Prospective cohort study integrating plasma proteomics and machine learning for early risk prediction of prostate cancer.

Background: Early detection of prostate cancer (PCa) remains a clinical challenge. Plasma proteomics provides a non-invasive tool for identifying individuals at elevated risk prior to symptom onset or PSA elevation.

Methods: We quantified 1,463 plasma proteins in 23,825 PCa-free men from the UK Biobank (UKB). Participants were split into training and validation sets. Cox regression and Light Gradient Boosting Machine (LightGBM) with forward feature selection were used to identify and rank predictive proteins. Model performance was assessed by area under the receiver operating characteristic curve (AUC) in the validation set, and SHAP values were used to interpret feature contributions.

Results: TSPAN1 and GP2 consistently ranked as top predictors across all analyses. In the training set, both proteins remained significantly associated with PCa risk after Bonferroni correction in multivariable Cox models. LightGBM with forward selection further prioritized TSPAN1 and GP2 as key contributors, and SHAP analysis confirmed their dominant importance. In the validation set, a model combining TSPAN1, GP2, and demographic variables achieved an AUC of 0.728 for overall PCa prediction and 0.760 for 5-year risk. Based on Youden Index-derived thresholds, high-expression groups of TSPAN1 and GP2 were associated with hazard ratios of 1.75 and 1.60, respectively. Longitudinal profiling showed that TSPAN1 levels began rising approximately 9 years before diagnosis, while GP2 increased from 6 years prior.

Conclusions: TSPAN1 and GP2 are promising long-term predictive biomarkers for PCa. A streamlined proteomics-based model may enable individualized risk stratification and inform earlier, less invasive screening strategies.