Prostate cancer (PCa) is annually the most frequently
diagnosed
cancer in the male population. To date, the diagnostic path for PCa
detection includes the dosage of serum prostate-specific antigen (PSA)
and the digital rectal exam (DRE). However, PSA-based screening has
insufficient specificity and sensitivity; besides, it cannot discriminate
between the aggressive and indolent types of PCa. For this reason,
the improvement of new clinical approaches and the discovery of new
biomarkers are necessary. In this work, expressed prostatic secretion
(EPS)-urine samples from PCa patients and benign prostatic hyperplasia
(BPH) patients were analyzed with the aim of detecting differentially
expressed proteins between the two analyzed groups. To map the urinary
proteome, EPS-urine samples were analyzed by data-independent acquisition
(DIA), a high-sensitivity method particularly suitable for detecting
proteins at low abundance. Overall, in our analysis, 2615 proteins
were identified in 133 EPS-urine specimens obtaining the highest proteomic
coverage for this type of sample; of these 2615 proteins, 1670 were
consistently identified across the entire data set. The matrix containing
the quantified proteins in each patient was integrated with clinical
parameters such as the PSA level and gland size, and the complete
matrix was analyzed by machine learning algorithms (by exploiting
90% of samples for training/testing using a 10-fold cross-validation
approach, and 10% of samples for validation). The best predictive
model was based on the following components: semaphorin-7A (sema7A),
secreted protein acidic and rich in cysteine (SPARC), FT ratio, and
prostate gland size. The classifier could predict disease conditions
(BPH, PCa) correctly in 83% of samples in the validation set. Data
are available via ProteomeXchange with the identifier PXD035942.