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Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups., Results: We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation., Conclusions: This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.

A major challenge in the post-sequencing era is to elucidate the activity and biological function of genes that reside in the human genome. An important subset includes genes that encode proteins that regulate gene expression or maintain the structural integrity of the genome. Using a novel oligonucleotide-binding substrate as bait, we show the feasibility of a modified functional expression-cloning strategy to identify human cDNAs that encode a spectrum of nucleic acid-binding proteins (NBPs). Approximately 170 cDNAs were identified from screening phage libraries derived from a human colorectal adenocarcinoma cell line and from noncancerous fetal lung tissue. Sequence analysis confirmed that virtually every clone contained a known DNA- or RNA-binding motif. We also report on a complementary sorting strategy that, in the absence of subcloning and protein purification, can distinguish different classes of NBPs according to their particular binding properties. To extend our functional annotation of NBPs, we have used GeneChip expression profiling of 14 different breast-derived cell lines to examine the relative transcriptional activity of genes identified in our screen and cluster analysis to discover other genes that have similar expression patterns. Finally, we present strategies to analyze the upstream regulatory region of each gene within a cluster group and select unique combinations of transcription factor binding sites that may be responsible for dictating the observed synexpression.