The Experts below are selected from a list of 100194 Experts worldwide ranked by ideXlab platform
Eamonn R Maher - One of the best experts on this subject based on the ideXlab platform.
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identification of cyclin d1 and other novel targets for the von hippel lindau tumor suppressor gene by Expression Array analysis and investigation of cyclin d1 genotype as a modifier in von hippel lindau disease
Cancer Research, 2002Co-Authors: Malgorzata Zatyka, Nancy Fernandes Da Silva, Steven C Clifford, Mark R Morris, Michael S Wiesener, Kaiuwe Eckardt, Richard S Houlston, Frances M Richards, Farida Latif, Eamonn R MaherAbstract:Germ-line mutations in the von Hippel-Lindau (VHL) tumor suppressor disease are associated with a high risk of retinal and cerebellar hemangioblastomas, renal cell carcinoma (RCC), and, in some cases, pheochromocytoma (PHE). In addition, somatic mutation or epigenetic inactivation of the VHL gene occurs in most clear cell RCCs. VHL protein (pVHL) has a critical role in regulating proteasomal degradation of the HIF transcription factor, and VHL inactivation results in overExpression of many hypoxia-inducible mRNAs including vascular endothelial growth factor (VEGF). To identify novel pVHL target genes we investigated the effect of wild-type (WT) pVHL on the Expression of 588 cancer-related genes in two VHL-defective RCC cell lines. Expression Array analysis identified nine genes that demonstrated a >2-fold decrease in Expression in both RCC cell lines after restoration of WT pVHL. Three of the nine genes (VEGF, PAI-1, and LRP1) had been reported previously as pVHL targets and are known to be hypoxia-inducible. In addition, six novel targets were detected: cyclin D1 (CCND1), cell division protein kinase 6, collagen VIII alpha 1 subunit, CD59 glycoprotein precursor, integrin beta8, and interleukin 6 precursor IFN-beta2. We found no evidence that CCND1, cell division protein kinase 6, CD59, and integrin beta8 Expression was influenced by hypoxia suggesting that pVHL down-regulates these targets by a HIF-independent mechanism. A type 2C pVHL mutant (V188L), which is associated with a PHE only phenotype (and had been shown previously to retain the ability to promote HIF ubiquitylation), retained the ability to suppress CCND1Expression suggesting that loss of pVHL-mediated suppression of cyclin D1 is not necessary for PHE development in VHL disease. Other studies have suggested that: (a) genetic modifiers influence the phenotypic Expression of VHL disease; and (b) polymorphic variation at a CCND1 codon 242 A/G single nucleotide polymorphism (SNP) may influence cancer susceptibility or prognosis in some situations. Therefore, we analyzed the relationship between CCND1 genotype and phenotypic Expression of VHL disease. There was an association between the G allele and multiple retinal angiomas (P = 0.04), and risk of central nervous system hemangioblastomas (P = 0.05). These findings suggest that a variety of HIF-independent mechanisms may contribute to pVHL tumor suppressor activity and that polymorphic variation at one pVHL target influences the phenotypic Expression of VHL disease.
Masamichi Hayashi - One of the best experts on this subject based on the ideXlab platform.
