Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Background: There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk . Methods: We downloaded the data of 487 PCa patients from TCGA database. We used immunologically genes as input for consensus clustering, and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and AR scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed GSEA to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, GEO, cBioPortal, and ICGC databases. Results: Four PCa subtypes (C1, C2, C3, C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1+2+4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations of CLU and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in above databases. Conclusions: PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.