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Gene expression analysis of pre-defined sets of neoplastic tissues

ABSTRACT: To identify a group of genes, characteristic of a single neoplasia and determinant for the risk to relapse, we have performed gene expression analysis of ductal breast carcinoma samples using Affymetrix oligonucletide microarray (HU133A). Biopses from 60 patients, 30 good prognosis (desease free from six to ten years after surgical treatment) and 30 poor prognosis, have been analyzed. From each group we have created six homogeneous pools to show, during the analysis, only genes expressed in a greatly different way. A reduced number of cases has been used to avoid dilution of genes expressed only in few samples. Analysis have identified 77 candidate genes with a specific ontological distribution that contained some genes that were linked to breast cancer by previous studies. Microarray results validation has been performed by real-time PCR analysis on 127 single cases of breast cancer. Gene function study and literature data allow to define our candidate genes as potential markers of prognosis. Keywords: Breast cancer gene expression analysis From a group of 130 frozen tumor samples, 30 cases of ductal invasive carcinoma with recurrence within 72 months from surgery (high-risk) and 30 without recurrence (low-risk) were selected for microarray analysis. From each group, 6 RNA pools of 5 samples each were prepared and analyzed on GeneChip Human Genome U133A oligonucleotide microarrays. Hybridizations were performed in technical duplicates, in two experimental sessions.

ORGANISM(S): Homo sapiens

SUBMITTER: Cimino Daniela

PROVIDER: S-ECPF-GEOD-9662 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ?anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

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Gene expression analysis of pre-defined sets of neoplastic tissues

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