Control of oxidative stress by miRNA and impact on ovarian tumorigenesis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE26193 | GEO | 2011/11/01
SECONDARY ACCESSION(S): PRJNA135023
REPOSITORIES: GEO
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Project description:Transcriptome analysis of high-grade human ovarian adenocarcinomas. The hypothesis tested in the present study was that two reciprocal pathways, namely oxidative stress response and fibrosis, enable to build a hierarchical cluster of ovarian patients. Ovarian human tumor samples from Institut Curie were analysed on human genome U133 plus 2.0 array (Affymetrix). Microarray analysis was performed using Partek Genomic Suite software and transcriptomic data were normalized using GCRMA algorithm. Probes from oxidative stress response and fibrosis defined by Ingenuity software were used to build a hierarchical cluster of ovarian patients.
2011-11-01 | E-GEOD-26193 | biostudies-arrayexpress
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2010-05-25 | E-GEOD-12418 | biostudies-arrayexpress
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Project description:The clinical outcome for patients with ovarian cancer is difficult to predict. However, the use of biomarkers as additional prognostic factors may improve the outcome for these patients. In order to find novel candidate biomarkers, differences in gene expressions were analyzed in 54 stage III serous ovarian adenocarcinomas with oligonucleotide microarrays containing 27,000 unique probes. The microarray data was verified with quantitative real-time polymerase chain reaction for the genes TACC1, MUC5B and PRAME. With a hierarchical cluster analysis we detected a sub-group including 60% of the survivors. The gene expressions in tumours from patients in this sub-group of survivors were compared to the remaining tumours, and 204 genes were found to be differently expressed. We conclude that the sub-group of survivors might represent patients with favourable tumour biology and sensitivity to treatment. A selection of the 204 genes might be used as a predictive model to distinguish patients within and outside of this group. Alternative chemotherapy strategies could then be offered as first-line treatment, which could improve the clinical outcome for these patients. Keywords: Comparison between groups of samples
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2014-04-05 | E-GEOD-56531 | biostudies-arrayexpress
Project description:Schaner M., et al.; Mol Biol Cell. 2003 Nov;14(11):4376-86. Figure 1 Unsupervised hierarchical clustering of ovarian cell lines and ovarian cancers. Cell lines were not co-clustered with the tumor specimens, because these cell lines have a very prominent proliferation cluster (Perou et al., 1999; Ross et al., 2000) that significantly influences the clustering of the tumor samples if the two sample sets are not analyzed separately. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Keywords: Logical Set
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Project description:To demonstrate the use of a whole-genome oligonucleotide array to perform expression profiling on a series of microdissected late-stage, high-grade papillary serous ovarian adenocarcinomas to establish a prognostic gene signature correlating with survival and to identify novel survival factors in ovarian cancer. Advanced stage papillary serous tumors of the ovary are responsible for the majority of ovarian cancer deaths, yet the molecular determinants modulating patient survival are poorly characterized. We identify and validate a prognostic gene expression signature correlating with survival in a series of microdissected serous ovarian tumors. Experiment Overall Design: We identified 53 advanced stage, high-grade primary tumor specimens and 10 normal ovarian surface epithelium (OSE) brushings.
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Project description:We used unsupervised hierarchical clustering to analyse expression in primary ovarian tumors and associated abdominal deposits. GeneGo pathway analysis of differentially expressed genes between primary tumors and deposits revealed 4 of the top 10 pathways related to cytoskeleton remodeling and cell adhesion. Primary ovarian tumours and matched abdominal deposits were selected for RNA extraction and hybridization on Affymetrix microarrays.
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