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A methylation cut-off for patient stratification was established in a training cohort ( = 0.003, hazard ratio = 1.814) to the Gleason score, pathological T stage, prostate-specific antigen, and surgical margins in a multivariate analysis.The clinical performance was particularly high in patients at intermediate risk (Gleason score of 7) and in samples containing high tumor cell content.A function is fitted through the top X% of each bin, thereby defining a limit fold change.All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SD The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR.The performance of both array platforms in identifying regulated and non-regulated genes was identical.With either platform, 16 of 17 definitely regulated genes were correctly identified, and no definitely unregulated transcript was falsely identified as regulated.Using the Taq Man Gene Expression Assay based real-time PCR data set as the reference set, the performance of the two microarray platforms was evaluated focusing on the following criteria: (1) Sensitivity and accuracy in detection of expression; (2) Fold change correlation with real-time PCR data in pair-wise tissues as well as in gene expression profiles determined across all tissues; (3) Sensitivity and accuracy in detection of differential expression.Gene Expression Assay based real-time PCR technology.

1,375 genes represented by both microarray platforms and spanning a wide dynamic range in gene expression levels, were selected for Taq Man Gene Expression Assay based real-time PCR validation.The complete sequencing of several genomes, including that of the human, has signaled the beginning of a new era in which scientists are becoming increasingly interested in functional genomics; that is, uncovering both the functional roles of different genes, and how these genes interact with, and/or influence, each other.DNA microarrays are rapidly becoming a fundamental tool in discovery-based genomic and biomedical research.The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions.Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates.The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels.