A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer
Objective: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized i...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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China Anti-Cancer Association
2021-05-01
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| Series: | Cancer Biology & Medicine |
| Subjects: | |
| Online Access: | http://www.cancerbiomed.org/index.php/cocr/article/view/1861 |
| _version_ | 1818436627167444992 |
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| author | Denise Drago Annapaola Andolfo Ettore Mosca Alessandro Orro Luigi Nocera Vito Cucchiara Matteo Bellone Francesco Montorsi Alberto Briganti |
| author_facet | Denise Drago Annapaola Andolfo Ettore Mosca Alessandro Orro Luigi Nocera Vito Cucchiara Matteo Bellone Francesco Montorsi Alberto Briganti |
| author_sort | Denise Drago |
| collection | DOAJ |
| description | Objective: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). Methods: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy. Results: We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1–2 and 4–5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3). Conclusions: A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies. |
| first_indexed | 2024-12-14T17:11:47Z |
| format | Article |
| id | doaj.art-50aeba4fda354560855a428811f19749 |
| institution | Directory Open Access Journal |
| issn | 2095-3941 |
| language | English |
| last_indexed | 2024-12-14T17:11:47Z |
| publishDate | 2021-05-01 |
| publisher | China Anti-Cancer Association |
| record_format | Article |
| series | Cancer Biology & Medicine |
| spelling | doaj.art-50aeba4fda354560855a428811f197492022-12-21T22:53:33ZengChina Anti-Cancer AssociationCancer Biology & Medicine2095-39412021-05-0118260461510.20892/j.issn.2095-3941.2020.0617A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancerDenise Drago0Annapaola Andolfo1Ettore Mosca2Alessandro Orro3Luigi Nocera4Vito Cucchiara5Matteo Bellone6Francesco Montorsi7Alberto Briganti8ProMeFa, Proteomics and Metabolomics Facility, Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyProMeFa, Proteomics and Metabolomics Facility, Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyInstitute of Biomedical Technologies, National Research Council (CNR), Milan 20090, ItalyInstitute of Biomedical Technologies, National Research Council (CNR), Milan 20090, ItalyDepartment of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyDepartment of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyDivision of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyDepartment of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyDepartment of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, ItalyObjective: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). Methods: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy. Results: We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1–2 and 4–5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3). Conclusions: A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.http://www.cancerbiomed.org/index.php/cocr/article/view/1861prostatecancereps-urinemetabolomicspredictiondiagnosis |
| spellingShingle | Denise Drago Annapaola Andolfo Ettore Mosca Alessandro Orro Luigi Nocera Vito Cucchiara Matteo Bellone Francesco Montorsi Alberto Briganti A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer Cancer Biology & Medicine prostate cancer eps-urine metabolomics prediction diagnosis |
| title | A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| title_full | A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| title_fullStr | A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| title_full_unstemmed | A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| title_short | A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| title_sort | novel expressed prostatic secretion eps urine metabolomic signature for the diagnosis of clinically significant prostate cancer |
| topic | prostate cancer eps-urine metabolomics prediction diagnosis |
| url | http://www.cancerbiomed.org/index.php/cocr/article/view/1861 |
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