Abstract
BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.
Highlights
Prostate cancer is the most common genitourinary tumor in men
The findings showed that the proliferation rate of prostate cancer cells DU145 and PC-3 was significantly lower after knocking down BIRC5 compared with that of the control group (Figures 12D,E)
The in vitro experiments in the current study showed that BIRC5 is highly expressed in prostate cancer tissues, and it modulates proliferation and migration rate of prostate cancer cells
Summary
Prostate cancer is the most common genitourinary tumor in men. It is the fifth leading cause of cancer mortalities worldwide. 1.3 million new cases and 359,000 cancer-related mortalities were reported in Bray et al (2018). Treatment modalities for prostate cancer mainly include surgical resection, radiotherapy, and hormonal therapy. The 5-year survival rate of local PCa can reach 100% owing to advances in diagnostic and therapeutic methods, the 5-year survival rate of metastatic PCa is approximately 30% (Litwin and Tan, 2017). Therapeutic efficacy of prostate cancer can be improved by understanding the molecular mechanisms of prostate cancer occurrence and progression. Prostate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa
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