Establishment of a novel prognostic prediction model through bioinformatics analysis for prostate cancer based on ferroptosis-related genes and its application in immune cell infiltration.

Background: Ferroptosis-related genes (FRGs) play vital roles in survival and prognosis of prostate cancer (PCa) patients. We establish a ferroptosis-related prediction model through bioinformatics analysis for overall survival (OS) and disease-free survival (DFS), so as to evaluate the clinical survival status through the characteristics of immune cell infiltration (ICI), which could provide information for treatment monitoring. Methods: At first, 268 FRGs were obtained from previous studies. Differentially expressed FRGs were identified based on The Cancer Genome Atlas (TCGA) database, and FRG enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then performed univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to establish OS- and DFS-related prognostic prediction models. The association of the model and clinicopathological features was further analyzed. Subsequently, unique genomic signatures of immune cell subsets were obtained through the KEGG database. Based on specific genes associated with ferroptosis and their association with ICI, immune infiltration was assessed in patients in different risk groups. Results: We constructed an OS- and an DFS-prognostic model through bioinformatics analysis. The predicted values of OS and DFS-related models were higher in T3-4 than in T1-2 (P=0.0057, P<0.001), and the predicted value of the DFS model in N0 stage was higher than that in N1 stage (P=0.0136). Results of Single-sample gene set enrichment analysis (ssGSEA) on the basis of the KEGG dataset showed p53 signaling being the most enriched signal in the high-risk group, while endocytosis was the most enriched signal in the low-risk group. M2 macrophages (P=0.007) and neutrophils (P=0.024) were enriched in the high-risk group, and CD4-activated memory T cells were significantly accumulated in the low-risk group (P=0.017). Conclusions: The OS- and DFS-related model based on FRGs and ICI create new insights into the disease state assessment of PCa patients., which may aid in the development of individualized and precise treatment in the future.