前列腺癌
工作流程
外体
泌尿系统
前列腺
癌症
微泡
化学
医学
计算机科学
内科学
生物化学
小RNA
基因
数据库
作者
Shuai Qiu,Yue Li,Zheng Zhang,Chunchang Li,Haoyu Wang,Ao Chen,Yan Zhi,Yan Liu,Zifei Li,Hua Huang,Yi Liu,Yiqi Seow,Ruibing Chen,Jinhong Guo,Simeng Wen,Jing Tian,Hongtuan Zhang,Ranlu Liu,Gang Han,Baolong Wang
出处
期刊:iScience
[Cell Press]
日期:2025-01-27
卷期号:28 (2): 111896-111896
标识
DOI:10.1016/j.isci.2025.111896
摘要
Clear differentiation of high-grade and clinically insignificant prostate cancer (PCa) is critical for clinical decision-making. Here, we developed a proprietary urinary exosome isolation approach (EVLatch) and established a facile diagnostic workflow. We discovered that EEF1A1 levels, abundantly expressed on urinary exosomes, positively correlate to urinary exosome counts irrespective of source and collection time and demonstrated that EEF1A1 enables in-assay quantification of urinary exosomes. Importantly, a prostate cancer urinary EVLatch-based artificial intelligence diagnostics (PURE-AID) classification system utilizing PCA3, HOXC6, and DLX1 as targets with SPDEF for reference and EEF1A1 for quality checking, trained on 271 patients, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 in the test set of 351 patients. Combination of PURE-AID with prostate-specific antigen (PSA) and age increases AUROC to 0.80 and reduces 54.3% of unnecessary biopsies with 86.8% sensitivity. Our study provides a new classification system for differentiating high-grade PCa in a workflow- and patient-friendly manner.
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