Multi-omics biomarker pipeline reveals elevated levels of protein-glutamine gamma-glutamyltransferase 4 in seminal plasma of prostate cancer patients

Abstract

PurposeProstate-specific antigen, a blood serum biomarker of prostate cancer, lacks specificity and prognostic significance, so considerable efforts are devoted to developing novel biomarkers. Seminal plasma, due to its proximity to prostate, is a promising fluid for biomarker discovery and non-invasive diagnostics. In this study, we investigated if seminal plasma proteins could increase specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers.\n\nExperimental DesignTo select 148 most promising biomarker candidates, we combined proteins identified through five independent data mining or experimental approaches: tissue transcriptomics, seminal plasma proteomics, cell secretomics, tissue specificity and androgen regulation. A rigorous biomarker development pipeline based on targeted proteomics assays was designed to evaluate the most promising candidates.\n\nResultsWe qualified 77 and verified 19 proteins in seminal plasma of 67 negative biopsy and 155 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which could predict prostate cancer on biopsy and outperformed age and serum PSA. Machine-learning approaches also revealed improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an inhouse ELISA, TGM4 was up-regulated 3.7-fold (P=0.006) and revealed AUC 0.66 for detecting prostate cancer on biopsy for patients with serum PSA[≥]4 ng/mL and age[≥]50. Low levels of TGM4 (median 120 pg/mL) were detected in blood serum, but could not differentiate between negative biopsy, prostate cancer or prostate inflammation.\n\nConclusionsPerformance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.