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

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

ObjectiveTo construct a diagnostic model of osteoarthritis related to methylation genes using machine learning algorithms, and analyze its prognostic value and biological functions.MethodsThe GSE 63695 and GSE162484 datasets including human osteoarthritis (OA) and normal samples were downloaded from...

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Bibliographic Details
Main Authors: Xu Cui, Houlin Ji, Shengyang Guo, Ju Liu, Linyuan Zhang, Yongwei Jia, Yin Cui, Xiaoxiao Zhou
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Genetics
Subjects:
osteoarthritis
methylation
machine learning
diagnostic model
biological functions
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2025.1595676/full
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Summary:ObjectiveTo construct a diagnostic model of osteoarthritis related to methylation genes using machine learning algorithms, and analyze its prognostic value and biological functions.MethodsThe 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.ResultsAn 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.ConclusionThe diagnostic model of methylation-related genes constructed based on machine learning algorithms can effectively identify OA.
ISSN:1664-8021

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