MetaCore enrichment analysis of Egr3-correlated genes.

<p>MetaCore enrichment analysis of Egr3-correlated genes.</p

Cell type-specific expression coefficients^* for Egr transcription factors in relapse and non-relapse prostate cancer (n = 108 arrays).

*<p>Probabilities of β_j were all <0.05 and are not shown; equations for calculating β_j are defined in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054096#s2" target="_blank">Materials and Methods</a>. βj are expressed relative to the mean expression of all probe sets of the array.</p

Egr3 target validation.

<p><b>A:</b> Stable Egr3 knockdown in M12 prostate cancer cells. M12 cells were transfected with scramble or shEgr3 plasmids and subjected to antibiotic selection to achieve stable knockdown. The shSCR-M12 and two shEgr3-M12 clones (cl2 and cl3) were used for further experiments. Egr3 protein levels were analyzed by western blotting using anti-Egr3 antibodies. Membranes were stripped and reprobed with antibodies to β-actin. Molecular weights are shown on the left. <b>B:</b> shSCR-M12 and shEgr3-M12 clone 3 were transfected with pGL3-IL6 or pGL3-IL8 reporter plasmids and a renilla luciferase plasmid as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054096#s2" target="_blank">Methods</a>. A commercial luciferase assay was used to measure firefly and renilla luciferase activity. Results show the ratio of IL6 and IL8 promoter-dependent firefly luciferase activity normalized to renilla for each condition. <b>C:</b> qPCR analysis of Egr3 target genes. Total RNA was extracted from M12 (scramble) and M12 shEgr3 (cl2 and cl3) and analyzed by quantitative RT-PCR. Expression levels were assessed using the 2–ΔΔCT relative quantification method and GAPDH was used for normalization. Results as expressed as function of control (scramble). The error bars were generated using the 2–ΔΔCT method to take into account the standard deviation of GAPDH and the standard deviation of the measured gene.</p

Demographical information of 97 patients used for the analysis of Egr3 in relapse and non-relapse prostate cancer.

*<p>84 prostate cancer patients provided 108 arrays and 13 normal prostate donors provided 19 arrays.</p

Egr3-correlated genes with a reported interaction with an Egr transcription factor^*.

*<p>Interactions and references are as reported by MetaCore. Genes from this table are highlighted blue <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054096#pone.0054096.s005" target="_blank">Table S1</a></b>.</p

Human Protein Atlas immunohistochemistry using anti-Egr3 antibodies (left) and Aperio ImageScope pseudocolored prostate sections (right) based on thresholding as described in Materials and Methods.

<p><b>A–B</b>: HPA normal prostate, patients 2098 and 2472, respectively. <b>C–D</b>: HPA prostate cancer samples, patients 3303 and 3744, respectively. All additional available HPA cases are shown in the supplement information. E: histogram of strong positive pixel ratio (NSR) for normal and prostate cancer patients.</p

Normalized Affymetrix expression of Egr3 (probe set 206115_at) in the SPECS U133Plus2.0 dataset consisting of 19 normal prostate samples and 108 prostate cancer samples.

<p>Affymetrix expression is plotted on a linear scale where the anti-log₂ of each patient’s Egr3 expression value is plotted on the y-axis. The dashed line at 1292 denotes the mean Egr3 intensity value for all samples.</p

Survival analysis for the seven-gene Classifier with Gleason sum.

<p>Kaplan-Meier estimates of survival time of 42 independent patients in test Data Set 2 (GSE25136) according to the seven-gene Classifier with the Surgical Pathology-determined Gleason sum. The Gleason sum variable has the same weighting as each gene in the determination of classification.</p

Comparison of PAM-based gene classifier in two independent tests.

<p>Comparison of PAM-based gene classifier in two independent tests.</p

An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account

<div><p>One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.</p> </div