Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Analysis of early primary breast cancer to identify prognostic markers and associated pathways: mRNA and miRNA profiling

ABSTRACT: This SuperSeries is composed of the following subset Series: GSE22216: microRNA expression profiling of early primary breast cancer to identify prognostic markers and associated pathways GSE22219: Gene expression profiling of early primary breast cancer to identify prognostic markers and associated pathways Refer to individual Series. Supplementary file: Shows correspondence between mRNA and miRNA samples.

ORGANISM(S): Homo sapiens

SUBMITTER: Francesca Buffa

PROVIDER: E-GEOD-22220 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer.

Buffa Francesca M FM Camps Carme C Winchester Laura L Snell Cameron E CE Gee Harriet E HE Sheldon Helen H Taylor Marian M Harris Adrian L AL Ragoussis Jiannis J

Cancer research 20110707 17

microRNA expression profiling plays an emerging role in cancer classification and identification of therapeutic strategies. In this study, we have evaluated the benefits of a joint microRNA-mRNA analysis in breast cancer. Matched mRNA and microRNA global expression profiling was conducted in a well-annotated cohort of 207 cases with complete 10-year follow-up. Penalized Cox regression including microRNA expression, mRNA expression, and clinical covariates was used to identify microRNAs associate ...[more]

PMID: 21737487

Similar Datasets

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

Analysis of early primary breast cancer to identify prognostic markers and associated pathways: mRNA and miRNA profiling

Publications

microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure