Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Introduction Up to 30% Stage I lung cancer patients suffer recurrence within 5 years of curative surgery. We sought to improve existing protein-coding gene and microRNA expression prognostic classifiers by incorporating epigenetic biomarkers. Methods Genome-wide screening of DNA methylation and pyrosequencing analysis of HOXA9 promoter methylation were performed in two independently collected cohorts of Stage I lung adenocarcinoma. The prognostic value of HOXA9 promoter methylation alone and in combination with mRNA and miRNA biomarkers was assessed by Cox regression and Kaplan-Meier survival analysis in both cohorts. Results Promoters of genes marked by Polycomb in Embryonic Stem Cells were methylated de novo in tumors and identified patients with poor prognosis. The HOXA9 locus was methylated de novo in Stage I tumors (P < 0.0005). High HOXA9 promoter methylation was associated with worse cancer-specific survival (Hazard Ratio [HR], 2.6; P = 0.02) and recurrence-free survival (HR, 3.0; P = 0.01), and identified high-risk patients in stratified analysis of Stage IA and IB. Four protein-coding gene (XPO1, BRCA1, HIF1α, DLC1), miR-21 expression and HOXA9 promoter methylation were each independently associated with outcome (HR, 2.8; P = 0.002; HR, 2.3; P = 0.01; and HR, 2.4; P = 0.005, respectively), and, when combined, identified high-risk, therapy naïve, Stage I patients (HR, 10.2; P = 3x10−5). All associations were confirmed in two independently collected cohorts. Conclusion A prognostic classifier comprising three types of genomic and epigenomic data may help guide the postoperative management of Stage I lung cancer patients at high risk of recurrence.

Understanding the language encrypted in the gene regulatory regions of the human genome is a challenging goal for the genomic era. Although customary extrapolations from steady-state mRNA levels have been effective, deciphering these regulatory codes will require additional empirical data sets that more closely reflect the dynamic progression of molecular events responsible for inducible transcription. We describe an approach using chromatin immunoprecipitation to profile the kinetic occupancy of the transcriptional coactivator and histone acetyltransferase p300 at numerous mitogen-induced genes in activated T cells. Comparison of these profiles reveals a class of promoters that share common patterns of inducible expression, p300 recruitment, dependence on selective p300 domains, and sensitivity to histone deacetylase inhibitors. Remarkably, this class also shares an evolutionarily conserved promoter composition and structure that accurately predicts additional human genes with similar functional attributes. This ''reverse genomic'' approach will have broad application for the genome-wide classification of promoter structure and function.

Background: Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25-30 base), long oligonucleotide (50-80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard.