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GEMS (Gene Expression Metasignatures), a Web Resource for Querying Meta-analysis of Expression Microarray Datasets: 17β-Estradiol in MCF-7 Cells

Departments of ¹ Molecular and Cellular Biology, ² Human and Molecular Genetics, and ³ Medicine, ⁴ Nuclear Receptor Signaling Atlas Bioinformatics Resource, and ⁵ BCM Bioinformatics Research Center, Baylor College of Medicine, Houston, Texas

Requests for reprints: Neil J. McKenna, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030. Phone: 713-798-8568; Fax: 713-790-1275; E-mail: nmckenna{at}bcm.edu.

Key Words: estradiol • gene expression profiling • meta-analysis

With large amounts of public expression microrray data being generated by multiple laboratories, it is a significant task for the bench researcher to routinely identify available datasets, and then to evaluate the collective evidence across these datasets for regulation of a specific gene in a given system. 17β-Estradiol stimulation of MCF-7 cells is a widely used model in the growth of breast cancer. Although myriad independent studies have profiled the global effects of this hormone on gene expression in these cells, disparate experimental variables and the limited power of the individual studies have combined to restrict the agreement between them as to the specific gene expression signature elicited by this hormone. To address these issues, we have developed a freely accessible Web resource, Gene Expression MetaSignatures (GEMS) that provides the user a consensus for each gene in the system. We conducted a weighted meta-analysis encompassing over 13,000 genes across 10 independent published datasets addressing the effect of 17β-estradiol on MCF-7 cells at early (3–4 hours) and late (24 hours) time points. In a literature survey of 58 genes previously shown to be regulated by 17β-estradiol in MCF-7 cells, the meta-analysis combined the statistical power of the underlying datasets to call regulation of these genes with nearly 85% accuracy (false discovery rate–corrected P < 0.05). We anticipate that with future expression microarray dataset contributions from investigators, GEMS will evolve into an important resource for the cancer and nuclear receptor signaling communities. [Cancer Res 2009;69(1):23–6]

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