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Identification of the collagen type 1 alpha 1 gene ( COL1A1 ) as a candidate survival-related factor associated with hepatocellular carcinoma
BMC Cancer, 2014Co-Authors: Masamichi Hayashi, Yoko Nishikawa, Yukiyasu Okamura, Shuji Nomoto, Mitsuhiro Hishida, Yoshikuni Inokawa, Mitsuro Kanda, Chie Tanaka, Daisuke Kobayashi, Suguru YamadaAbstract:Hepatocellular carcinoma (HCC) is one of the major causes of cancer-related death especially among Asian and African populations. It is urgent that we identify carcinogenesis-related genes to establish an innovative treatment strategy for this disease. Triple-combination Array analysis was performed using one pair each of HCC and noncancerous liver samples from a 68-year-old woman. This analysis consists of Expression Array, single nucleotide polymorphism Array and methylation Array. The gene encoding collagen type 1 alpha 1 (COL1A1) was identified and verified using HCC cell lines and 48 tissues from patients with primary HCC. Expression Array revealed that COL1A1 gene Expression was markedly decreased in tumor tissues (log2 ratio –1.1). The single nucleotide polymorphism Array showed no chromosomal deletion in the locus of COL1A1. Importantly, the methylation value in the tumor tissue was higher (0.557) than that of the adjacent liver tissue (0.008). We verified that Expression of this gene was suppressed by promoter methylation. Reactivation of COL1A1 Expression by 5-aza-2′-deoxycytidine treatment was seen in HCC cell lines, and sequence analysis identified methylated CpG sites in the COL1A1 promoter region. Among 48 pairs of surgical specimens, 13 (27.1%) showed decreased COL1A1 mRNA Expression in tumor sites. Among these 13 cases, 10 had promoter methylation at the tumor site. The log-rank test indicated that mRNA down-regulated tumors were significantly correlated with a poor overall survival rate (P = 0.013). Triple-combination Array analysis successfully identified COL1A1 as a candidate survival-related gene in HCCs. Epigenetic down-regulation of COL1A1 mRNA Expression might have a role as a prognostic biomarker of HCC.
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identification of the a kinase anchor protein 12 akap12 gene as a candidate tumor suppressor of hepatocellular carcinoma
Journal of Surgical Oncology, 2012Co-Authors: Masamichi Hayashi, Yoko Nishikawa, Yukiyasu Okamura, Suguru Yamada, Shin Takeda, Mitsuro Kanda, Shuji Nomoto, Hiroyuki Sugimoto, Tsutomu Fujii, Yasuhiro KoderaAbstract:Background Hepatocellular carcinoma (HCC) is a major health problem, and identification of new tumor-related genes is an urgent task. Methods To detect tumor-related genes effectively, we performed double-combination Array analysis, which consisted of an Expression Array and a single nucleotide polymorphism (SNP) Array of a single surgical HCC specimen. Results Expression Array analysis identified AKAP12 as one of the genes with reduced Expression in HCC tissues when compared with non-cancerous adjacent hepatic tissues. In addition, AKAP12 Expression levels in tumor tissues from 48 HCC samples were significantly lower (P < 0.001) than those in normal tissues, and the downregulation was significantly correlated with poor overall survival rate (P = 0.003). However, SNP Array analysis revealed that locus 6q24-q25 where AKAP12 was located did not show chromosomal deletion. In contrast, hypermethylation in the AKAP12 promoter regions was observed in 41 of 48 HCC samples. We then confirmed that AKAP12 gene re-Expression occurs after 5-aza-2′-deoxycytidine (5-aza-dC) treatment through direct sequence analysis of the AKAP12 promoter region in HCC cell lines. Conclusions The current data suggest that AKAP12 is downregulated in cancer tissues through promoter hypermethylation, and may have a role as a candidate tumor suppressor gene for HCC. J. Surg. Oncol. 2012;105:381–386. © 2011 Wiley Periodicals, Inc.
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Identification of the A kinase anchor protein 12 (AKAP12) gene as a candidate tumor suppressor of hepatocellular carcinoma.
Journal of Surgical Oncology, 2011Co-Authors: Masamichi Hayashi, Yoko Nishikawa, Yukiyasu Okamura, Suguru Yamada, Shin Takeda, Mitsuro Kanda, Shuji Nomoto, Hiroyuki Sugimoto, Tsutomu Fujii, Yasuhiro KoderaAbstract:Background Hepatocellular carcinoma (HCC) is a major health problem, and identification of new tumor-related genes is an urgent task. Methods To detect tumor-related genes effectively, we performed double-combination Array analysis, which consisted of an Expression Array and a single nucleotide polymorphism (SNP) Array of a single surgical HCC specimen. Results Expression Array analysis identified AKAP12 as one of the genes with reduced Expression in HCC tissues when compared with non-cancerous adjacent hepatic tissues. In addition, AKAP12 Expression levels in tumor tissues from 48 HCC samples were significantly lower (P
Jonathan K Pritchard - One of the best experts on this subject based on the ideXlab platform.
