Identification of novel biomarkers differentially expressed between African-American and Caucasian-American prostate cancer patients

Abstract

Prostate cancer (PCa) incidence and mortality rate vary among racial and ethnic groups with the highest occurrence in African American (AA) men who have mortality rates twice that of Caucasians (CA). In this study, we focused on differential expression of proteins in AA prostate cancer compared to CA using Protein Pathway Array Analysis (PPAA), in order to identify protein biomarkers associated with PCa racial disparity. Fresh frozen prostate samples (n=90) obtained from radical prostatectomy specimens with PCa, including 25 AA tumor, 21 AA benign, 23 CA tumor, 21 CA benign samples were analyzed. A total of 286 proteins and phosphoproteins were assessed using PPAA. By PPAA analysis, 33 proteins were found to be significantly differentially expressed in tumor tissue (n=48, including both CA and AA) in comparison to benign tissue (n=42). We further compared protein expression levels between AA and CA tumor groups and found that 3 proteins were differentially expressed (P<0.05 and q<5%). Aurora was found to be significantly increased in AA tumors, while Cyclin D1 and HNF-3a proteins were downregulated in AA tumors. Predicted risk score was significantly different between AA and CA ethnic groups using logistic regression analysis. In conclusion, we identified Aurora, Cyclin D1 and HNF-3a proteins as being differentially expressed between AA and CA in PCa tissue. Our study suggests that these proteins might be involved in different pathways that lead to aggressive PCa behavior in AA patients, potentially serving as biomarkers for the PCa racial disparity.

Keywords: African American; Caucasian; Prostate cancer; molecular markers; racial disparity.

Figure 1

Boxplot of AA and CA risk score distribution. Boxplot of AA and CA patients risk score distribution in cohort 2 showing the CA group has larger median value and quantile range.

Figure 2

Non-negative Matrix factorization analysis of gene features separating AA and CA group (A) and AA/CA sample clustering using the feature gene sets (B). (A) 13 genes have significant differential expression between AA and CA; (B) AA and CA samples segregate into subgroups using the 13 gene features.