NCBI Logo
GEO Logo
   NCBI > GEO > Accession DisplayHelp Not logged in | LoginHelp
GEO help: Mouse over screen elements for information.
          Go
Series GSE55235 Query DataSets for GSE55235
Status Public on Feb 21, 2014
Title Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation
Organism Homo sapiens
Experiment type Expression profiling by array
Summary 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 Kiendl’s 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.
 
Overall design Synovial tisssue from healthy joints, OA joints, and RA joints.
 
Contributor(s) Woetzel D, Huber R, Kupfer P, Pohlers D, Pfaff M, Driesch D, Häupl T, Koczan D, Stiehl P, Guthke R, Kinne RW
Citation(s) 24690414
Submission date Feb 21, 2014
Last update date Aug 10, 2018
Contact name Thomas Häupl
E-mail(s) thomas.haeupl@charite.de
Phone +49 30 450513293
Organization name Charité
Department Rheumatology
Street address Charitéplatz 1
City Berlin
ZIP/Postal code 10117
Country Germany
 
Platforms (1)
GPL96 [HG-U133A] Affymetrix Human Genome U133A Array
Samples (30)
GSM1332201 Synovial tisssue from healthy joint 1 (ND_1)
GSM1332202 Synovial tisssue from healthy joint 2 (ND_2)
GSM1332203 Synovial tisssue from healthy joint 3 (ND_3)
Relations
BioProject PRJNA238979

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE55235_RAW.tar 105.8 Mb (http)(custom) TAR (of CEL)
Raw data provided as supplementary file
Processed data included within Sample table

| NLM | NIH | GEO Help | Disclaimer | Accessibility |
NCBI Home NCBI Search NCBI SiteMap