Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40822282", "kind": "science-record", "revision": 4, "content_hash": "sha256:4c59b09b887fda6f03d5356ec0a7953bb88e1a4810a067c04ced6d9f35eeb594", "created_at": "2026-11-01T20:30:55Z", "updated_at": "2026-11-01T22:57:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms.", "doi": "10.3389/fgene.2025.1595676", "pmid": "40822282", "authors": ["Xu Cui", "Houlin Ji", "Shengyang Guo", "Ju Liu", "Linyuan Zhang", "Yongwei Jia", "Yin Cui", "Xiaoxiao Zhou"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Objective: To construct a diagnostic model of osteoarthritis related to methylation genes using machine learning algorithms, and analyze its prognostic value and biological functions.\nMethods: The GSE 63695 and GSE162484 datasets including human osteoarthritis (OA) and normal samples were downloaded from the GEO datasets. The microarray chip data of chondrocytes were analyzed using R software to obtain differentially methylated genes. Genes were selected through SVM-RFE analysis and LASSO regression model, and a diagnostic model for OA was established. The performance of the model was assessed by the receiver operating characteristic (ROC) curve. The gene set enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the genes incorporated within the model.\nResults: An overall 11 DEGs were identified:7 genes were remarkably upregulated and 4 genes were distinctly downregulated. By means of machine learning algorithms, ARHGEF10, ATP11A, NOTCH1, THSD4, NIPA1, SIM2, MAN1C1, ENDOG, CCNC, TAF5, and VPS52 were ultimately incorporated into the model, which could effectively diagnose OA. The area under the curve (AUC) in the datasets GSE 63695 and GSE162484 was 0.96 and 0.93 respectively.\nConclusion: The diagnostic model of methylation-related genes constructed based on machine learning algorithms can effectively identify OA.", "claims": [{"id": "claim-method", "statement": "Genes were selected through SVM-RFE analysis and LASSO regression model, and a diagnostic model for OA was established.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40822282", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Genes were selected through SVM-RFE analysis and LASSO regression model, and a diagnostic model for OA was established."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40822282", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A machine-learning diagnostic model based on 11 methylation-related genes achieved AUCs of 0.96 and 0.93 in GSE 63695 and GSE162484, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40822282", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The area under the curve (AUC) in the datasets GSE 63695 and GSE162484 was 0.96 and 0.93 respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A machine-learning diagnostic model based on 11 methylation-related genes achieved AUCs of 0.96 and 0.93 in GSE 63695 and GSE162484, respectively.", "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:40822282"], "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:GSE162484"], "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:GSE162484"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40822282", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1595676", "sha256": "sha256:bddc0e48cefc99b426df6247e694b7732236256303537159fcf39aa0cdeaa565", "bytes": 24094, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1595676", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12353723", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE162484", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162484", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE162484", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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