The polycomb group protein EZH2 is involved in progression of prostate cancer

Prostate cancer is a leading cause of cancer-related death in males and is second only to lung cancer. Although effective surgical and radiation treatments exist for clinically localized prostate cancer, metastatic prostate cancer remains essentially incurable. Here we show, through gene expression profiling(1), that the polycomb group protein enhancer of zeste homolog 2 (EZH2)(2,3) is overexpressed in hormone-refractory, metastatic prostate cancer. Small interfering RNA (siRNA) duplexes(4) targeted against EZH2 reduce the amounts of EZH2 protein present in prostate cells and also inhibit cell proliferation in vitro. Ectopic expression of EZH2 in prostate cells induces transcriptional repression of a specific cohort of genes. Gene silencing mediated by EZH2 requires the SET domain and is attenuated by inhibiting histone deacetylase activity. Amounts of both EZH2 messenger RNA and EZH2 protein are increased in metastatic prostate cancer; in addition, clinically localized prostate cancers that express higher concentrations of EZH2 show a poorer prognosis. Thus, dysregulated expression of EZH2 may be involved in the progression of prostate cancer, as well as being a marker that distinguishes indolent prostate cancer from those at risk of lethal progression.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62896/1/nature01075.pd

Molecular Concepts Analysis Links Tumors, Pathways, Mechanisms, and Drugs

Global molecular profiling of cancers has shown broad utility in delineating pathways and processes underlying disease, in predicting prognosis and response to therapy, and in suggesting novel treatments. To gain further insights from such data, we have integrated and analyzed a comprehensive collection of “molecular concepts”" representing > 2500 cancer-related gene expression signatures from Oncomine and manual curation of the literature, drug treatment signatures from the Connectivity Map, target gene sets from genome-scale regulatory motif analyses, and reference gene sets from several gene and protein annotation databases. We computed pairwise association analysis on all 13,364 molecular concepts and identified > 290,000 significant associations, generating hypotheses that link cancer types and subtypes, pathways, mechanisms, and drugs. To navigate a network of associations, we developed an analysis platform, the Molecular Concepts Map. We demonstrate the utility of the approach by highlighting molecular concepts analyses of Myc pathway activation, breast cancer relapse, and retinoic acid treatment

ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets

Oncomine 3.0: Genes, Pathways, and Networks in a Collection of 18,000 Cancer Gene Expression Profiles

DNA microarrays have been widely applied to cancer transcriptome analysis; however, the majority of such data are not easily accessible or comparable. Furthermore, several important analytic approaches have been applied to microarray analysis; however, their application is often limited. To overcome these limitations, we have developed Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community. Our analysis has identified the genes, pathways, and networks deregulated across 18,000 cancer gene expression microarrays, spanning the majority of cancer types and subtypes. Here, we provide an update on the initiative, describe the database and analysis modules, and highlight several notable observations. Results from this comprehensive analysis are available at http://www.oncomine.org