The Landscape of Immune Cells Infiltrating in Prostate Cancer

Abstract

Background: This study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa. Methods: Infiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa. Results: Ten immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the five- and 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible. Conclusions: This study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients’ prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.