Osteoarthritis Immune Microenvironment Biomarkers from GEO Gene Expression Analysis

A 767.4 KB CSV file published on figshare by Zhengyao Zhang in April 2026. The dataset contains results from a bioinformatics study exploring immune modulation in osteoarthritis, identifying key biomarkers and the role of PTPRC. Analysis methods included differential expression, WGCNA, and machine learning on gene expression data from the GEO database.

Use Cases

• Validate identified hub genes (PTPRC, CX3CR1, ITGB2) in independent osteoarthritis studies based on the described analysis pipeline.
• Investigate associations between gene expression and immune cell infiltration in osteoarthritis based on the study's immune microenvironment focus.
• Train machine learning models for biomarker discovery in degenerative joint diseases based on the use of XGBoost and Random Forest.
• Conduct pathway enrichment analysis on osteoarthritis-related gene modules based on the described GO and KEGG results.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting reuse and redistribution.
• Analysis is based on 1,171 upregulated differentially expressed genes identified from GEO data.
• Study employed multiple validation methods including Mendelian randomization and in vitro experiments.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect methodological bias inherent to the specific bioinformatics pipeline described.

Provenance

Source

Gene expression data sourced from the GEO database.

Collection Method

Analyzed using LIMMA, WGCNA, protein-protein interaction networks, XGBoost, and Random Forest.

Time Range

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Freshness

Last updated 2026-04-13 04:35:35; freshness should be verified.

Geography

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