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

IntroductionProstate 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 PCa, it has also led to a dramatic increase in overdiagnosing indo...

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Autores principales:	Yibei Dai, Yiyun Wang, Ying Cao, Pan Yu, Lingyu Zhang, Zhenping Liu, Ying Ping, Danhua Wang, Gong Zhang, Yiwen Sang, Xuchu Wang, Zhihua Tao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	extracellular vesicle EpCAM chemiluminescent immunoassay prostate cancer multivariate diagnostic model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/09618bc4d7654c6f82daebeed0d607dc
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spelling	oai:doaj.org-article:09618bc4d7654c6f82daebeed0d607dc2021-11-30T19:16:39ZA Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis2234-943X10.3389/fonc.2021.777684https://doaj.org/article/09618bc4d7654c6f82daebeed0d607dc2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.777684/fullhttps://doaj.org/toc/2234-943XIntroductionProstate 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 PCa, 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 epithelial cell adhesion molecule (EpCAM)-CD9–positive extracellular vesicles (EVs) (uEVEpCAM-CD9) to improve the diagnosis of PCa.MethodsWe 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 PCa 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.ResultsResults showed that uEVEpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEVEpCAM-CD9 was 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 PCa [area under the curve (AUC) = 0.952, P < 0.0001]. When applied to a validation test (N = 77), the model achieved an AUC 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.ConclusionsThe 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 transrectal ultrasound (TRUS) biopsy.Yibei DaiYiyun WangYing CaoPan YuLingyu ZhangZhenping LiuYing PingDanhua WangGong ZhangYiwen SangXuchu WangZhihua TaoFrontiers Media S.A.articleextracellular vesicleEpCAMchemiluminescent immunoassayprostate cancermultivariate diagnostic modelNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	extracellular vesicle EpCAM chemiluminescent immunoassay prostate cancer multivariate diagnostic model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	extracellular vesicle EpCAM chemiluminescent immunoassay prostate cancer multivariate diagnostic model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yibei Dai Yiyun Wang Ying Cao Pan Yu Lingyu Zhang Zhenping Liu Ying Ping Danhua Wang Gong Zhang Yiwen Sang Xuchu Wang Zhihua Tao A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
description	IntroductionProstate 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 PCa, 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 epithelial cell adhesion molecule (EpCAM)-CD9–positive extracellular vesicles (EVs) (uEVEpCAM-CD9) to improve the diagnosis of PCa.MethodsWe 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 PCa 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.ResultsResults showed that uEVEpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEVEpCAM-CD9 was 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 PCa [area under the curve (AUC) = 0.952, P < 0.0001]. When applied to a validation test (N = 77), the model achieved an AUC 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.ConclusionsThe 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 transrectal ultrasound (TRUS) biopsy.
format	article
author	Yibei Dai Yiyun Wang Ying Cao Pan Yu Lingyu Zhang Zhenping Liu Ying Ping Danhua Wang Gong Zhang Yiwen Sang Xuchu Wang Zhihua Tao
author_facet	Yibei Dai Yiyun Wang Ying Cao Pan Yu Lingyu Zhang Zhenping Liu Ying Ping Danhua Wang Gong Zhang Yiwen Sang Xuchu Wang Zhihua Tao
author_sort	Yibei Dai
title	A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
title_short	A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
title_full	A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
title_fullStr	A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
title_full_unstemmed	A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis
title_sort	multivariate diagnostic model based on urinary epcam-cd9-positive extracellular vesicles for prostate cancer diagnosis
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/09618bc4d7654c6f82daebeed0d607dc
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A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis

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