Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.