Immune Microenvironment and Response in Prostate Cancer Using Large Population Cohorts

Abstract

Immune microenvironment of prostate cancer (PCa) is implicated in disease progression. However, previous studies have not fully explored PCa immune microenvironment. This study used ssGSEA algorithm to explore expression levels of 53 immune terms in a combined PCa cohort (eight cohorts; 1,597 samples). The top 10 immune terms were selected based on the random forest analysis and used for immune-related risk score (IRS) calculation. Furthermore, we explored differences in clinical and genomic features between high and low IRS groups. An IRS signature based on the 10 immune terms showed high prediction potential for PCa prognosis. Patients in the high IRS group showed significantly higher percentage of immunotherapy response factors, implying that IRS is effective in predicting immunotherapy response rate. Furthermore, consensus clustering was performed to separate the population into three IRSclusters with different clinical outcomes. Patients in IRScluster3 showed the worst prognosis and highest immunotherapy response rate. On the other hand, patients in IRScluster2 showed better prognosis and low immunotherapy response rate. In addition, VGLL3, ANPEP, CD38, CCK, DPYS, CST2, COMP, CRISP3, NKAIN1, and F5 genes were differentially expressed in the three IRSclusters. Furthermore, CMap analysis showed that five compounds targeted IRS signature, thioridazine, trifluoperazine, 0175029-0000, trichostatin A, and fluphenazine. In summary, immune characteristics of PCa tumor microenvironment was explored and an IRS signature was constructed based on 10 immune terms. Analysis showed that this signature is a useful tool for prognosis and prediction of immunotherapy response rate of PCa.