AUTHOR=Dai Yibei , Wang Yiyun , Cao Ying , Yu Pan , Zhang Lingyu , Liu Zhenping , Ping Ying , Wang Danhua , Zhang Gong , Sang Yiwen , Wang Xuchu , Tao Zhihua 

TITLE=A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.777684

DOI=10.3389/fonc.2021.777684

ISSN=2234-943X

ABSTRACT=Introduction: Prostate cancer (PCa) is one of the most frequently diagnosed cancers and the leading cause of cancer death in males worldwide. Although prostate-specific antigen (PSA) screening has considerably improved the detection of prostate cancer, it has also led to a dramatic increase in overdiagnosing indolent disease due to its low specificity. This study aimed to develop and validate a multivariate diagnostic model based on the urinary EpCAM-CD9 positive EVs (uEVEpCAM-CD9) to improve the diagnosis of prostate cancer.
Methods: We investigated the performance of uEVEpCAM-CD9 from urine samples of 193 participants (112 PCa patients, 55 benign prostatic hyperplasia patients and 26 healthy donors) to diagnose prostate cancer using our laboratory-developed chemiluminescent immunoassay. We applied machine learning to training sets and subsequently evaluated the multivariate diagnostic model based on uEVEpCAM-CD9 in validation sets.
Results: Results showed uEVEpCAM-CD9 were able to distinguish PCa from controls and a significant decrease of uEVEpCAM-CD9 were observed after prostatectomy. We further used a training set (N=116) and constructed an exclusive multivariate diagnostic model based on uEVEpCAM-CD9, PSA and other clinical parameters, which showed an enhanced diagnostic sensitivity and specificity and performed excellently to diagnose prostate cancer (AUC = 0.952, P<0.0001). When applied to a validation test (N= 77), the model achieved an area under the curve of 0.947 (P<0.0001). Moreover, this diagnostic model also exhibited a superior diagnostic performance (AUC = 0.917, P<0.0001) over PSA (AUC = 0.712, P=0.0018) at the PSA gray zone. 
Conclusions: The multivariate model based on uEVEpCAM-CD9 achieved a notable diagnostic performance to diagnose PCa. In the future, this model may potentially be used to better select patients for prostate TRUS biopsy.