Exploring the key functions of T cells and the regulation of the immune microenvironment in prostate cancer using single-cell RNA sequencing and bulk RNA sequencing.

The incidence of prostate cancer continues to increase, making it the second most common malignant tumor among men worldwide. Immunotherapy has emerged as a key therapeutic strategy for treating tumors. Numerous studies have established that the efficacy of tumor immunotherapy is closely associated with the tumor microenvironment and T cell subsets. However, the specific functions of certain T cell subsets in prostate cancer remain incompletely characterized. Therefore, this study aimed to systematically investigate the distribution patterns of T cell subsets within the tumor microenvironment of prostate cancer patients and their correlations with clinicopathological parameters. Therefore, we investigated the impact of T cells on the tumor microenvironment of prostate cancer at the single-cell level. We employed a variety of analytical methods to reveal the functions of T cells, including cell interaction analysis, time-series analysis, enrichment analysis, immune infiltration analysis, and other analytical approaches. By integrating bulk RNA-seq data, we constructed and validated a prognostic risk model based on T cell marker genes. Finally, we utilized the ssGSEA and ESTIMATE algorithms to explore the relationship between the prognostic risk model and immunotherapy. After quality control, 16,999 cells from the single-cell data were retained for downstream analysis. Our study focused on T cells, revealing the communication between various cell types and T cells. Pseudotime analysis showed that different T cell marker genes exhibited differential expression at various time points, corresponding to distinct biological processes. Enrichment analysis indicated that T cell marker genes were enriched in several immune-related pathways. From our analysis, BCAS2, EIF2S2, RIOK3, and ATP6V1E1 were ultimately identified as prognostic markers. Immune infiltration analysis revealed that high-risk patients had lower immune scores, stromal scores, and ESTIMATE scores and greater tumor purity compared to low-risk patients. We analyzed the mechanisms involving T cells in prostate cancer from multiple perspectives, constructed a prognostic model, and conducted immune infiltration analysis. Our findings contribute to the understanding of prostate cancer and its prognosis, providing valuable insights for future research and prognostic assessments in prostate cancer.