Oxidative Stress Biomarkers and Drug Candidates for Rheumatoid Arthritis

281 differentially expressed oxidative stress-related genes were identified from rheumatoid arthritis transcriptome data. The research determined three core biomarker genes using machine learning algorithms and screened a potential therapeutic compound. Results include diagnostic model efficacy, immune cell infiltration analysis, and validation from an in vitro cell model.

Use Cases

• Validate the three identified core genes (e.g., CXCL10, EDNRB, MMP13) as diagnostic biomarkers for rheumatoid arthritis using the provided analysis.
• Investigate the association between the 281 oxidative stress-related genes and immune cell infiltration patterns described in the dataset.
• Explore the predicted binding affinity of the screened compound narciclasine with target proteins based on molecular docking and dynamics simulation results.
• Reproduce the functional enrichment analysis for pathways like MAPK, AMPK, TNF, and Toll-like receptor signaling using the gene list.

Strengths

• Analysis is based on 281 identified differentially expressed oxidative stress-related genes.
• Findings are validated through multiple methods including machine learning, molecular dynamics simulation, and in vitro cell model experiments.
• The dataset is openly shared under a CC BY 4.0 license, facilitating reuse.

Limitations

• The dataset is very small (12.9 KB), indicating it contains summary results rather than raw transcriptomic data.
• Specific sample sizes, row counts, and column structures for the underlying data are not provided.
• The findings are preliminary and require further experimental and clinical validation.

Provenance

Source

Transcriptome data obtained from the GEO database.

Collection Method

Integrative bioinformatics analysis combining differential expression, machine learning, and molecular dynamics simulations.

Freshness

Last updated March 2026.