Protein fingerprinting can be used on serum samples to help accurately distinguish between cancer of the prostate, benign prostate hyperplasia (BPH) and healthy tissue. Proteins are detected by a protein-chip array and an artificial intelligence learning algorithm is used to reduce the number of proteins found down to the number that are required to differentiate prostate cancer from noncancer cohorts. 2DGE and mass spectrometry have been used to study serum proteins expressed in patients with BPH and those with high-grade prostatic intraepithelial neoplasm (Gu et al. 2008). Serum amyloid A was found to be expressed in the cancer patients, but weakly or not at all in those with BPH.
Biomarkers, such as autoantibody signatures, may improve the early detection of prostate cancer. Autoantibodies against peptides derived from prostate-cancer tissue could be used as the basis for a screening test for prostate cancer. Fetuin-A, an established tumor antigen in several types of cancer, has been implicated in prostate cancer and autoantibodies specific for fetuin-A show usefulness as a prognostic indicator for prostate cancer patients prone to progress to metastatic disease (Mintz et al. 2015).
Serum-fingerprinting method has a higher specificity than PSA test for differentiating prostate cancer from BPH and unaffected healthy men. This approach can substantially reduce unnecessary prostate biopsies. Another advantage of this technique is that prostate cancer might be detected earlier than with PSA screening. The next step is to identify other biomarkers that can differentiate aggressive cancers from nonaggressive cancers, to make this classification system for early detection as effective as possible.
Concluding Remarks on Biomarkers of Prostate Cancer
Despite many promising candidates, no single biomarker has satisfied the criteria as the ideal biomarker. Limited clinical use of IL-6, TGF-p1 and PCA3 has started, and further widespread availability of these tests is expected in the near future. The trend is to use artificial neural networks and panels of biomarkers instead of individual assays. Although PSA has some well-known limitations, it remains the best biomarker available for prostate cancer when used in conjunction with nomograms or risk calculators.
There is a tremendous need for better prognostic biomarkers in prostate cancer to assist in the identification of patients with aggressive forms of the disease who can potentially benefit from earlier and more intensive forms of treatment. Potential biomarkers of prostatic cancer include caveolin-1, p-Akt, p27, the met oncogene, Ki67 (MIB-1), 8q24 over-expression, polycomb protein EZH2, plasma TGF-B1 and IL-6 among others.