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Journal of Molecular Biomarkers & Diagnosis

ISSN: 2155-9929

Open Access

Combination of Multiple Markers Predicts Prostate Cancer Outcome

Abstract

Maria A Svensson, Roopika Menon, Jessica Carlsson, Wenzel Vogel, Ove Andrén, Michael Nowak and Sven Perner

Today we are facing a large problem of overtreatment in men with prostate cancer (PCa) due to the current lack of reliable prognostic biomarkers. Aberrations including ETS family gene rearrangements, phosphatase and tensin homolog (PTEN) deletions, enhancer of zeste homolog 2 (EZH2) overexpression and changes in the androgen receptor (AR) are commonly described in PCa and are believed to have an important role in progression of the disease. The aim of this study is to analyze if the protein expression of ERG, AR, PTEN and EZH2, and the deletion status of the PTEN, either alone or in combination can predict clinical outcome. Our cohort consists of 214 men that have undergone radical prostatectomy at the University Hospital of Örebro, Sweden between 1989-2005. Immunohistochemistry was used to detect AR, ERG, PTEN and EZH2 antigen and PTEN deletion was assessed using chromogenic in situ hybridization. The overall frequency showed AR-, ERG- and EZH2 expression in 99.5%, 52.9%, and 92.3% respectively. PTEN deletion was seen in 37.4% of the cases, where homozygous and heterozygous deletion was present in 18.1% and 19.2%, respectively. Our results show that there was a significant association between the combined ERG- and EZH2 expression and PTEN deletion with PCa specific death (p=0.035). This significant association was also seen in the group of cases that harbored both ERG expression and PTEN deletion (p=0.036). Cases expressing ERG, exhibiting PTEN loss (either hetero- or homozygous loss) and a Gleason score ≥8 showed a significantly higher rate of developing castration-resistant PCa (CRPC) and dying of PCa. The current lack of a reliable prognostic tool available for PCa is a large problem, the results from this study and others shows the great potential in using multiple biomarkers to predict PCa outcome.

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