An expression signature at diagnosis to estimate prostate cancer patients’ overall survival

Abstract

Background:This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis.Methods:To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan–Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival.Results:An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91–11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79–6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone.Conclusions:The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm

“…More importantly, gene expression is a consequence of cumulative genetic and epigenetic alterations. With the goal of clinically stratifying samples into risk groups, several gene expression biomarkers have been proposed for a large variety of cancer types202122232425.…”

Cancer Stem Cells

“…Instead two unipotent basal and luminal stem cells were identified. The same was described in prostate development (Liu et al 2011; Table 1 Tumor-initiating cell-related gene signature studies reporting prognostic signatures in independent patient data derived from bulk cells (Glinsky et al 2005;Phillips et al 2006;Liu et al 2007;Shipitsin et al 2007;Stevenson et al 2009;Gentles et al 2010;Eppert et al 2011;Merlos-Suarez et al 2011;Becker et al 2012;Liu et al 2012;Atkinson et al 2013;Metzeler et al 2013;Schwede et al 2013;Van den Broeck et al 2013;Peng et al 2014;Yin et al 2014;Pfefferle et al 2015;Yang et al 2015) Cancer Type Signature Source…”

RNA biomarkers to facilitate the identification of aggressive prostate cancer

“…Aside from the commercially available gene panels, a number of other groups have established gene signatures prognostic for biochemical recurrence or mortality, using methods such as candidate gene approaches to investigate particular pathways (Irshad et al, 2013; Peng et al, 2014), and whole transcriptome analysis either by microarray (Gasi Tandefelt et al, 2013; Yu et al, 2004) or RNA sequencing (Long et al, 2014). A collection of the gene signatures that have been described are summarized in Table 3, and include panels that have demonstrated prognostic ability for either biochemical recurrence or mortality.…”