algorithm, utLIFE, to avoid unnecessary biopsy. The objective is to recognize all or clinically significant PCa. Of the 801 participants recruited in our study, 630 are selected for subsequent analysis. In the training cohort (n = 237), utLIFE-PC gets an area under the receiver operating characteristic curve (AUC) of 0.967 and a sensitivity of 85.57% at 95% specificity. In the independent prospective validation cohort (n = 343), utLIFE-PC has an AUC of 0.929, sensitivity of 84.24%, and specificity of 93.26%. Notably, in patients with ≥grade group (GG)2 and ≥GG3, the assay's sensitivity is still excellent (85.33% and 87.10%, respectively). The model shows better performance than prostate-specific antigen (PSA) (p < 0.001) or the single-dimensional biomarkers (methylation, p < 0.001; copy-number variations [CNVs], p < 0.001; mutation, p < 0.001). The utLIFE-PC model can potentially optimize the PCa diagnostic process and avoid unnecessary biopsies. This study was registered at Chinese Clinical Trial Registry: ChiCTR2300071837.</p>","PeriodicalId":9822,"journal":{"name":"Cell Reports Medicine","volume":" ","pages":"101870"},"PeriodicalIF":11.7000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11722088/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and validation of the utLIFE-PC algorithm for noninvasive detection of prostate cancer in urine: A prospective, observational study.\",\"authors\":\"Sujun Han, Mingshuai Wang, Yong Wang, Junlong Wu, Zhaoxia Guo, Huina Wang, Ranlu Liu, Xiaofu Qiu, Linjun Hu, Jianbin Bi, Weigang Yan, Hengqing An, Gejun Zhang, Yi Zhi, Zhiyuan Chen, Libin Chen, Lei Liu, Huanqing Cheng, Shuaipeng Zhu, Meng Wang, Yanrui Zhang, Xiao Liu, Feng Lou, Shanbo Cao, Dingwei Ye, Yuanjie Niu, Nianzeng Xing\",\"doi\":\"10.1016/j.xcrm.2024.101870\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Overbiopsy is a serious health issue in prostate cancer (PCa) diagnostics. We have developed a urine tumor DNA multidimensional bioinformatic algorithm, utLIFE, to avoid unnecessary biopsy. The objective is to recognize all or clinically significant PCa. Of the 801 participants recruited in our study, 630 are selected for subsequent analysis. In the training cohort (n = 237), utLIFE-PC gets an area under the receiver operating characteristic curve (AUC) of 0.967 and a sensitivity of 85.57% at 95% specificity. In the independent prospective validation cohort (n = 343), utLIFE-PC has an AUC of 0.929, sensitivity of 84.24%, and specificity of 93.26%. Notably, in patients with ≥grade group (GG)2 and ≥GG3, the assay's sensitivity is still excellent (85.33% and 87.10%, respectively). The model shows better performance than prostate-specific antigen (PSA) (p < 0.001) or the single-dimensional biomarkers (methylation, p < 0.001; copy-number variations [CNVs], p < 0.001; mutation, p < 0.001). The utLIFE-PC model can potentially optimize the PCa diagnostic process and avoid unnecessary biopsies. This study was registered at Chinese Clinical Trial Registry: ChiCTR2300071837.</p>\",\"PeriodicalId\":9822,\"journal\":{\"name\":\"Cell Reports Medicine\",\"volume\":\" \",\"pages\":\"101870\"},\"PeriodicalIF\":11.7000,\"publicationDate\":\"2024-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11722088/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell Reports Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xcrm.2024.101870\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Reports Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.xcrm.2024.101870","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Development and validation of the utLIFE-PC algorithm for noninvasive detection of prostate cancer in urine: A prospective, observational study.
Overbiopsy is a serious health issue in prostate cancer (PCa) diagnostics. We have developed a urine tumor DNA multidimensional bioinformatic algorithm, utLIFE, to avoid unnecessary biopsy. The objective is to recognize all or clinically significant PCa. Of the 801 participants recruited in our study, 630 are selected for subsequent analysis. In the training cohort (n = 237), utLIFE-PC gets an area under the receiver operating characteristic curve (AUC) of 0.967 and a sensitivity of 85.57% at 95% specificity. In the independent prospective validation cohort (n = 343), utLIFE-PC has an AUC of 0.929, sensitivity of 84.24%, and specificity of 93.26%. Notably, in patients with ≥grade group (GG)2 and ≥GG3, the assay's sensitivity is still excellent (85.33% and 87.10%, respectively). The model shows better performance than prostate-specific antigen (PSA) (p < 0.001) or the single-dimensional biomarkers (methylation, p < 0.001; copy-number variations [CNVs], p < 0.001; mutation, p < 0.001). The utLIFE-PC model can potentially optimize the PCa diagnostic process and avoid unnecessary biopsies. This study was registered at Chinese Clinical Trial Registry: ChiCTR2300071837.
Cell Reports MedicineBiochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
15.00
自引率
1.40%
发文量
231
审稿时长
40 days
期刊介绍:
Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine.
Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.