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exon specific qtls skew the inferred distribution of Expression qtls detected using gene Expression Array data
PLOS ONE, 2012Co-Authors: Jeanbaptiste Veyrieras, Daniel J Gaffney, Joseph K Pickrell, Yoav Gilad, Matthew Stephens, Jonathan K PritchardAbstract:Mapping of Expression quantitative trait loci (eQTLs) is an important technique for studying how genetic variation affects gene regulation in natural populations. In a previous study using Illumina Expression data from human lymphoblastoid cell lines, we reported that cis-eQTLs are especially enriched around transcription start sites (TSSs) and immediately upstream of transcription end sites (TESs). In this paper, we revisit the distribution of eQTLs using additional data from Affymetrix exon Arrays and from RNA sequencing. We confirm that most eQTLs lie close to the target genes; that transcribed regions are generally enriched for eQTLs; that eQTLs are more abundant in exons than introns; and that the peak density of eQTLs occurs at the TSS. However, we find that the intriguing TES peak is greatly reduced or absent in the Affymetrix and RNA-seq data. Instead our data suggest that the TES peak observed in the Illumina data is mainly due to exon-specific QTLs that affect 3′ untranslated regions, where most of the Illumina probes are positioned. Nonetheless, we do observe an overall enrichment of eQTLs in exons versus introns in all three data sets, consistent with an important role for exonic sequences in gene regulation.
Thomas Werner - One of the best experts on this subject based on the ideXlab platform.
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Yeast Expression-Array analysis goes molecular.
Trends in genetics : TIG, 2003Co-Authors: Thomas WernerAbstract:Combining Expression-Array analysis with molecular mechanisms of transcription control is an approach that is still in its infancy. Currently, the best understood eukaryotic organism is Saccharomyces cerevisiae, and it is with the genome of this organism that most progress in molecular Array analysis has been achieved. Molecular analysis, in the form of de novo motif detection, has recently been combined directly with Expression-level analysis, bypassing clustering solely by statistics of Expression levels or by restricting analysis to known transcription factor binding sites. This has identified several sets of transcription factors and their target genes, complementing similar approaches. These studies might significantly enhance similar analyses for human and mouse Expression Arrays, demonstrating the huge potential of integrated sequence and Expression-level analysis.
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Cluster analysis and promoter modelling as bioinformatics tools for the identification of target genes from Expression Array data.
Pharmacogenomics, 2001Co-Authors: Thomas WernerAbstract:Expression Arrays yield enormous amounts of data linking genes, via their cDNA sequences, to gene Expression patterns. This now allows the characterisation of gene Expression in normal and diseased tissues, as well as the response of tissues to the application of therapeutic reagents. Expression Array data can be analysed with respect to the underlying protein sequences, which facilitates the precise determination of when and where certain groups of genes are expressed. More recent developments of clustering algorithms take additional parameters of the experimental set-up into account, focusing more directly on co-regulated set of genes. However, the information concerning transcriptional regulatory networks responsible for the observed Expression patterns is not contained within the cDNA sequences used to generate the Arrays. Regulation of Expression is determined to a large extent by the promoter sequences of the individual genes (and/or enhancers). The complete sequence of the human genome now provides the molecular basis for the identification of many regulatory regions. Promoter sequences for specific cDNAs can be obtained reliably from genomic sequences by exon mapping. In the many cases in which cDNAs are 5'-incomplete, high quality promoter prediction tools can be used to locate promoters directly in the genomic sequence. Once sufficient numbers of promoter sequences have been obtained, a comparative promoter analysis of the co-regulated genes and groups of genes can be applied in order to generate models describing the higher order levels of transcription factor binding site organisation within these promoter regions. Such modules represent the molecular mechanisms through which regulatory networks influence gene Expression, and candidates can be determined solely by bioinformatics. This approach also provides a powerful alternative for elucidating the functional features of genes with no detectable sequence similarity, by linking them to other genes on the basis of their common promoter structures.
