Evidence map›Paper›PMID 42292423›Full record

ArticleFrontiers in immunology2026

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.

Xinzhou Huang, Yongkun Wei, Yani Rao, Yue Wei, Hui Chen, Yunping Bao

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xinzhou HuangDepartment of Orthopedics, 3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, China.
Yongkun WeiDepartment of Orthopedics, 3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, China.
Yani RaoDepartment of Orthopedics, 3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, China.
Yue WeiThe First Clinical Medical College of Yangtze University, Jingzhou, China.
Hui ChenDepartment of Laboratory, The First People's Hospital of Jingzhou (First Affiliated Hospital of Yangtze University), Jingzhou, China.
Yunping BaoThe First Clinical Medical College of Yangtze University, Jingzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

CaprylatesFluorocarbonsMachine LearningOsteoarthritisAlgorithmsBiomarkersGene Expression ProfilingHumansMolecular Docking SimulationBiomarkersCaprylatesFluorocarbonsperfluorooctanoic acidbioinformatics analysisimmune infiltrationmachine learningmolecular dockingosteoarthritisperfluorooctanoic acidWGCNA

Identifiers

PMID42292423
PMCPMC13253507

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.