Machine-Learning-Based Prediction of Biochemical Recurrence in Prostate Cancer Integrating Fatty-Acid Metabolism and Stemness.

ABSTRACT: Prostate cancer (PCa) is a common malignancy among men worldwide. After radical prostatectomy (RP) and radical radiotherapy (RT), patients may experience biochemical recurrence (BCR) of prostate cancer, indicating disease progression. Therefore, it is meaningful to predict and accurately assess the risk of BCR, and a machine-learning-based-model for BCR prediction in PCa based on fatty-acid metabolism and cancer-cell stemness was developed. A stemness prediction model and ssGSEA (single-sample gene set enrichment analysis) empirical cumulative distribution function algorithm were used to score the stemness scoring (mRNAsi) and fatty-acid metabolism of prostate-cancer samples, respectively, and further analysis showed that the two scores of the samples were positively correlated. Based on W

SUBMITTER: Dai Z 

PROVIDER: S-EPMC12841470 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature