Background. To evaluate the diagnostic potential of [-2] proPSA (p2PSA), %p2PSA, Prostate Health Index (phi), and phi density (PHID) as independent biomarkers and in composition of multivariable models in predicting high-grade prostatic intraepithelial neoplasia (HGPIN) and overall and clinically significant prostate cancer (PCa). Methods. 210 males scheduled for prostate biopsy with total PSA (tPSA) range 2-10 ng/mL and normal digital rectal examination were enrolled in the prospective study. Blood samples to measure tPSA, free PSA (fPSA), and p2PSA were collected immediately before 12-core prostate biopsy. Clinically significant PCa definition was based on Epstein’s criteria or ISUP grade≥2 at biopsy. Results. PCa has been diagnosed in 112 (53.3%) patients. Epstein significant and ISUP grade≥2 PCa have been identified in 81 (72.3%) and 40 (35.7%) patients, respectively. Isolated HGPIN at biopsy have been identified in 24 (11.4%) patients. Higher p2PSA and its derivative mean values were associated with PCa. At 90% sensitivity, PHID with cut-off value of 0.54 have demonstrated the highest sensitivity of 35.7% for overall PCa detection, so PHID and phi with cut-off values of 33.2 and 0.63 have demonstrated the specificity of 34.7% and 34.1% for ISUP grade≥2 PCa detection at biopsy, respectively. In univariate ROC analysis, PHID with AUC of 0.77 and 0.80 was the most accurate predictor of overall and Epstein significant PCa, respectively, so phi with AUC of 0.77 was the most accurate predictor of ISUP grade≥2 PCa at biopsy. In multivariate logistic regression analysis, phi improved diagnostic accuracy of multivariable models by 5% in predicting ISUP grade≥2 PCa. Conclusions. PHID and phi have shown the greatest specificity at 90% sensitivity in predicting overall and clinically significant PCa and would lead to significantly avoid unnecessary biopsies. PHID is the most accurate predictor of overall and Epstein significant PCa, so phi is the most accurate predictor of ISUP grade≥2 PCa. phi significantly improves the diagnostic accuracy of multivariable models in predicting ISUP grade≥2 PCa.

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Advance in molecular biology and the new technologies for biomedical research are being rapidly introduced into the research of complex pathologies worldwide. Implementation of these technologies, however, needs substantial financial resources for the equipment and for training the specialists. The rapid development of biomedical research over the past decade increases the risk of moral ageing of the implemented technologies and raises doubts as to whether countries with limited financial resources could afford them. In this article, we share our institutional experience in the implementation of post-genomic technologies in cancer research in Lithuania and stress the need of modern infrastructure in biomedical research, despite the needed efforts and associated risks.

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