Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40636547", "kind": "science-record", "revision": 4, "content_hash": "sha256:4a2a9a07665c139fd16bb830965f58fdcad9183984aca5b0dbf892fe86d79762", "created_at": "2026-11-01T20:30:44Z", "updated_at": "2026-11-01T22:56:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms.", "doi": "10.2147/JPR.S517733", "pmid": "40636547", "authors": ["Yijun Sun", "Guoqi Dong", "Hui Gao", "Yong Yao", "Huayuan Yang"], "journal": "Journal of pain research", "year": 2025, "abstract": "Purpose: Rheumatoid arthritis (RA) is a persistent inflammatory condition, and electroacupuncture (EA) has been demonstrated to effectively reduce the symptoms associated with RA. However, the molecular mechanisms underlying the effects of EA in RA remained poorly understood. This study aimed to identify potential diagnostic biomarkers for RA and elucidated the molecular targets of EA by using bioinformatics analysis and machine learning algorithms in peripheral blood samples.\nMethods: We obtained datasets from the Gene Expression Omnibus(GEO) database containing samples from RA patients (GSE15573) and from RA patients after EA treatment (GSE59526) for bioinformatics analysis. Diagnostic biomarkers were identified using three distinct machine learning algorithms (LASSO, Random Forest and SVM-REF). A rat model of RA was established using Complete Freund's Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential expression of identified diagnostic biomarkers and assess the modulatory impact of EA on these genes.\nResults: Twenty-six genes were identified as differentially expressed following EA treatment. Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272). Besides, EA treatment significantly increased the paw withdrawal threshold (PWT) and the peripheral blood expression of both ARHGAP17 and VEGFB in CFA rats.\nConclusion: This study employed three machine learning algorithms to identify potential diagnostic biomarkers for the alleviation of RA by EA. The biomarkers demonstrated robust diagnostic performance across multiple validation datasets. Furthermore, animal experiments confirmed that EA exerted a favorable regulatory effect on these diagnostic biomarkers. The findings of this study provided novel therapeutic targets for the EA treatment of RA.", "claims": [{"id": "claim-method", "statement": "Diagnostic biomarkers were identified using three distinct machine learning algorithms (LASSO, Random Forest and SVM-REF).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40636547", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Diagnostic biomarkers were identified using three distinct machine learning algorithms (LASSO, Random Forest and SVM-REF)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE15573, GSE59526, GSE17755, GSE205962, and GSE93272).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40636547", "locator": "sections[22] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets analyzed for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE15573, GSE59526, GSE17755, GSE205962, and GSE93272)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40636547", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272).", "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:40636547"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15573"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15573"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40636547", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JPR.S517733", "sha256": 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A rat model of RA was established using Complete F", "expression of both ARHGAP17 and VEGFB in CFA rats. Conclusion: This study employed three machi", "versity of Traditional Chinese Medicine. The rats were provided with free access to food and water", "pathogen free (SPF) male Sprague-Dawley (SD) rats aged 8 weeks weighing 160–200 g were procure"], "order_basis": "paper_text_order:919", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["te Freund's Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential ex", "wed by Bonferroni post hoc tests applied for qPCR analysis, while behavioral analysis was cond"], "order_basis": "paper_text_order:1008", "id": "assay:2"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Diagnostic biomarkers were identified using three distinct machine learning algorithms (LASSO, Random Forest and SVM-REF)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}