Endosomal gene expression: a new indicator for prostate cancer patient prognosis?

Abstract

Prostate cancer continues to be a major cause of morbidity and mortality in men, but a method for accurate prognosis in these patients is yet to be developed. The recent discovery of altered endosomal biogenesis in prostate cancer has identified a fundamental change in the cell biology of this cancer, which holds great promise for the identification of novel biomarkers that can predict disease outcomes. Here we have identified significantly altered expression of endosomal genes in prostate cancer compared to non-malignant tissue in mRNA microarrays and confirmed these findings by qRT-PCR on fresh-frozen tissue. Importantly, we identified endosomal gene expression patterns that were predictive of patient outcomes. Two endosomal tri-gene signatures were identified from a previously published microarray cohort and had a significant capacity to stratify patient outcomes. The expression of APPL1, RAB5A, EEA1, PDCD6IP, NOX4 and SORT1 were altered in malignant patient tissue, when compared to indolent and normal prostate tissue. These findings support the initiation of a case-control study using larger cohorts of prostate tissue, with documented patient outcomes, to determine if different combinations of these new biomarkers can accurately predict disease status and clinical progression in prostate cancer patients.

Keywords: biomarkers; endosomal gene expression; mRNA; prognosis; prostate cancer.

Figure 1. Vertical scatter plots of endosome-associated gene expression data from the Tomlins cohort [17]

Expression profiling data derived from the Chinnaiyan Human 20K Hs6 array of 18 non-malignant tissues, 13 prostatic intraepithelial neoplasias, 30 primary prostate cancer and 19 metastatic cancer tissue samples were quantitated to show relative amount of expression of lysosomal-related genes. Statistical significance is represented by an asterisk (*P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001).

Figure 2. Kaplan-Meier analysis of endosomal genes and patient stratification based on biochemical recurrence (BCR)

Patients from the Glinsky cohort [18] were stratified into two groups by K-means clustering based on amount (high - black line, low - grey line) of gene expression in prostate cancer samples. Analysis was performed using the Log-Rank test. IGF2R (P = 0.007), CTSB (P = 0.03), MYO1B (P = 0.03), SORT1 (P = 0.004) and STX12 (P = 0.001) differentiated patients at risk of relapse based on the amount of gene expression showed evidence of prognostic capacity.

Figure 3

A. Kaplan-Meier survival analysis of a combined APPL1, RAB5A and EEA1 gene signature for cancer patients expressing ≤ 10 ng/mL PSA. Patients from the Glinsky cohort [18] expressing PSA ≤ 10 mg/mL were stratified into groups by K-means clustering based on RAB5A, APPL1 and EEA1 gene expression; the three-gene combined signature of APPL1, RAB5A and EEA1 stratified patients based on BCR (P ≤ 0.0397, Log-Rank test; high expression - black line, low expression - grey line). B. Kaplan-Meier survival analysis of MYO1B, PDCD6IP and STX12 expression and combined gene signature for cancer patients expressing ≤ 10 ng/mL PSA; the three-gene combined signature of MYO1B, PDCD6IP and STX12 stratified patients based on BCR (P ≤ 0.0029, Log-Rank test; low risk - black line, high risk - grey line). BCR: biochemical recurrence; HR: hazard ratio; CI: confidence interval.

Figure 4. Vertical scatter plots of endosome-associated gene expression from qPCR mRNA analysis on non-malignant (n = 11), indolent (n = 5) and aggressive (n = 6) prostate cancer tissues from the Irish Prostate Cancer Research Consortium, Dublin, Ireland

Statistical significance is represented by an asterisk (*P ≤ 0.05; **P ≤ 0.01).