Objective: The purpose of the study was to develop an improved classifier for predicting biochemical recurrence (BCR) in clinically localized PCa patients after radical prostatectomy.
Methods and materials: Retrospective study including 122 PCa patients who attended our department between 2000 and 2007. Gene expression patterns were analyzed in 21 samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in 101 independent PCa patients who underwent radical prostatectomy. Logistic regression analysis was used to identify individual predictors of BCR. A risk score for predicting BCR including clinicopathological and gene expression variables was developed. Interaction networks were built by GeneMANIA Cytoscape plugin.
Results: A total of 37 patients developed BCR (36.6%) in a median follow-up of 120 months. Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR < 0.1). We found that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion, and positive margins were independent prognostic factors of BCR. A risk score generated using these variables was able to discriminate between 2 groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, P< 0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the 2 genes of the model were not found.
Conclusion: Combination of gene expression patterns and clinicopathological variables in a robust, easy-to-use, and reliable classifier may contribute to improve PCa risk stratification.
Keywords: Biomarkers; Gene expression signature; Microarrays; Quantitative PCR; Risk stratification.
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