<abstract xmlns="http://www.w3.org/1999/xhtml"> Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.</abstract>

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.

Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

Research Centre for Genetic Engineering and Biotechnology "Georgi D Efremov", Macedonian Academy of Sciences and Arts, Skopje, R. Macedonia

PRILOZI

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly...

Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

Published Online: Jun 16, 2015

Page range: 5 - 36

DOI: https://doi.org/10.1515/prilozi-2015-0027

© 2015

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.