Identification of genes potentially involved in the acquisition of androgen-independent and metastatic tumor growth in an autochthonous genetically engineered mouse prostate cancer model.

BACKGROUND: A major focus of prostate cancer research has been to identify genes that are deregulated during tumor progression, potentially providing diagnostic markers and therapeutic targets. METHODS: We have employed serial analysis of gene expression (SAGE) and microarray hybridization to identify alterations that occur during malignant transformation in the Transgenic Adenocarcinoma of the Mouse Prostate (TRAMP) model. Many of these alterations were validated by real-time PCR (rtPCR). RESULTS: We identified several hundred mRNAs that were deregulated. Cluster analysis of microarray profiles with samples from various stages of the disease demonstrated that androgen-independent (AI) primary tumors are similar to metastases; 180 transcripts have expression patterns suggesting an involvement in the genesis of late-stage tumors, and our data support a role for phospholipase A2 group IIA in the acquisition of their highly aggressive characteristics. CONCLUSIONS: Our analyses identified well-characterized genes that were previously known to be involved in prostate cancer, validating our study, and also uncovered transcripts that had not previously been implicated in prostate cancer progression.

New insights into ADPKD molecular pathways using combination of SAGE and microarray technologies.

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in the PKD1 or PKD2 gene, but cellular mechanisms of cystogenesis remain unclear. In an attempt to display the array of cyst-specific molecules and to elucidate the disease pathway, we have performed comprehensive high-throughput expression analysis of normal and ADPKD epithelia in a two-step fashion. First, we generated expression profiles of normal and cystic epithelia derived from kidney and liver using serial analysis of gene expression (SAGE). We found 472 and 499 differentially expressed genes with fivefold difference in liver and kidney libraries, respectively. These genes encode growth factors, transcription factors, proteases, apoptotic factors, molecules involved in cell-extracellular matrix interactions, and ion channels. As a second step, we constructed a custom cDNA microarray using a subset of the differentially regulated genes identified by SAGE and interrogated ADPKD patient samples. Subsequently, a set of differentially expressed genes was refined to 26 up-regulated and 48 down-regulated genes with ap value of <0.01. This study may provide valuable insights into the pathophysiology of ADPKD and suggest potential therapeutic targets.

Vascular gene expression in nonneoplastic and malignant brain.

Malignant gliomas are uniformly lethal tumors whose morbidity is mediated in large part by the angiogenic response of the brain to the invading tumor. This profound angiogenic response leads to aggressive tumor invasion and destruction of surrounding brain tissue as well as blood-brain barrier breakdown and life-threatening cerebral edema. To investigate the molecular mechanisms governing the proliferation of abnormal microvasculature in malignant brain tumor patients, we have undertaken a cell-specific transcriptome analysis from surgically harvested nonneoplastic and tumor-associated endothelial cells. SAGE-derived endothelial cell gene expression patterns from glioma and nonneoplastic brain tissue reveal distinct gene expression patterns and consistent up-regulation of certain glioma endothelial marker genes across patient samples. We define the G-protein-coupled receptor RDC1 as a tumor endothelial marker whose expression is distinctly induced in tumor endothelial cells of both brain and peripheral vasculature. Further, we demonstrate that the glioma-induced gene, PV1, shows expression both restricted to endothelial cells and coincident with endothelial cell tube formation. As PV1 provides a framework for endothelial cell caveolar diaphragms, this protein may serve to enhance glioma-induced disruption of the blood-brain barrier and transendothelial exchange. Additional characterization of this extensive brain endothelial cell gene expression database will provide unique molecular insights into vascular gene expression.

Correction of sequence-based artifacts in serial analysis of gene expression.

MOTIVATION: Serial Analysis of Gene Expression (SAGE) is a powerful technology for measuring global gene expression, through rapid generation of large numbers of transcript tags. Beyond their intrinsic value in differential gene expression analysis, SAGE tag collections afford abundant information on the size and shape of the sample transcriptome and can accelerate novel gene discovery. These latter SAGE applications are facilitated by the enhanced method of Long SAGE. A characteristic of sequencing-based methods, such as SAGE and Long SAGE is the unavoidable occurrence of artifact sequences resulting from sequencing errors. By virtue of their low-random incidence, such tag errors have minimal impact on differential expression analysis. However, to fully exploit the value of large SAGE tag datasets, it is desirable to account for and correct tag artifacts. RESULTS: We present estimates for occurrences of tag errors, and an efficient error correction algorithm. Error rate estimates are based on a stochastic model that includes the Polymerase chain reaction and sequencing error contributions. The correction algorithm, SAGEScreen, is a multi-step procedure that addresses ditag processing, estimation of empirical error rates from highly abundant tags, grouping of similar-sequence tags and statistical testing of observed counts. We apply SAGEScreen to Long SAGE libraries and compare error rates for several processing scenarios. Results with simulated tag collections indicate that SAGEScreen corrects 78% of recoverable tag errors and reduces the occurrences of singleton tags. AVAILABILITY: The SAGEScreen software is available for academic users from the first author.