Serum Untargeted Metabolomics Integrated with SHAP-Based Machine Learning for Multiclass Stratification of Prostate Cancer, Prostatitis, and Benign Prostatic Hyperplasia.

BACKGROUND

Prostate cancer, benign prostatic hyperplasia, and prostatitis share substantial overlap in clinical symptoms and biological characteristics, which hampers non-invasive and early differential diagnosis. Untargeted metabolomics enables comprehensive profiling of disease-associated metabolic alterations; however, its high dimensionality and strong feature correlations challenge conventional statistical approaches.

METHODS

To address this, we analyzed serum untargeted LC-MS data following standardized preprocessing. We adopted a nested cross-validation strategy to evaluate various feature selection methods and machine learning classifiers, ultimately determining that multiclass LASSO regression was the most effective feature selection approach.

RESULTS

An optimized Random Forest model demonstrated strong, superior performance in distinguishing between prostate cancer, prostatitis, benign prostatic hyperplasia, and healthy controls (out-of-fold accuracy: 93.8%; macro-F1: 0.937). Additionally, SHAP (SHapley Additive exPlanations) analysis translated feature statistical importance into biologically meaningful modules, revealing that distinct, disease-specific patterns of metabolic reprogramming drove the model's robust multiclass discrimination.

CONCLUSIONS

This study demonstrates the value of integrating serum untargeted metabolomics with advanced explainable machine learning for effective multiclass differentiation of major prostate diseases, providing a promising non-invasive framework for diagnostic stratification and metabolic biomarker discovery.