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

Cui Xu¹Houlin JI²Shengyang Guo¹Ju LIU¹Linyuan ZHANG¹Yongwei JIA¹Ying CUI¹Xiaoxiao ZHOU^3*

• 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

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.