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GSE9600
Homo sapiens
10 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Recurrent and/or metastatic head and neck squamous cell carcinoma (HNSCC) remains one of the most difficult cancers to treat with limited chemotherapeutic options. Here, we describe a patient with HNSCC who had complete response to methotrexate (MTX) after progressing on multiple cytotoxic agents; cetuximab, a monoclonal antibody (mAb) against Epidermal Growth Factor Receptor (EGFR), and AMG 479, a mAb against Insulin-like Growth Factor-1 Receptor (IGF-1R).
Publication Title
Insulin-like growth factor-1 receptor inhibitor, AMG-479, in cetuximab-refractory head and neck squamous cell carcinoma.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 5 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
To determine the mechanism of cetuximab-resistance in head and neck cancer, a cetuximab-sensitive cell line (SCC1) and its cetuximab-resistant derivative (1Cc8) were analyzed for differentially expressed genes using DNA microarrays. 900 differentially expressed genes were found using the statistical cut-off point of one-way ANOVA with FDR less than 1%.
Publication Title
Regulation of heparin-binding EGF-like growth factor by miR-212 and acquired cetuximab-resistance in head and neck squamous cell carcinoma.
Sample Metadata Fields
Cell line
View Samples- Bos taurus
- 13 Downloadable Samples
- Bovine Gene 1.0 ST Array (bovgene10st)
Description
After ovulation, somatic cells of the ovarian follicle (theca and granulosa cells) become the small and large luteal cells of the corpus luteum. Aside from known cell type-specific receptors and steroidogenic enzymes, little is known about the differences in the gene expression profiles of these four cell types. Analysis of the RNA present in each bovine cell type using Affymetrix microarrays yielded new cell-specific genetic markers, functional insight into the behavior of each cell type via Gene Ontology Annotations and Ingenuity Pathway Analysis, and evidence of small and large luteal cell lineages using Principle Component Analysis. Enriched expression of select genes for each cell type was validated by qPCR. This expression analysis offers insight into the lineage and differentiation process that transforms somatic follicular cells into luteal cells.
Publication Title
Gene expression profiling of bovine ovarian follicular and luteal cells provides insight into cellular identities and functions.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Genome U95 Version 2 Array (hgu95av2)
Description
mRNA expression levels in synovial fibroblasts in 6 rheumatoid arthritis patients versus 6 osteoarthritis patients.
Publication Title
Constitutive upregulation of the transforming growth factor-beta pathway in rheumatoid arthritis synovial fibroblasts.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 57 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background
Publication Title
Adapted Boolean network models for extracellular matrix formation.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 57 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background. Rheumatoid arthritis (RA) is a chronic inflammatory and destructive joint disease, characterized by overexpression of pro-inflammatory/-destructive genes and other activating genes (e.g., proto-oncogenes) in the synovial membrane (SM). The gene expression in disease is often characterized by significant inter-individual variances via specific synchronization/ desynchronization of gene expression. To elucidate the contribution of the variance to the pathogenesis of disease, expression variances were tested in SM samples of RA patients, osteoarthritis (OA) patients, and normal controls (NC).
Publication Title
Identification of intra-group, inter-individual, and gene-specific variances in mRNA expression profiles in the rheumatoid arthritis synovial membrane.
Sample Metadata Fields
Sex, Age, Disease
View Samples- Homo sapiens
- 32 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 29 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Specimen part, Disease, Disease stage
View Samples- Homo sapiens
- 15 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for RA), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.
Publication Title
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.
Sample Metadata Fields
Sex, Age
View Samples- Caenorhabditis elegans
- 5 Downloadable Samples
Illumina HiSeq 2000
Description
Transciptomic analysis of germline tumor cells to understand the role of autophagy and neuronal differentiation in lifespan extension. Overall design: Methods: Worms were grown on control L444 seeded plates or gld-1 RNAi seeded plates and subjected to RNA isolation and sequencing using standard Illumina protocols. Conclusions: Fasting of animals expressing tumors increases their lifespan two-fold through autophagy and modular changes in transcription as well as metabolism.
Publication Title
Autophagy and modular restructuring of metabolism control germline tumor differentiation and proliferation in C. elegans.
Sample Metadata Fields
Subject
View Samples