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Updated: Sep 16, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Toward Personalized Surgery in Advanced Prostate Cancer: Stratification by PTEN, AR-V7, TP53, TMPRSS2-ERG, and ERBB2
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Background: Advanced prostate cancer is a biologically heterogeneous disease often marked by multiple genetic and epigenetic alterations that influence tumor progression, treatment resistance, and prognosis. Among the most frequently altered genes are PTEN, AR-V7, TP53, TMPRSS2-ERG, and ERBB2, each with potential relevance for stratifying risk and guiding targeted therapy. Methods: This retrospective study included 43 patients with advanced prostate cancer who underwent radical prostatectomy. Tumor specimens were analyzed using fluorescence in situ hybridization (FISH) to assess the mutational status of the five markers. Clinicopathological parameters, including PSA levels, Gleason score, tumor stage, and invasion status, were correlated with molecular alterations using multinomial logistic regression. Results: The most common isolated alteration was PTEN loss (20.9%), followed by TP53 amplification (16.3%), TMPRSS2-ERG fusion (13.9%), AR-V7 expression (11.6%), and ERBB2 amplification (7%). Combined alterations were also observed, with dual or triple marker expression in select aggressive cases. PTEN- and AR-V7+ were associated with low PSA values despite aggressive pathology, while ERBB2+ correlated with high PSA levels and high Gleason scores. TP53+ and ERBB2+ were also significantly associated with high-grade tumors (Gleason 7). AR-V7+ was the only marker significantly associated with seminal vesicle invasion. Younger age was weakly correlated with AR-V7+ and TP53+ status. Conclusions: The molecular profile defined by PTEN, AR-V7, TP53, and ERBB2 identifies distinct biological subtypes in advanced prostate cancer, each with specific prognostic and therapeutic implications. Integration of these biomarkers into routine clinical assessment may improve treatment personalization and risk stratification. Validation in larger, prospective cohorts is warranted.

