Development and Verification of a Prostate Cancer Prognostic Signature Based on an Immunogenomic Landscape Analysis - Cheng, Hong; Wang, Yi; Liu, Chunhui; Wu, Tiange; Chen, Shuqiu; Chen, Ming (Frontiers in Oncology, 2021)
Purpose: Prostate cancer (PCa) has a high incidence in older men. In the field of tumor immunology therapy, there are no characteristics to predict the survival of PCa in tumor immunology. Therefore, it is necessary to explore the characteristics of PCa in immunology. Methods: In this study, RNA-seq and clinical data of 499 PCa tissue samples and 52 normal tissue samples were obtained from The Cancer Genome Atlas (TCGA). Finally, 193 differentially expressed immune-related genes (IRGs) between PCa and normal prostate tissues were identified based on TCGA. Functional enrichment analyses showed that immunology may act in a tumor-suppressive role in the initiation of PCa, and then we identified different expressed transcription factors (TFs) and construction of the correlation network between TFs and IRGs. Univariate Cox analysis and multivariate Cox regression analysis were performed to identify 5 keys prognostic IRGs (S100A2, NOX1, IGHV7-81, AMH, AGTR1). Furthermore, the predictive nomogram was established and verified. Results: As a result, we successfully constructed and validated an immune-related prediction model for PCa. In this model, 5-genes model showing more stable than other gene groups. Consistent with our expectations, the signature can independently predict the survival outcome of PCa patients. The results of S100A2 were validated in vitro. Patients with high-immune risk were found correlated with advanced stage. We also found that high S100A2 gene expression has a lower biochemical recurrence, high AMH gene expression has a higher gleason score, lymph node metastasis rate and tumor grade, a lower lymph nodes positive, high ATGR1 gene expression has a lower psa value. Conclusion: Our study showed that our 5-gene immune-related signature could treated as an independent prognostic indicator for PCa.