Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of yeast grown in media with and without amino acids

ABSTRACT: Effect of Amino Acid Starvation on Ino4 Binding. Comparing SCD conditions to 4hrs of Amino Acid starvation

INSTRUMENT(S): GenePix 4000B [Axon Instruments]

ORGANISM(S): Saccharomyces cerevisiae

SUBMITTER: Ziv Bar-Joseph

PROVIDER: E-MEXP-906 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Reconstructing dynamic regulatory maps.

Ernst Jason J Vainas Oded O Harbison Christopher T CT Simon Itamar I Bar-Joseph Ziv Z

Molecular systems biology 20070116

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expr ...[more]

PMID: 17224918

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Project description:Background: Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene, in a group of breast cancer patients with long-term (12-16 years) follow-up. Methods: The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using TTGE and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples. Results: Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. The TP53 mutation status showed strong association with the ?basal-like? and ?ERBB2+? gene expression subgroups, and tumors with mutation had a characteristic gene expression pattern. Conclusions: TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease. Experiment set consisting of 80 primary breast carcinomas collected at Ulleval University Hospital (ULL-samples), Oslo, Norway from 1990-94, and one normal sample from breast reduction surgery.

Dataset Information

Transcription profiling of yeast grown in media with and without amino acids

Publications

Reconstructing dynamic regulatory maps.

Similar Datasets

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