4.8 Article

Immune Microenvironment and Response in Prostate Cancer Using Large Population Cohorts

Journal

FRONTIERS IN IMMUNOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fimmu.2021.686809

Keywords

prostate cancer; immune; prognosis; immunotherapy; response rate

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Funding

  1. National Natural Science Foundation of China

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The study explored the immune characteristics of the prostate cancer tumor microenvironment and constructed an IRS signature based on 10 immune terms. Analysis revealed that this signature is a useful tool for predicting prognosis and immunotherapy response rate of prostate cancer. Patients in the high IRS group showed significantly higher immunotherapy response factors, indicating the effectiveness of IRS in predicting immunotherapy response rate.
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.

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