Identifying prognostic signatures in the microenvironment of prostate cancer

BACKGROUND: An increasing number of studies has indicated that the tumor microenvironment (TME), an important component of tumor tissue, has clinicopathological significance in predicting disease outcome and therapeutic efficacy. However, little evidence in prostate cancer (PCa) is available. METHODS: The cohort of TCGA-PRAD (n=477) was used in this study. Based on the proportion of 22 types of immune cells calculated by CIBERSORT, the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. The TMEscore was calculated based on cluster signature genes, which were obtained from DEGs by the random forest method, and the samples were classified into two subtypes. Analyses of somatic mutation and copy number variation (CNVs) were further conducted to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to immune checkpoint inhibitors (ICIs) as well as the prognosis of PCa. RESULTS: Based on the distribution of infiltrating immune cells in the TME, we constructed the TMEscore model and classified PCa samples into high and low TMEscore groups. Survival analysis indicated that the high TMEscore group had significantly better survival outcome than the low TMEscore group. Correlation analysis showed a significantly positive correlation between TMEscore and the known prognostic factors of PCa. CONCLUSIONS: Our study indicates that the TMEscore could be a potential prognostic biomarker in PCa. A comprehensive description of the characteristics of TME may help predict the response to therapies and provide new treatment strategies for PCa patients.