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.
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, that the polycomb group protein enhancer of zeste homolog 2 (EZH2) is overexpressed in hormone-refractory, metastatic prostate cancer. Small interfering RNA (siRNA) duplexes 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.
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.
Multiple, complex molecular events characterize cancer development and progression1,2. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumors, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high throughput liquid and gas chromatography-based mass spectrometry, we profiled more than 1126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer, and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly elevated during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also elevated in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase (GNMT), the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase (SARDH), induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Taken together, we profiled the metabolomic alterations of prostate cancer progression revealing sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
The Polycomb Group Protein EZH2 is a transcriptional repressor involved in controlling cellular memory and has been linked to aggressive prostate cancer. Here we investigate the functional role of EZH2 in cancer cell invasion and breast cancer progression. EZH2 transcript and protein were consistently elevated in invasive breast carcinoma compared with normal breast epithelia. Tissue microarray analysis, which included 917 samples from 280 patients, demonstrated that EZH2 protein levels were strongly associated with breast cancer aggressiveness. Overexpression of EZH2 in immortalized human mammary epithelial cell lines promotes anchorageindependent growth and cell invasion. EZH2-mediated cell invasion required an intact SET domain and histone deacetylase activity. This study provides compelling evidence for a functional link between dysregulated cellular memory, transcriptional repression, and neoplastic transformation.
Prostate cancer is the most frequently diagnosed cancer in American men. Screening for prostate-specific antigen (PSA) has led to earlier detection of prostate cancer, but elevated serum PSA levels may be present in non-malignant conditions such as benign prostatic hyperlasia (BPH). Characterization of gene-expression profiles that molecularly distinguish prostatic neoplasms may identify genes involved in prostate carcinogenesis, elucidate clinical biomarkers, and lead to an improved classification of prostate cancer. Using microarrays of complementary DNA, we examined gene-expression profiles of more than 50 normal and neoplastic prostate specimens and three common prostate-cancer cell lines. Signature expression profiles of normal adjacent prostate (NAP), BPH, localized prostate cancer, and metastatic, hormone-refractory prostate cancer were determined. Here we establish many associations between genes and prostate cancer. We assessed two of these genes-hepsin, a transmembrane serine protease, and pim-1, a serine/threonine kinase-at the protein level using tissue microarrays consisting of over 700 clinically stratified prostate-cancer specimens. Expression of hepsin and pim-1 proteins was significantly correlated with measures of clinical outcome. Thus, the integration of cDNA microarray, high-density tissue microarray, and linked clinical and pathology data is a powerful approach to molecular profiling of human cancer.
Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.T o identify genes potentially important in cancer, scientists have compared the global gene expression profiles of cancer tissue and corresponding normal tissue (1-11). Such analyses usually generate hundreds of genes differentially expressed in cancer relative to normal tissue, making it difficult to distinguish the genes that play a critical role in the neoplastic phenotype from those that represent epiphenomena or are spuriously differentially expressed. Another common experimental design is to compare cancer samples based on their degree of progression, as determined by histological grade, invasiveness, or metastatic potential (2,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). For example, it is known that high-grade undifferentiatedappearing cancers tend to behave more aggressively than their low-grade counterparts, often leading to poorer patient outcomes. To understand the mechanisms by which this progression occurs, many studies have compared the global gene expression profiles of undifferentiated and well differentiated cancers of the same origin. But again, like the ''cancer vs. normal'' studies, these analyses can also yield hundreds of differentially expressed genes. Thus, it remains a critical problem to elucidate the essential transcriptional features of neoplastic transformation and progression both to direct future research and to define candidate therapeutic targets.A logical approach for identifying the essential features of a process, given a large set of possibilities observed in a variety of independent systems, is to search for the intersection of observed possibilities across the set of systems, because it is expected that the essential features will be overrepresented and the system-specific, epiphenomenal, and spurious features will be underrepresented. Given the multitude of studies that have attempted to capture the cancer type-specific gene expression programs of neoplastic transformation and progressi...
Molecular profiling of cancer at the transcript level has become routine. Large-scale analysis of proteomic alterations during cancer progression has been a more daunting task. Here, we employed high-throughput immunoblotting in order to interrogate tissue extracts derived from prostate cancer. We identified 64 proteins that were altered in prostate cancer relative to benign prostate and 156 additional proteins that were altered in metastatic disease. An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed, revealing only 48%-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors.
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