Gene expression profiling of noninvasive primary urothelial tumours using microarrays

At present, the mechanism leading to bladder cancer is still poorly understood, and our knowledge about early events in tumorigenesis is limited. This study describes the changes in gene expression occurring during the neoplastic transition from normal bladder urothelium to primary Ta tumours. Using DNA microarrays, we identified novel differentially expressed genes in Ta tumours compared to normal bladder, and genes that were altered in high-grade tumours. Among the mostly changed genes between normal bladder and Ta tumours, we found genes related to the cytoskeleton (keratin 7 and syndecan 1), and transcription (high mobility group AT-hook 1). Altered genes in high-grade tumours were related to cell cycle (cyclin-dependent kinase 4) and transcription (jun d proto-oncogene). Furthermore, we showed the presence of high keratin 7 transcript expression in bladder cancer, and Western blotting analysis revealed three major molecular isoforms of keratin 7 in the tissues. These could be detected in urine sediments from bladder tumour patients

Expression of the Stress Response Oncoprotein LEDGF/p75 in Human Cancer: A Study of 21 Tumor Types

Oxidative stress-modulated signaling pathways have been implicated in carcinogenesis and therapy resistance. The lens epithelium derived growth factor p75 (LEDGF/p75) is a transcription co-activator that promotes resistance to stress-induced cell death. This protein has been implicated in inflammatory and autoimmune conditions, HIV-AIDS, and cancer. Although LEDGF/p75 is emerging as a stress survival oncoprotein, there is scarce information on its expression in human tumors. The present study was performed to evaluate its expression in a comprehensive panel of human cancers. Transcript expression was examined in the Oncomine cancer gene microarray database and in a TissueScan Cancer Survey Panel quantitative polymerase chain reaction (Q-PCR) array. Protein expression was assessed by immunohistochemistry (IHC) in cancer tissue microarrays (TMAs) containing 1735 tissues representing single or replicate cores from 1220 individual cases (985 tumor and 235 normal tissues). A total of 21 major cancer types were analyzed. Analysis of LEDGF/p75 transcript expression in Oncomine datasets revealed significant upregulation (tumor vs. normal) in 15 out of 17 tumor types. The TissueScan Cancer Q-PCR array revealed significantly elevated LEDGF/p75 transcript expression in prostate, colon, thyroid, and breast cancers. IHC analysis of TMAs revealed significant increased levels of LEDGF/p75 protein in prostate, colon, thyroid, liver and uterine tumors, relative to corresponding normal tissues. Elevated transcript or protein expression of LEDGF/p75 was observed in several tumor types. These results further establish LEDGF/p75 as a cancer-related protein, and provide a rationale for ongoing studies aimed at understanding the clinical significance of its expression in specific human cancers

HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer.

Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice