Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36685864", "kind": "science-record", "revision": 4, "content_hash": "sha256:258f20ab0d4dd90b27941f134fd28e2b79a17391997f80cd249397aa3b56d60a", "created_at": "2026-11-01T19:01:14Z", "updated_at": "2026-11-01T22:51:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis.", "doi": "10.3389/fgene.2022.1095058", "pmid": "36685864", "authors": ["Hongquan Heng", "Dazhuang Li", "Wenxing Su", "Xinyue Liu", "Daojiang Yu", "Zhengjun Bian", "Jian Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS. Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells. Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells. Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases.", "claims": [{"id": "claim-method", "statement": "The 36 common genes were employed in the LASSO and RF analyses to screen hub genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685864", "locator": "sections[16] \"Identification of hub genes based on machine learning algorithms\" paragraph 2", "quote": "The 36 common genes were employed in the LASSO and RF analyses to screen hub genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36685864", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO logistic regression and random forest analyses identified PLIN, PPAP2A, and TYROBP as shared hub genes between RA and PVNS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36685864", "locator": "sections[16] \"Identification of hub genes based on machine learning algorithms\" paragraph 2", "quote": "By overlapping the genes chosen by LASSO and RF, PLIN, PPAP2A, and TYROBP were identified as hub genes in PVNS and RA (Figure 5F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO logistic regression and random forest analyses identified PLIN, PPAP2A, and TYROBP as shared hub genes between RA and PVNS.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36685864"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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