High-throughput transcriptomic analysis nominates proteasomal genes as age-specific biomarkers and therapeutic targets in prostate cancer

Abstract

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.

Figure 1

(a) Forest plot showing the overall effect of age on 10-year metastasis in four clinical cohorts (MCI, MCII, CC and TJU). Age was not significantly associated with metastasis in any cohort individually nor in a pooled random effects model. Odds ratio is comparing the odds of 10-year metastatic progression in men >65 versus ⩽65. Bar plot showing the mean age (±s.e.m.) in patients who had a metastasis by 10 years versus those who did not in CC (b), MCI (c), MCII (d) and TJU (e). The mean ages were not significantly different in any of the groups between patients who metastasized by 10 years and those that did not.

Figure 2

(a) Venn diagram in the center of the panel displays the overlap in the top 1000 genes that were associated with metastatic progression across all four cohorts. 343 genes were downregulated (blue) and 479 genes were upregulated (yellow) only in metastatic patients age <65. 321 genes were downregulated (red) and 501 genes were upregulated (green) only in metastatic patients age ⩾65. 128 genes were upregulated (yellow–green) and 50 genes were downregulated (purple) in metastatic patients independent of age. The heat map on the left panel displays the expression of all 1000 genes prognostic in younger patients. The genes represented by the yellow bar are upregulated and the genes represented by the blue bar are downregulated in metastatic patients age <65. The heat map on the right panel displays the expression of all 1000 genes prognostic in older patients. The genes represented by the green bar are upregulated and the genes represented by the red bar are downregulated in metastatic patients age ⩾65. Hierarchical clustering in both age groups was able to stratify metastasis (the top bar above the heat maps). (b) Bar plot that compares the pooled Spearman's correlation coefficient of the expression of all genes versus age. The correlation coefficients ranged from −0.13 to 0.12.

Figure 3

(a) GSEA-enrichment plot of the most negatively enriched gene set: Biocarta proteasome pathway. (b) GSEA-enrichment plot of the most positively enriched gene set: ion channel activity. (c) Bar plot depicting the normalized enrichment scores (NES) of the top 10 most negatively enriched gene sets, which contain several gene sets related to proteasomes (green) and translation initiation (red). (d) Bar plot depicting the normalized enrichment scores (NES) of the top 10 most positively enriched gene sets, which contain several gene sets related to ion channels (blue).

Figure 4

(a) Table showing the delta-T values of the top proteasomal genes (delta-T<0.5) from the most negatively enriched gene set (Biocarta proteasome pathway). (b) Forest plot showing that high expression of three or more of any of these proteasomal genes conferred worse prognosis only in younger men. When examining these genes individually, high expression of PSMB4 (c), PSMB7 (d), PSMD14 (f), PSMB2 (g) and PSMD11 (h) all conferred significantly worse prognosis only in younger men, with PSMD6 showing the same trend with borderline significance (e). Kaplan–Meier curves show high expression of three or more of any of these proteasomal genes confers worse metastasis-free survival in younger (i) but not older (j) men.

Figure 5

In vitro dose-response curves for bortezomib in three widely used PCa cell lines, PC3, LNCaP and VCAP show that proliferation of all three cell lines were inhibited by bortezomib, with comparable IC50s of 4.26 nM for PC3, 7.59 nM for LNCaP and 2.41 nM for VCAP.