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用于尿液中前列腺癌无创检测的utLIFE-PC算法的开发与验证:一项前瞻性观察研究。

Development and validation of the utLIFE-PC algorithm for noninvasive detection of prostate cancer in urine: A prospective, observational study.

作者信息

Han Sujun, Wang Mingshuai, Wang Yong, Wu Junlong, Guo Zhaoxia, Wang Huina, Liu Ranlu, Qiu Xiaofu, Hu Linjun, Bi Jianbin, Yan Weigang, An Hengqing, Zhang Gejun, Zhi Yi, Chen Zhiyuan, Chen Libin, Liu Lei, Cheng Huanqing, Zhu Shuaipeng, Wang Meng, Zhang Yanrui, Liu Xiao, Lou Feng, Cao Shanbo, Ye Dingwei, Niu Yuanjie, Xing Nianzeng

机构信息

Department of Urology and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Department of Urology, The Second Affiliated Hospital of Tianjin Medical University, Tianjin, China.

出版信息

Cell Rep Med. 2024 Dec 17;5(12):101870. doi: 10.1016/j.xcrm.2024.101870. Epub 2024 Dec 9.

Abstract

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.

摘要

过度活检是前列腺癌(PCa)诊断中的一个严重健康问题。我们开发了一种尿液肿瘤DNA多维生物信息算法utLIFE,以避免不必要的活检。目标是识别所有或具有临床意义的PCa。在我们研究招募的801名参与者中,630名被选作后续分析。在训练队列(n = 237)中,utLIFE-PC在接受者操作特征曲线(AUC)下的面积为0.967,在95%特异性时灵敏度为85.57%。在独立前瞻性验证队列(n = 343)中,utLIFE-PC的AUC为0.929,灵敏度为84.24%,特异性为93.26%。值得注意的是,在分级组(GG)≥2和GG≥3的患者中,该检测的灵敏度仍然很高(分别为85.33%和87.10%)。该模型表现优于前列腺特异性抗原(PSA)(p < 0.001)或单维生物标志物(甲基化,p < 0.001;拷贝数变异[CNV],p < 0.001;突变,p < 0.001)。utLIFE-PC模型有可能优化PCa诊断过程并避免不必要的活检。本研究已在中国临床试验注册中心注册:ChiCTR2300071837。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f82/11722088/cd3fcdf6c835/fx1.jpg

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