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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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1595676/full |
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| author | Xu Cui Houlin Ji Shengyang Guo Ju Liu Linyuan Zhang Yongwei Jia Yin Cui Xiaoxiao Zhou |
| author_facet | Xu Cui Houlin Ji Shengyang Guo Ju Liu Linyuan Zhang Yongwei Jia Yin Cui Xiaoxiao Zhou |
| author_sort | Xu Cui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a2f9c7570f344dc3a973fec9cc6baa93 |
| institution | Matheson Library |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-a2f9c7570f344dc3a973fec9cc6baa932025-08-01T04:10:22ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-08-011610.3389/fgene.2025.15956761595676Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithmsXu Cui0Houlin Ji1Shengyang Guo2Ju Liu3Linyuan Zhang4Yongwei Jia5Yin Cui6Xiaoxiao Zhou7Department of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaJinji Lake Community Health Service Center of Suzhou Industrial Park, Suzhou, ChinaDepartment of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaDepartment of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaDepartment of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaDepartment of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaDepartment of Orthopedics, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, ChinaDepartment of Orthopedics, Guangming Traditional Chinese Medicine Hospital, Shanghai, ChinaObjectiveTo 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.https://www.frontiersin.org/articles/10.3389/fgene.2025.1595676/fullosteoarthritismethylationmachine learningdiagnostic modelbiological functions |
| spellingShingle | Xu Cui Houlin Ji Shengyang Guo Ju Liu Linyuan Zhang Yongwei Jia Yin Cui Xiaoxiao Zhou Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms Frontiers in Genetics osteoarthritis methylation machine learning diagnostic model biological functions |
| title | Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms |
| title_full | Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms |
| title_fullStr | Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms |
| title_full_unstemmed | Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms |
| title_short | Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms |
| title_sort | research on the diagnosis model of osteoarthritis based on methylation related genes using machine learning algorithms |
| topic | osteoarthritis methylation machine learning diagnostic model biological functions |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1595676/full |
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