ORIGINAL RESEARCH article

Front. Genet.

Sec. Computational Genomics

Volume 16 - 2025 | doi: 10.3389/fgene.2025.1595676

This article is part of the Research TopicGenetic Horizons: Exploring Genetic Biomarkers in Therapy and Evolution with the Aid of Artificial IntelligenceView all 4 articles

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

Provisionally accepted
Cui  XuCui Xu1Houlin  JIHoulin JI2Shengyang  GuoShengyang Guo1Ju  LIUJu LIU1Linyuan  ZHANGLinyuan ZHANG1Yongwei  JIAYongwei JIA1Ying  CUIYing CUI1Xiaoxiao  ZHOUXiaoxiao ZHOU3*
  • 1Department of Orthopedics, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
  • 2Jinji Lake Community Health Service Center of Suzhou Industrial Park, Jiangsu, China, Jiang su, China
  • 3Department of Orthopedics, Jiangwan Hospital of Shanghai Hongkou District, Hongkou District, Shanghai, China

The final, formatted version of the article will be published soon.

Objective: To construct a diagnostic model of osteoarthritis related to methylation genes using machine learning algorithms, and analyze its prognostic value and biological functions. Methods: 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 methylation differentially expressed genes (DEGs). Genes were selected through SVM-RFE analysis and LASSO regression model, and a diagnostic model for osteoarthritis 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. Results: An overall11 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 and could effectively diagnose osteoarthritis. The area under the curve (AUC) in the datasets GSE 63695 and GSE162484 was 0. 96 and 0. 93, respectively.The diagnostic model of methylation-related genes constructed based on machine learning algorithms can effectively identify osteoarthritis.

Keywords: Osteoarthritis, Methylation, machine learning, Diagnostic model, biological functions

Received: 19 Mar 2025; Accepted: 03 Jul 2025.

Copyright: © 2025 Xu, JI, Guo, LIU, ZHANG, JIA, CUI and ZHOU. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiaoxiao ZHOU, Department of Orthopedics, Jiangwan Hospital of Shanghai Hongkou District, Hongkou District, Shanghai, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.