Prognostic and Predictive Value of Machine Learning-Based Biomarker and Pathomics Signatures in Patients With Prostate Cancer.

ABSTRACT: Recurrence and the potential development of castration resistance after radical prostatectomy (RP) are significant challenges in the management of prostate cancer (PCa). Despite the development of advanced prognostic models, few have been clinically applied. Five machine learning algorithms (LASSO, RSF, SVM-RFE, Boruta, and XGBoost) were used to identify biomarkers for PCa using transcriptome data from multicenters (TCGA, MSKCC, DKFZ, and GSE70770) for constructing and validating the metastasis-associated prognostic risk score (MAPRS), which revealed the molecular biological heterogeneity and was confirmed with in-house histopathological samples. The pathomics score (PSpc), derived from a machine learning framework (XGBoost, RSF, GBM, plsRCox, CoxBoost, Enet, Ridge, LASSO, SVM, and superPC

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12485890 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature