Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review.

Cancer Manag Res

Biomedical Sciences Research Institute, Ulster University, Coleraine BT52 1SA, Northern Ireland.

Published: July 2020

AI Article Synopsis

  • Current methods for prostate cancer prediction rely on tPSA levels and digital rectal exams, but these are not very reliable, risking over-diagnosis and unnecessary procedures.
  • A review of studies from 2009 to 2019 highlights numerous potential biomarkers in various biological samples like urine and tissue that could help differentiate between benign prostatic hyperplasia (BPH) and prostate cancer.
  • To be useful in clinical settings, these biomarkers need further validation, and using multivariate panels could enhance risk prediction by combining these biomarkers with clinical data, improving patient care in primary care settings.

Article Abstract

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335899PMC
http://dx.doi.org/10.2147/CMAR.S250829DOI Listing

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