Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Abstract

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.

Figure 1

ML-based workflow.

Figure 2

Key steps of our workflow: (A) EPS-urine sample collection and FASP protocol, (B) elaboration of the sample card by DDA analysis, (C) high-pH reversed-phase C18 fractionation for spectral library generation and DIA analysis by Spectronaut, and (D) bioinformatics analysis by ML models. Created with BioRender.com.

Figure 3

Ranking plot of identified proteins by DIA analysis. Prostate-specific proteins (33) are indicated in violet, while the two components of our model (SPARC and Sema7A) are in purple.

Figure 4

This panel shows the dot plots relative to univariate analysis for the two proteins (SPARC and Sema7a) and the two clinical variables (FT ratio and prostate gland size); all these variables showed, on average, a significant decrease (p-value < 0.05) in PCa samples with respect to BPH.