Rheumatoid Arthritis Biomarkers and Drug Candidates from Bioinformatics Analysis

Figshare hosts a research dataset identifying potential oxidative stress-related biomarkers and therapeutic drugs for rheumatoid arthritis (RA). The analysis identified 281 differentially expressed oxidative stress-related genes (DEOSGs) and used three machine learning algorithms to determine three core biomarker genes. The dataset includes results from molecular docking and dynamics simulations, validated by in vitro cell model experiments.

Use Cases

• Validate the three identified core biomarker genes (e.g., CXCL10, EDNRB, MMP13) against independent RA transcriptomic datasets.
• Analyze the 281 DEOSGs for pathway enrichment in MAPK, AMPK, TNF, or Toll-like receptor signaling pathways.
• Use the molecular docking results to assess the binding affinity of the candidate drug narciclasine with target proteins.
• Correlate the expression patterns of core genes with immune cell subset distributions from the immune infiltration analysis.

Strengths

• Identifies 281 differentially expressed oxidative stress-related genes (DEOSGs) from transcriptome data.
• Integrates results from three machine learning algorithms, molecular docking, and molecular dynamics simulation.
• Includes experimental validation from an in vitro human RA synovial fibroblast model.

Limitations

• The dataset is small (144.8 KB), containing summarized analysis results rather than raw transcriptomic data.
• Biomarker identification is based on a specific bioinformatics pipeline; generalizability to other RA cohorts is untested.
• The temporal and geographic scope of the underlying GEO data is not specified, limiting reproducibility.

Provenance

Source

Transcriptome data obtained from the GEO database; analysis by Zhigang Shi.

Collection Method

Bioinformatics analysis integrating differential expression, weighted gene co-expression network analysis, machine learning, and molecular simulations.

Freshness

Last updated March 2026.