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

Immunotherapy has become a revolutionary treatment for cancer and brought new vitality to tumor immunity. Bone metastases are the most prevalent metastatic site for advanced prostate cancer (PCa). Therefore, finding new immunotherapy targets in PCa patients with bone metastasis is urgently needed. We conducted an elaborative bioinformatics study of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs) in PCa bone metastases. 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 was analyzed to acquire differentially expressed IRGs. 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. 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. Some tumor-infiltrating immune 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. Our work presented a thorough interpretation of TIICs and IRGs for illustrating and discovering new potential immune checkpoints in bone metastases of PCa.