Plasma Proteomic Profiling Was Used to Discover a Biochemical Recurrence Prediction Model for Prostate Cancer.

Area of Science:

• Proteomics and Cancer Biomarkers
• Molecular Oncology and Diagnostics

Background:

• Prostate cancer (PCa) is a leading male malignancy with limited minimally-invasive biomarkers for outcome prediction and monitoring.
• Existing methods like International Society of Urological Pathology (ISUP) grades and pathological stages have limitations in predicting PCa recurrence.

Purpose of the Study:

• To investigate the plasma proteomic profile in prostate cancer (PCa) patients for potential minimally-invasive biomarkers.
• To develop and validate a predictive model for biochemical recurrence in PCa using identified protein signatures.

Main Methods:

• Proteomic profiling of plasma from 222 PCa patients and 159 healthy controls.
• Integrative analysis of proteome data with clinical features (ISUP grades, PSA).
• Development of a 17-protein panel and a biochemical recurrence prediction model, validated by parallel reaction monitoring (PRM) assay.

Main Results:

• Identification of protein networks associated with ISUP grades and prostate-specific antigen (PSA).
• Classification of PCa into three subtypes (PCa-I, PCa-II, PCa-III) with distinct prognoses and molecular signatures.
• A 17-protein panel demonstrated superior prediction of biochemical recurrence compared to ISUP grades and pathological stages.

Conclusions:

• The plasma proteomic landscape of PCa provides a valuable resource for understanding disease biology and developing predictive tools.
• The developed 17-protein panel offers a promising, accurate, and minimally-invasive biomarker for predicting PCa biochemical recurrence.
• This study establishes a novel predictive model that enhances the management and monitoring of prostate cancer patients.