Henry A Adeola1,2, Nelson C Soares2, Juliano D Paccez1,2, Lisa Kaestner3, Jonathan M Blackburn2, Luiz F Zerbini1,2. 1. International Centre for Genetic Engineering and Biotechnology, University of Cape Town, Cape Town, South Africa. 2. Faculty of Health Sciences, Division of Medical Biochemistry, Institute of Infectious Diseases & Molecular Medicine, University of Cape Town, Cape Town, South Africa. 3. Urology Department, Grootes Schuur Hospital, Cape Town, South Africa.
Abstract
PURPOSE: Improvement in diagnostic accuracy of prostate cancer (PCa) progression using MS-based methods to analyze biomarkers in our African, Caucasian, and Mixed Ancestry patients can advance early detection and treatment monitoring. EXPERIMENTAL DESIGN: MS-based proteomic analysis of pooled (N = 36) and individual samples (N = 45) of PCa, benign prostatic hyperplasia, normal healthy controls, and patients with other uropathies was used to identify differences in proteomics profile. Samples were analyzed for potential biomarkers and proteome coverage in African, Caucasian, and Mixed Ancestry PCa patients. RESULTS: A total of 1102 and 5595 protein groups and nonredundant peptides, respectively, were identified in the pooling experiments (FDR = 0.01). Twenty potential biomarkers in PCa were identified and fold differences ± 2SD were observed in 17 proteins using intensity-based absolute quantification. Analysis of 45 individual samples yielded 1545 and 9991 protein groups and nonredundant peptides, respectively. Seventy-three (73) proteins groups, including existing putative PCa biomarkers, were found to be potential biomarkers of PCa by label-free quantification and demonstrated ethnic trends within our PCa cohort. CONCLUSION AND CLINICAL RELEVANCE: Urinary proteomics is a promising route to PCa biomarker discovery and may serve as source of ethnic-related biomarkers of PCa.
PURPOSE: Improvement in diagnostic accuracy of prostate cancer (PCa) progression using MS-based methods to analyze biomarkers in our African, Caucasian, and Mixed Ancestry patients can advance early detection and treatment monitoring. EXPERIMENTAL DESIGN: MS-based proteomic analysis of pooled (N = 36) and individual samples (N = 45) of PCa, benign prostatic hyperplasia, normal healthy controls, and patients with other uropathies was used to identify differences in proteomics profile. Samples were analyzed for potential biomarkers and proteome coverage in African, Caucasian, and Mixed Ancestry PCa patients. RESULTS: A total of 1102 and 5595 protein groups and nonredundant peptides, respectively, were identified in the pooling experiments (FDR = 0.01). Twenty potential biomarkers in PCa were identified and fold differences ± 2SD were observed in 17 proteins using intensity-based absolute quantification. Analysis of 45 individual samples yielded 1545 and 9991 protein groups and nonredundant peptides, respectively. Seventy-three (73) proteins groups, including existing putative PCa biomarkers, were found to be potential biomarkers of PCa by label-free quantification and demonstrated ethnic trends within our PCa cohort. CONCLUSION AND CLINICAL RELEVANCE: Urinary proteomics is a promising route to PCa biomarker discovery and may serve as source of ethnic-related biomarkers of PCa.
Authors: Jan Muntel; Yue Xuan; Sebastian T Berger; Lukas Reiter; Richard Bachur; Alex Kentsis; Hanno Steen Journal: J Proteome Res Date: 2015-10-22 Impact factor: 4.466
Authors: Ling Hao; Samuel Thomas; Tyler Greer; Chad M Vezina; Sagar Bajpai; Arya Ashok; Angelo M De Marzo; Charles J Bieberich; Lingjun Li; William A Ricke Journal: Am J Physiol Renal Physiol Date: 2019-04-17