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Target Gene Identification From Expression Array Data by Promoter Analysis
Biomolecular engineering, 2001Co-Authors: Thomas WernerAbstract:Abstract DNA microchips and Expression Arrays yield enormous amounts of data linking cDNA sequences to gene Expression patterns. This now allows the characterization of gene Expression in normal and diseased tissues as well as the response of tissues to the application of therapeutic reagents. Software currently exists to analyze DNA Array/chip data with respect to corresponding mRNA sequences, which facilitates the precise determination of when and where certain groups of genes are expressed. The information concerning transcriptional regulatory networks responsible for the observed Expression patterns is not contained within the cDNA sequences used to generate the Arrays, but resides often within the promoter sequences of the individual genes (and/or enhancers). The complete sequence of the human genome will provide the molecular basis for the identification of such regulatory regions. Promoter sequences for specific cDNAs can be obtained reliably from genomic sequences simply by exon mapping. Promoter prediction tools can also be used to locate promoters directly in the genomic sequence in many cases in which cDNAs are 5′-incomplete. Once sufficient numbers of promoter sequences have been obtained, the comparative promoter analysis of the co-regulated genes and groups of genes can be applied in order to generate models describing the higher order levels of the transcription factor binding site organization within these promoter regions. As evident from several examples, this approach can identify promoter modules responsible for the common regulation of promoters solely by the application of bioinformatics methods. Such modules represent the molecular mechanisms through which regulatory networks influence gene Expression. Another advantage of this approach is that it also provides a powerful alternative for elucidating functional features of genes with no detectable sequence similarity, by linking them to other genes on the basis of their common promoter structures.
Malgorzata Zatyka - One of the best experts on this subject based on the ideXlab platform.
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identification of cyclin d1 and other novel targets for the von hippel lindau tumor suppressor gene by Expression Array analysis and investigation of cyclin d1 genotype as a modifier in von hippel lindau disease
Cancer Research, 2002Co-Authors: Malgorzata Zatyka, Nancy Fernandes Da Silva, Steven C Clifford, Mark R Morris, Michael S Wiesener, Kaiuwe Eckardt, Richard S Houlston, Frances M Richards, Farida Latif, Eamonn R MaherAbstract:Germ-line mutations in the von Hippel-Lindau (VHL) tumor suppressor disease are associated with a high risk of retinal and cerebellar hemangioblastomas, renal cell carcinoma (RCC), and, in some cases, pheochromocytoma (PHE). In addition, somatic mutation or epigenetic inactivation of the VHL gene occurs in most clear cell RCCs. VHL protein (pVHL) has a critical role in regulating proteasomal degradation of the HIF transcription factor, and VHL inactivation results in overExpression of many hypoxia-inducible mRNAs including vascular endothelial growth factor (VEGF). To identify novel pVHL target genes we investigated the effect of wild-type (WT) pVHL on the Expression of 588 cancer-related genes in two VHL-defective RCC cell lines. Expression Array analysis identified nine genes that demonstrated a >2-fold decrease in Expression in both RCC cell lines after restoration of WT pVHL. Three of the nine genes (VEGF, PAI-1, and LRP1) had been reported previously as pVHL targets and are known to be hypoxia-inducible. In addition, six novel targets were detected: cyclin D1 (CCND1), cell division protein kinase 6, collagen VIII alpha 1 subunit, CD59 glycoprotein precursor, integrin beta8, and interleukin 6 precursor IFN-beta2. We found no evidence that CCND1, cell division protein kinase 6, CD59, and integrin beta8 Expression was influenced by hypoxia suggesting that pVHL down-regulates these targets by a HIF-independent mechanism. A type 2C pVHL mutant (V188L), which is associated with a PHE only phenotype (and had been shown previously to retain the ability to promote HIF ubiquitylation), retained the ability to suppress CCND1Expression suggesting that loss of pVHL-mediated suppression of cyclin D1 is not necessary for PHE development in VHL disease. Other studies have suggested that: (a) genetic modifiers influence the phenotypic Expression of VHL disease; and (b) polymorphic variation at a CCND1 codon 242 A/G single nucleotide polymorphism (SNP) may influence cancer susceptibility or prognosis in some situations. Therefore, we analyzed the relationship between CCND1 genotype and phenotypic Expression of VHL disease. There was an association between the G allele and multiple retinal angiomas (P = 0.04), and risk of central nervous system hemangioblastomas (P = 0.05). These findings suggest that a variety of HIF-independent mechanisms may contribute to pVHL tumor suppressor activity and that polymorphic variation at one pVHL target influences the phenotypic Expression of VHL disease.
