Differentially expressed glycoproteins in pre‐ and post‐digital rectal examination urine samples for detecting aggressive prostate cancer

Wang, Yuefan; Lih, Tung-Shing Mamie; Höti, Naseruddin; Sokoll, Lori J.; Chesnut, Gregory T.; Petrovics, György; Kohaar, Indu; Zhang, Hui

doi:10.1002/pmic.202200023

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…This result is comparable to the one obtained by Wang et al [35] using DIA-MS analysis of nonglycosylated serum proteins for discovery and ELISA assay for validation on two proteins (SPP1 and CP). To address this issue, to date, more promising results have been obtained by performing glycoproteomic analysis on urine as biological specimen, collected either before or after digital rectal exam [36][37][38][39]. In these studies, AUC values up to 0.85 have been reported in the effort to classify AG and NAG disease.…”

Section: Discussionmentioning

confidence: 99%

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables

Gabriele,

Aracri,

Prestagiacomo

et al. 2023

Clin Proteom

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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.

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Section: Discussionmentioning

confidence: 99%

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables

Gabriele,

Aracri,

Prestagiacomo

et al. 2023

Clin Proteom

View full text Add to dashboard Cite

show abstract

“…applied DIA to study small amounts of extracellular matrix of lung cancer tissue specimens [6], while Wang et al. analyzed enriched glycoproteins in urine samples from prostate cancer patients [7]. Kverneland et al.…”

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confidence: 99%