To further investigate the combinatorial utility of genes associated with TET2 for improved clinical decision-making, we used a backwards feature selection strategy to generate an optimal 38-gene random forest model with binary outcome, in a training cohort (the Moreno cohort) of 100 formalin-fixed, paraffin-embedded prostate cancer samples [18], and validated this model in the Cancer Genome Atlas (TCGA) prostate tumor dataset [19]. This evidence concerns the gene TET2 and prostate cancer.