Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage

• Published in: Bone & joint research Vol. 13; no. 2; pp. 66 - 82
• Main Authors: Zhao, Di, Zeng, Ling-Feng, Liang, Gui-Hong, Luo, Ming-Hui, Pan, Jian-Ke, Dou, Yao-Xing, Lin, Fang-Zheng, Huang, He-Tao, Yang, Wei-Yi, Liu, Jun
• Format: Journal Article
• Language: English
• Published: England The British Editorial Society of Bone & Joint Surgery 05.02.2024
• Subjects: articular cartilage degradation; biomarkers; cartilage; cartilage tissue; dysregulated key genes; gene expression; gene expression profiles; inflammation; Knee; machine learning; Oral Tranexamic Acid; osteoarthritic cartilage; osteoarthritis; osteoarthritis (oa); pathogenesis; Randomized Controlled Trial; rnas; Total Blood Loss; Total Knee Arthroplasty; transcription
• DOI: 10.1302/2046-3758.132.BJR-2023-0074.R1
• ISSN: 2046-3758

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.