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Background:Although prostate cancer (PCa) is hypothesized to differ in nature between younger versus older patients, the underlying molecular distinctions are poorly understood. We hypothesized that high-throughput transcriptomic analysis would elucidate biological differences in PCas arising in younger versus older men, and would nominate potential age-specific biomarkers and therapeutic targets.Methods:The high-density Affymetrix GeneChip platform, encompassing >1 million genomic loci, was utilized to assess gene expression in 1090 radical prostatectomy samples from patients with long-term follow-up. We identified genes associated with metastatic progression by 10 years post-treatment in younger (age<65) versus older (age⩾65) patients, and ranked these genes by their prognostic value. We performed Gene Set Enrichment Analysis (GSEA) to nominate biological concepts that demonstrated age-specific effects, and validated a target by treating with a clinically available drug in three PCa cell lines derived from younger men.Results:Over 80% of the top 1000 prognostic genes in younger and older men were specific to that age group. GSEA nominated the proteasome pathway as the most differentially prognostic in younger versus older patients. High expression of proteasomal genes conferred worse prognosis in younger but not older men on univariate and multivariate analysis. Bortezomib, a Food and Drug Administration approved proteasome inhibitor, decreased proliferation in three PCa cell lines derived from younger patients.Conclusions:Our data show significant global differences in prognostic genes between older versus younger men. We nominate proteasomeal gene expression as an age-specific biomarker and potential therapeutic target specifically in younger men. Limitations of our study include clinical differences between cohorts, and increased comorbidities and lower survival in older patients. These intriguing findings suggest that current models of PCa biology do not adequately represent genetic heterogeneity of PCa related to age, and future clinical trials would benefit from stratification based on age. [ABSTRACT FROM AUTHOR]