Aberrant glycosylation has long been known to be associated with cancer, since it is involved in key mechanisms such as tumour onset, development and progression. This review will focus on protein glycosylation studies in cells, tissue, urine and serum in the context of prostate cancer. A dedicated section will cover the glycoforms of prostate specific antigen, the molecule that, despite some important limitations, is routinely tested for helping prostate cancer diagnosis. Our aim is to provide readers with an overview of mass spectrometry-based glycoproteomics of prostate cancer. From this perspective, the first part of this review will illustrate the main strategies for glycopeptide enrichment and mass spectrometric analysis. The molecular information obtained by glycoproteomic analysis performed by mass spectrometry has led to new insights into the mechanism linking aberrant glycosylation to cancer cell proliferation, migration and immunoescape.

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95–0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Background: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. Methods: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO₂ (titanium dioxide) strategy. Results: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). Conclusions: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Background: Heart failure (HF) is a clinical syndrome that involves multiple interconnected pathways. Circulating biomarkers in HF emerged as powerful tools for risk stratification, diagnostic confirmation, prognostic assessment, and monitoring of treatment efficacy. The aim of the present study was to evaluate circulating levels of biomarkers in elderly patients with improved HF ejection fraction, previously with left ventricular ejection fraction (LVEF) <40%, after six months of drug therapy optimisation. Methods: We enrolled 100 HFimpEF outpatients. All patients provided medical history and underwent physical examination at baseline and after six months of follow-up. The serum values of circulating biomarkers were assessed with an ELISA test. Proteomic analysis was performed on serum samples collected from a subset of 13 patients at baseline and after six months of follow-up. Results: At follow-up, we observed significant improvements in glycometabolic, renal and inflammatory profiles (p < 0.001). Proteomic analysis revealed selective changes in key cardiovascular (CV)-related proteins, such as insulin-like growth factor-binding protein 4 (IBP4), thrombospondin-4 (TSP4), intercellular adhesion molecule 1 (ICAM1), and syndecan-4 (SDC4). Conclusions: This study demonstrates significant improvements across multiple CV biomarkers after six months of therapy optimisation in HFimpEF patients, providing evidence for comprehensive therapeutic effects targeting inflammation, oxidative stress, neurohormonal activation, and thrombotic risk.

BACKGROUND: Heart failure (HF) is a clinical syndrome that involves multiple interconnected pathways. Circulating biomarkers in HF emerged as powerful tools for risk stratification, diagnostic confirmation, prognostic assessment, and monitoring of treatment efficacy. The aim of the present study was to evaluate circulating levels of biomarkers in elderly patients with HF improved ejection fraction, previously with left ventricular ejection fraction (LVEF) <40%, after 6 months of drug therapy optimisation. METHODS: We enrolled 100 HFimpEF outpatients. All patients underwent medical history and physical examination, at the baseline and after 6 months of follow-up. The serum values of circulating biomarkers were assessed with ELISA test. Proteomic analysis was performed on serum samples collected from a subset of 13 patients at baseline and after 6 months of follow-up. RESULTS: At follow-up, we observed significant improvements in glycometabolic, renal and inflammatory profile (p<0.001). Proteomic analysis revealed selective changes in key cardiovascular (CV)-related proteins, such as Insulin-like growth factor-binding protein 4 (IBP4) Thrombospondin4 (TSP4), Intercellular adhesion molecule 1 (ICAM1), Syndecan-4 (SDC4). CONCLUSIONS: This study demonstrates significant improvements across multiple CV biomarkers, after 6 months of therapy optimization, in HFimpEF patients, providing evidence for comprehensive therapeutic effects targeting inflammation, oxidative stress, neurohormonal activation, and thrombotic risk.