GSE55457

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation [Jena]

Whole-study status Not suitable

Dataset does not meet the current suitability threshold.

Whole-study downloads — these do not apply your sample requirements.

Selected cohort requirements

Samples meeting selected requirements: 0 cases / 0 controls

View matching samples

13 case / 10 control samples elsewhere in the study have incomplete or conflicting evidence; they are not included above

Patient pairs verified in indexed sample metadata: 0

A case/control comparison meeting these requirements is not confirmed.

Counts describe samples, not independent patients. Pair verification uses explicit subject identifiers in indexed sample metadata. Zero verified pairs does not establish that the study is unpaired; reported matched-normal design alone is insufficient.

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

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.

Source Evidence

4 sources
Source Contribution Status
GEO

Original dataset metadata, sample metadata, source links, and GEO data availability flags.

Sample records
33 GSM samples
Primary source
recount3

Planned trusted source for processed count availability.

No match found
Expression Atlas

Planned source for curated expression experiment links.

No match found
PubMed

Primary GEO citations and related PubMed articles that mention this dataset.

21 linked publications 21 DOIs

Primary publication

  1. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Arthritis research & therapy, 2014

Related publications

  1. Gender inequality in osteoarthritis: a bioinformatics perspective. Clinical rheumatology, 2026
  2. Identification of potential key efferocytosis-related genes in rheumatoid arthritis using machine learning. Clinical rheumatology, 2026
  3. CD27 Neutralization Attenuates Rheumatoid Arthritis by Suppressing. Biochemical genetics, 2026
  4. Integrating network toxicology, molecular docking and experimental validation reveals potential mechanisms of DEHP in osteoarthritis. Experimental gerontology, 2026
  5. G protein-coupled receptor-related genes matrix metallopeptidase 9 and tachykinin precursor 1 promote osteoarthritis progression and influence the immune microenvironment. International journal of biological macromolecules, 2025
  6. Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration. Journal of orthopaedics, 2026
  7. MSX2 serves as a chondroprotective factor in osteoarthritis: insights from expression analysis and functional validation studies. Experimental cell research, 2026
  8. Elucidation of hub cross-talk genes as potential biomarkers and drug prediction for chronic periodontitis and rheumatoid arthritis: A bioinformatics analysis. Medicine, 2025
  9. Analysis of key genes and pathways of knee osteoarthritis based on GEO data mining. Medicine, 2025
  10. Integrative transcriptomic analysis identifies synovium-derived biomarkers for OA-related synovitis and builds a validated diagnostic nomogram. Medicine, 2026
  11. Identification of Autophagy-Associated Molecular Signatures in Osteoarthritis Revealed by Integrative Multi-Omics and Experimental Validation. Chemical biology & drug design, 2026
  12. Programmed cell death-related gene S100A9 promotes macrophage M1 polarization and chondrocyte apoptosis in rheumatoid arthritis. Biology direct, 2026
  13. [Identification of efferocytosis-related genes in osteoarthritis and prediction of traditional Chinese medicines based on bioinformatics and machine learning]. Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026
  14. Analysis of common differential gene expression between rheumatoid arthritis and ulcerative colitis. PloS one, 2026
  15. Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics. PloS one, 2026
  16. KIF11 promotes rheumatoid arthritis pathogenesis by activating M1 macrophage polarization and promoting inflammatory cytokine secretion. PloS one, 2026
  17. Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis. Journal of multidisciplinary healthcare, 2025
  18. Single-Cell and Mendelian Randomization Analyses Identify Macrophage Polarization and Efferocytosis Biomarkers in Osteoarthritis and Reveal Their Regulatory Mechanisms. Iranian journal of biotechnology, 2026
  19. Immune cells with senescence-related transcriptional signatures orchestrate the inflammatory continuum in osteoarthritis synovium: a single-cell and machine learning study. Frontiers in immunology, 2026
  20. Mendelian Randomization and Transcriptome Analyses Reveal Important Roles for CEBPB and CX3CR1 in Osteoarthritis. Bioengineering (Basel, Switzerland), 2025
Available

Metadata Quality Components

Analysis Readiness

Review before analysis
85% DE readiness

Contrast completeness

25/25 pts

Both case and control groups are present in the proposed grouping.

Minimum group size

20/20 pts

Each contrast group has at least 2 samples.

Processed data availability

20/20 pts

Processed data is marked available in the GEO index.

Sample decision clarity

3/15 pts

Unassigned or review-needed samples remain before analysis.

Blocking warnings

7/10 pts

Warnings are present, but none are critical blockers.

Metadata normalization

10/10 pts

Metadata quality is high enough to support downstream handoff.

Blockers

  • 10 samples are still unassigned.

Next Steps

  • Resolve unassigned or review-needed samples in the grouping table.

Analysis Preparation Checklist

4/6 ready

Group labels reviewed

Needs review

Siftome proposed case and control labels, but no reviewer confirmation is stored yet.

