Heterogeneity and prognostic significance of mast cell subsets in the tumor microenvironment of prostate cancer.

[BACKGROUND] Prostate cancer (PC) is one of the malignant tumors with high incidence and mortality worldwide in men. Immune cells in the tumor microenvironment (TME), particularly mast cells, play important roles in tumor progression and prognosis. However, the dual roles of mast cells in PC have not been fully elucidated.

[METHODS] In this study, single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing, and bioinformatics analyses were performed to investigate the heterogeneity of mast cell subsets in the TME of PC and their association with prognosis. Single-cell data from 39 PC tumor samples were analyzed, and prognostic prediction was validated using datasets including HMU, TCGA, and MSKCC cohorts. Non-negative matrix factorization was applied to cluster mast cell subsets, followed by analyses of subset-specific gene markers, transcription factor activity, and biological pathways. Survival analysis and ROC curve evaluation were conducted to assess prognostic value.

[RESULTS] Mast cells in the TME of PC were classified into two distinct subsets, each characterized by unique gene markers and functional pathways. Mast cell1 was highly associated with pro-tumorigenic pathways, whereas mast cell2 predominantly exhibited antitumor immune regulatory properties. High expression of mast cell1 signatures was correlated with poorer survival outcomes, while high expression of mast cell2 signatures was associated with better survival. Key marker genes such as BIRC3 and FOS were identified as potential prognostic factors, high BIRC3 expression was significantly associated with unfavorable prognosis, whereas high FOS expression correlated with favorable prognosis.

[CONCLUSION] This study revealed functional heterogeneity of mast cell subsets in the TME of PC and their distinct roles in tumor progression. The identification of subset-specific marker genes provides novel molecular targets for clinical diagnosis, prognostic prediction, and personalized therapy in PC.