The landscape of long noncoding RNAs in the human transcriptome.

Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes.

Chimeric transcript discovery by paired-end transcriptome sequencing.

Recurrent gene fusions are a prevalent class of mutations arising from the juxtaposition of 2 distinct regions, which can generate novel functional transcripts that could serve as valuable therapeutic targets in cancer. Therefore, we aim to establish a sensitive, high-throughput methodology to comprehensively catalog functional gene fusions in cancer by evaluating a paired-end transcriptome sequencing strategy. Not only did a paired-end approach provide a greater dynamic range in comparison with single read based approaches, but it clearly distinguished the high-level "driving" gene fusions, such as BCR-ABL1 and TMPRSS2-ERG, from potential lower level "passenger" gene fusions.

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.

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.

Mining for regulatory programs in the cancer transcriptome.

DNA microarrays have been widely applied to cancer transcriptome analysis. The Oncomine database contains a large collection of such data, as well as hundreds of derived gene-expression signatures. We studied the regulatory mechanisms responsible for gene deregulation in these cancer signatures by searching for the coordinate regulation of genes with common transcription factor binding sites.

C5a-induced gene expression in human umbilical vein endothelial cells.

The endothelium plays a critical role in the inflammatory process. The complement activation product, C5a, is known to have proinflammatory effects on the endothelium, but the molecular mechanisms remain unclear. We have used cDNA microarray analysis to assess gene expression in human umbilical vein endothelial cells (HUVECs) that were stimulated with human C5a in vitro.

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, that the polycomb group protein enhancer of zeste homolog 2 (EZH2) is overexpressed in hormone-refractory, metastatic prostate cancer.

alpha-Methylacyl-CoA racemase: expression levels of this novel cancer biomarker depend on tumor differentiation.

alpha-Methylacyl-CoA racemase (AMACR) has previously been shown to be a highly sensitive marker for colorectal and clinically localized prostate cancer (PCa). However, AMACR expression was down-regulated at the transcript and protein level in hormone-refractory metastatic PCa, suggesting a hormone-dependent expression of AMACR. To further explore the hypothesis that AMACR is hormone regulated and plays a role in PCa progression AMACR protein expression was characterized in a broad range of PCa samples treated with variable amounts and lengths of exogenous anti-androgens.

Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies. To extract maximum value from the accumulating mass of publicly available cancer gene expression data, methods are needed to evaluate, integrate, and intervalidate multiple datasets. Here we demonstrate a statistical model for performing meta-analysis of independent microarray datasets.

alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer.

Context: Molecular profiling of prostate cancer has led to the identification of candidate biomarkers and regulatory genes. Discoveries from these genome-scale approaches may have applicability in the analysis of diagnostic prostate specimens.

Objectives: To determine the expression and clinical utility of alpha-methylacyl coenzyme A racemase (AMACR), a gene identified as being overexpressed in prostate cancer by global profiling strategies.