Open the sample grouping CSV and confirm group labels before analysis.

Minimum sample count met

Ready

Both groups meet the minimum of 2 samples.

Confirm whether the cohort is large enough for the intended statistical analysis.

Processed counts available

Ready

Processed data availability is marked true in the GEO index.

Verify that downloaded processed files contain usable count or expression identifiers.

Ambiguous samples handled

Needs review

0 review-needed samples and 10 unassigned samples remain.

Resolve ambiguous and unassigned samples before finalizing the contrast.

Exclusion decisions recorded

Ready

No hard-excluded samples were detected by the current rules.

Record any manual exclusions in the downstream analysis notes.

Source traceability preserved

Ready

The dataset and sample accessions can be traced back to GEO.

Keep GEO accessions with exported metadata.

Bioinformatician Review

Bioinformatician review required

Ask a bioinformatician to review grouping, count availability, exclusions, and design before DE.

Analysis readiness has blockers that must be resolved before DE.

One or more samples are unassigned and need manual grouping decisions.

Assay context may require expert handling before DESeq2-style analysis.

Why This Is Ranked Here

Tumor vs normal
Not suitable Dataset does not meet the current suitability threshold.

Applied Review Decisions

No active review decisions affect this ranking.

Derived Classifications

  • Assay differs from search intent Microarray Search intent expects bulk RNA-seq. Assay came from Normalization rules.
  • Disease context Rheumatoid arthritis Disease context came from Normalization rules.
  • Likely case group Rheumatoid arthritis 13 samples
  • Likely control group Healthy control 10 samples

Intent Interpretation

  • Downgrade or exclusion reason Downgraded assay: microarray cohorts are separated from the RNA-seq intent. Derived from current-intent ranking reasons.

Ranking Facts

Supporting evidence

  • Comparison: Disease/model vs control. Specimen: Tissue. Comparison suitability: 0%.
  • Interpreted disease metadata: Rheumatoid arthritis.
  • Processed data is available for downstream review.
  • External evidence: PubMed links 21 publications; 21 DOIs available: PMID 24690414, PMID 40765736, PMID 40885352, PMID 40958328, PMID 41007174.
  • Publication context: Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.. Arthritis research & therapy, 2014. PMID 24690414. DOI 10.1186/ar4526.
  • Publication context: Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis.. Journal of multidisciplinary healthcare, 2025. PMID 40765736. DOI 10.2147/JMDH.S537507.
  • Publication context: G protein-coupled receptor-related genes matrix metallopeptidase 9 and tachykinin precursor 1 promote osteoarthritis progression and influence the immune microenvironment.. International journal of biological macromolecules, 2025. PMID 40885352. DOI 10.1016/j.ijbiomac.2025.147208.
  • Publication context: 18 additional linked PubMed publications are available.

Cautions

  • Downgraded assay: microarray cohorts are separated from the RNA-seq intent.
  • Disease/control design without a tumor/normal comparison is outside tumor/normal triage.
  • Microarray expression context downgraded below primary bulk RNA-seq for the default tissue ranking.
  • Overall score capped because comparison suitability is weak for the selected search intent.
  • Overall score capped because microarray cohorts should be compared separately from RNA-seq cohorts.
  • Comparison suitability score is below the Phase 1 recommended threshold.

