Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancer

Abstract

Abstract Background Immunotherapy has become a revolutionary treatment for cancer and brought new vitality to tumor immunity. Still, using either immunotherapy alone is unlikely to significantly change the outcome of prostate cancer (PCa), especially metastatic PCa. Bone metastases are the most prevalent metastatic site for advanced PCa. Therefore, finding new immunotherapy targets in PCa patients with bone metastasis is urgently needed. Methods We conducted an elaborative bioinformatics study of immune related genes (IRGs) and tumor-infiltrating immune cells (TIICs) in PCa bone metastases. The TCGA-PRAD and cBioPortal databases were integrated to obtain RNA-sequencing data and clinical prognostic information. Univariate and multivariate Cox regression analyses were conducted to construct an overall survival (OS) prediction model. GSE32269 in the GEO database was analyzed to acquire differentially expressed IRGs. A total of 209 differentially expressed IRGs were identified, of which 159 were down-regulated and 50 were up-regulated. Subsequently, the PPI network was established by Cytoscape for identifying hub genes and biological network. The OS prediction model was established by employing six IRGs (MAVS, HSP90AA1, FCGR3A, CTSB, FCER1G, and CD4). The CIBERSORT algorithm was adopted to assess the proportion of TIICs in each group. Furthermore, Transwell, MTT, and wound healing assays were employed to determine the effect of MAVS on PCa cells. Results High-risk patients had worse OS compared to the low-risk patients in the training and validation cohorts. Meanwhile, clinically practical nomograms were generated using these identified IRGs to predict the 3- and 5-year survival rates of patients. The infiltration percentages of some TIICs were closely linked to the risk score of the OS prediction model. Naïve B cells, M1 and M2 macrophages, and CD4 memory resting T cells were related to the OS. FCGR3A was closely correlated with some TIICs. In vitro experiments verified that up-regulation of MAVS suppressed the proliferation and metastatic abilities of PCa cells. Conclusions Our work presented a thorough interpretation of TIICs and IRGs for illustrating and discovering new potential immune checkpoints in bone metastases of PCa. Additionally, we developed a trustworthy OS risk score model that may serve as a prognostic biomarker and potential immune checkpoints for immunotherapy.