Abstract
OBJECTIVES: To identify immunosuppressive neutrophil subsets in patients with prostate cancer (PCa) and construct a risk prediction model for prognosis and immunotherapy response of the patients based on these neutrophil subsets. METHODS: Single-cell and transcriptome data from PCa patients were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Neutrophil subsets in PCa were identified through unsupervised clustering, and their biological functions and effects on immune regulation were analyzed by functional enrichment, cell interaction, and pseudo-time series analyses. Lasso-Cox regression was utilized to construct a prognostic risk model based on the immunosuppressive neutrophil subsets, and survival analysis and ROC curve analysis were used to compare the prognosis of PCa patients with high and low risks stratified using this model. The relationship of the prognostic risk model with PCa immune infiltration and immune response was evaluated using CIBERSORT and TIDE scores. RESULTS: PCa tissues showed a significantly greater proportion of infiltrating neutrophils than the adjacent normal tissues (P<0.05). PCa-associated neutrophils could be clustered into two independent cell subsets: Neu_1 and Neu_2. Neu_2 cells exhibited highly enriched immunoregulatory functions and were highly differentiated and mature, with upregulated immunosuppressive cytokines such as TGFB1, ITGB2, and LGALS3. Based on the genetic characteristics of Neu_2 cell subsets, the prognostic risk model was constructed. The patients in the high-risk group identified by the model had a shorter biochemical recurrence time (P<0.05) and a higher proportion of Tregs and M2-TAMs cell infiltration (P<0.05) with a higher risk of immune rejection and poorer immune response scores. CONCLUSIONS: PCa-associated neutrophils are highly heterogeneous. The prognostic risk model constructed based on the immunosuppressive neutrophil Neu_2 subset can effectively predict both the survival outcomes and immune response of PCa patients.