What does this research mean for the field?

Perfluorooctanoic acid (PFOA) exposure promotes osteoarthritis pathogenesis by dysregulating IGFBP1, MKI67, and C9, which alter the immune microenvironment and serve as highly accurate diagnostic biomarkers. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to elucidate the mechanisms by which perfluorooctanoic acid contributes to osteoarthritis and identify potential biomarkers.

June 1, 2026Frontiers in Immunology0 citationsOpen Access

From algorithm to verification: based on network toxicology and machine learning, the immunomodulatory role of IGFBP1/MKI67/C9 in perfluorooctanoic acid-induced osteoarthritis was discovered, and a diagnostic model was constructed

XHXinzhou Huang YWYongkun Wei YRYani Rao

Key Points

This study aims to elucidate the mechanisms by which perfluorooctanoic acid contributes to osteoarthritis and identify potential biomarkers.
Integrated gene expression data from public databases GSE114007 and GSE89408 for analysis.
Utilized six machine learning algorithms to identify key genes, followed by qRT-PCR validation of gene expression in synovial tissue.
Constructed and evaluated a diagnostic nomogram based on identified biomarkers.
Fifteen differentially expressed genes associated with PFOA and osteoarthritis were identified, with IGFBP1, MKI67, and C9 being significantly upregulated.
The diagnostic nomogram achieved an AUC of 0.903 in the training set and 0.939 in the validation set (GSE51588).
Molecular docking revealed strong binding of PFOA to the core proteins IGFBP1, MKI67, and C9, with binding energies of -6.0, -8.5, and -7.5 kcal/mol.

Abstract

Background Perfluorooctanoic acid (PFOA), a widespread persistent environmental contaminant, has been associated with osteoarthritis (OA) onset and progression, though mechanisms remain unclear. This study elucidates PFOA’s influence on OA pathogenesis, evaluates its effects on disease progression, and identifies diagnostic biomarkers. Method Obtain PFOA and OA-related gene expression data from public databases, integrate GSE114007 and GSE89408, and perform batch correction. Differentially expressed genes were identified via limma for GO and KEGG enrichment. Six machine learning algorithms (Lasso, SVM, Boruta, XGBoost, LightGBM, AdaBoost) and WGCNA screened key genes. Expression of candidate genes in OA synovial tissue was verified by qRT-PCR, and a diagnostic nomogram was constructed and evaluated. Immune cell infiltration was analyzed by ssGSEA, and molecular docking studied PFOA binding to target proteins. Result 15 PFOA-related OA differentially expressed genes were identified. Machine learning and WGCNA determined IGFBP1, MKI67 and C9 as core genes; qRT-PCR verified they were significantly upregulated in OA patients. Enrichment analysis revealed involvement in inflammatory, immune and metabolic processes. Immune infiltration analysis indicated multiple immune cells significantly increased in OA samples; core genes helped inhibit excessive Th17 and B cell responses while enhancing Treg and NKT regulatory activity. Molecular docking showed strong binding of PFOA to the three core proteins (binding energies: -6.0, -8.5, -7.5 kcal/mol). The nomogram achieved AUC of 0.903 in training set and 0.939 in external validation set (GSE51588). Conclusion PFOA exposure may be associated with OA immune microenvironment alterations, potentially involving dysregulation of IGFBP1, MKI67 and C9, contributing to inflammation and cartilage degradation. These three genes are promising diagnostic biomarkers for OA, providing new insights into environmental pollutant involvement in OA pathogenesis.

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