Dataset Warnings

Normalized Fields

102 fields

Sample Group Preview

33 rows shown
Show Hide
Study totals: Case 13 | Control 10 | Unassigned 10 | Excluded 0 | Ambiguous 0 33 rows shown
Group Role Accession Title Source / characteristics Reason
Rheumatoid arthritis Case GSM1337314 Rheumatoid arthritis, synovial membrane patient EB_20 (U133A) Synovial membrane patient EB_20 (rheumatoid arthritis) Gender: Male; Age: 75; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337315 Rheumatoid arthritis, synovial membrane patient EB_27 (U133A) Synovial membrane patient EB_27 (rheumatoid arthritis) Gender: Female; Age: 79; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337316 Rheumatoid arthritis, synovial membrane patient EB_30 (U133A) Synovial membrane patient EB_30 (rheumatoid arthritis) Gender: Female; Age: 63; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337317 Rheumatoid arthritis, synovial membrane patient EB_35 (U133A) Synovial membrane patient EB_35 (rheumatoid arthritis) Gender: Female; Age: 66; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337318 Rheumatoid arthritis, synovial membrane patient EB_40 (U133A) Synovial membrane patient EB_40 (rheumatoid arthritis) Gender: Female; Age: 64; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337319 Rheumatoid arthritis, synovial membrane patient EB_41 (U133A) Synovial membrane patient EB_41 (rheumatoid arthritis) Gender: Female; Age: 63; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337320 Rheumatoid arthritis, synovial membrane patient EB_56 (U133A) Synovial membrane patient EB_56 (rheumatoid arthritis) Gender: Female; Age: 46; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337321 Rheumatoid arthritis, synovial membrane patient EB_74 (U133A) Synovial membrane patient EB_74 (rheumatoid arthritis) Gender: Female; Age: 71; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337322 Rheumatoid arthritis, synovial membrane patient EB_108 (U133A) Synovial membrane patient EB_108 (rheumatoid arthritis) Gender: Female; Age: 72; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337323 Rheumatoid arthritis, synovial membrane patient EB_109 (U133A) Synovial membrane patient EB_109 (rheumatoid arthritis) Gender: Female; Age: 2; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337324 Rheumatoid arthritis, synovial membrane patient EB_110 (U133A) Synovial membrane patient EB_110 (rheumatoid arthritis) Gender: Male; Age: 47; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337325 Rheumatoid arthritis, synovial membrane patient EB_124 (U133A) Synovial membrane patient EB_124 (rheumatoid arthritis) Gender: Male; Age: 59; Clinical status: Rheumatoid arthritis Case marker detected.
Rheumatoid arthritis Case GSM1337326 Rheumatoid arthritis, synovial membrane patient EB_141 (U133A) Synovial membrane patient EB_141 (rheumatoid arthritis) Gender: Female; Age: 73; Clinical status: Rheumatoid arthritis Case marker detected.
Healthy control Control GSM1337304 Normal, synovial membrane patient EB_125 (U133A) Synovial membrane patient EB_125 (normal controls) Gender: Male; Age: 61; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337305 Normal, synovial membrane patient EB_184 (U133A) Synovial membrane patient EB_184 (normal controls) Gender: Male; Age: 64; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337306 Normal, synovial membrane patient EB_188 (U133A) Synovial membrane patient EB_188 (normal controls) Gender: Female; Age: 78; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337307 Normal, synovial membrane patient EB_228 (U133A) Synovial membrane patient EB_228 (normal controls) Gender: Male; Age: 65; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337308 Normal, synovial membrane patient J_19 (U133A) Synovial membrane patient J_19 (normal controls) Gender: Male; Age: 53; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337309 Normal, synovial membrane patient NST_1 (U133A) Synovial membrane patient NST_1 (normal controls) Gender: Male; Age: 68; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337310 Normal, synovial membrane patient J_2 (U133A) Synovial membrane patient J_2 (normal controls) Gender: Female; Age: 29; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337311 Normal, synovial membrane patient J_6 (U133A) Synovial membrane patient J_6 (normal controls) Gender: Male; Age: 17; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337312 Normal, synovial membrane patient J_7 (U133A) Synovial membrane patient J_7 (normal controls) Gender: Male; Age: 39; Clinical status: Normal control Sample-local control marker detected.
Healthy control Control GSM1337313 Normal, synovial membrane patient J_12 (U133A) Synovial membrane patient J_12 (normal controls) Gender: Male; Age: 36; Clinical status: Normal control Sample-local control marker detected.
Unassigned Unassigned GSM1337327 Osteoarthritis, synovial membrane patient EB_21 (U133A) Synovial membrane patient EB_21 (osteoarthrisits) Gender: Female; Age: 77; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337328 Osteoarthritis, synovial membrane patient EB_24 (U133A) Synovial membrane patient EB_24 (osteoarthrisits) Gender: Female; Age: 71; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337329 Osteoarthritis, synovial membrane patient EB_54 (U133A) Synovial membrane patient EB_54 (osteoarthrisits) Gender: Female; Age: 76; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337330 Osteoarthritis, synovial membrane patient EB_90 (U133A) Synovial membrane patient EB_90 (osteoarthrisits) Gender: Female; Age: 61; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337331 Osteoarthritis, synovial membrane patient EB_94 (U133A) Synovial membrane patient EB_94 (osteoarthrisits) Gender: Female; Age: 75; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337332 Osteoarthritis, synovial membrane patient EB_97 (U133A) Synovial membrane patient EB_97 (osteoarthrisits) Gender: Male; Age: 78; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337333 Osteoarthritis, synovial membrane patient EB_99 (U133A) Synovial membrane patient EB_99 (osteoarthrisits) Gender: Male; Age: 69; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337334 Osteoarthritis, synovial membrane patient EB_100 (U133A) Synovial membrane patient EB_100 (osteoarthrisits) Gender: Female; Age: 71; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337335 Osteoarthritis, synovial membrane patient EB_104 (U133A) Synovial membrane patient EB_104 (osteoarthrisits) Gender: Female; Age: 80; Clinical status: Osteoarthritis No explicit case or control marker detected.
Unassigned Unassigned GSM1337336 Osteoarthritis, synovial membrane patient EB_140 (U133A) Synovial membrane patient EB_140 (osteoarthrisits) Gender: Female; Age: 66; Clinical status: Osteoarthritis No explicit case or control marker detected.

Use as Discovery Cohort

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Carry Siftome's disease, organism, specimen, assay, data type, and accession into a validation project. Uncertain fields remain Unknown.