The Analysis of Tumor Microenvironment of Prostate Cancer Identifies Prognostic Signatures

Abstract Background : Tumor microenvironment (TME) is an essential part of tumor tissue, and increasing references suggested that TME has clinicopathological significance in predicting prognosis and therapeutic efficacy. However, little efficacy has been demonstrated in prostate cancer. Methods : The cohort TCGA-PRAD (n=477) was used in this study. Based on the proportion of 22 types of immune cells calculated by CIBERSORT, TME was classified by K-means Clustering and differentially expressed genes (DEGs) were determined. Then TMEscore was calculated based on cluster signature genes, which were obtained from DEGs by random forest method, and the samples were classified to two subtypes. We performed somatic mutation and copy number variation analysis to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to ICIs as well as the prognosis of PCa. Results: Based on the proportion of immune cells, we constructed the TMEscore model and classified PCa samples into TMEscore high and TMEscore low groups. The results of survival analysis suggested that the TMEscore high group had significantly better survival outcome than the TMEscore low group. The correlation analysis showed a significantly positive correlation between TMEscore and the known prognostic factors of tumors. Conclusion : our study indicates that the TMEscore may be a potential prognostic biomarker in PCa. A comprehensive description of the characteristics of TME may help to explain the response to therapies for PCa patients and provide the new strategies for treatment.