Construction and Verification of a Prognostic Model for Prostate Cancer based on Ribosome Biogenesis-Related Genes

Background: Prostate cancer (PCa) is a prevalent and often aggressive malignancy. While ribosome production is a critical process in numerous cancers, the role of ribosome biogenesis-related genes (RB-RGs) in PCa remains underexplored. Therefore, this study aims to examine the expression characteristics of RB-RGs as potential prognostic markers in PCa. Methods: Transcriptomic data were obtained from public databases to pinpoint differentially expressed genes (DEGs) associated with PCa. Among these, DEGs that were also identified as regulatory genes were selected as candidate genes and subjected to regression analysis to determine those with prognostic significance. A prognostic risk model was then created and subjected to validation. A standalone prognostic evaluation was performed. Additionally, analyses for enrichment, immune infiltration, and drug prediction were conducted for both high- and low-risk PCa cohorts. Finally, the levels of the prognostic genes were validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western Blot (WB). Results: SMARCA4 , FBLL1 , RRS1, and NVL were selected as prognostic genes. A risk model, exhibited considerable precision in evaluating the risk of PCa. The risk score, in conjunction with the prostate-specific antigen (PSA) score and Gleason score, has been recognized as independent prognostic factors for PCa. Analysis of biological pathways indicated notable variations in pathways, especially the cell cycle, when comparing the high- and low-risk groups. The expression of prognostic genes demonstrated a correlation with the presence of 21 distinct types of immune cells, notably including type-17 T helper cells, within PCa samples. Furthermore, notable variations in the levels of target genes ( AKT1 , CDK1 , HIF1A , NFKB1 , SRC, and TRIM24 ) were detected between the high- and low-risk cohorts in relation to different chemotherapeutic agents. PCR analysis showed that NVL and SMARCA4 were significantly upregulated in PCa samples (p < 0.05), with no significant difference in RRS 1. WB analysis showed that SMARCA4 , FBLL 1 , and RRS1 and NVL were differently expressed in both PCa and Control, with higher upregulation in PCa than in the control group. Conclusion: Among RB-RGs, SMARCA4 , FBLL1 , RRS1 and NVL were identified as prognostic genes for PCa, thereby providing a new direction for clinical disease treatment.