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BACKGROUND: Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis, but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE: To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS: The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website. R language was used to batch correct the osteoarthritis dataset, extract osteoarthritis ferroptosis genes and perform differential analysis, analyze differentially expressed genes for GO function and KEGG signaling pathway. WGCNA analysis and machine learning (random forest, LASSO regression, and SVM-RFE analysis) were also used to screen osteoarthritis ferroptosis signature genes. The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups. The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION: (1) 12 548 osteoarthritis genes were obtained by batch correction and PCA analysis, while 484 ferroptosis genes were obtained, resulting in 24 differentially expressed genes of osteoarthritis ferroptosis. (2) GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus, cellular components such as apical and apical plasma membranes, and molecular functions such as heme binding and tetrapyrrole binding. (3) KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway. (4) After using WGCNA analysis and machine learning screening, we obtained the characteristic gene KLF2. After validation by gene microarray, we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus (P=0.000 14). (5) In vitro chondrocyte assay showed that type II collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes (P < 0.05), while in osteoarthritis ferroptosis, mast cells activated was closely correlated with dendritic cells (r=0.99); KLF2 was closely correlated with natural killer cells (r=-1, P=0.017) and T cells follicular helper (r=-1, P=0.017). (6) The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hypoxia is strongly associated with the development and progression of osteoarthritic chondrocyte injury, but the specific targets and regulatory mechanisms are unclear. OBJECTIVE: A machine learning approach was used to identify KDEL(Lys-Asp-Glu-Leu) receptor 3 (KDELR3) as a characteristic gene for osteoarthritis hypoxia and immune infiltration analysis, to provide new ideas and methods for the treatment of osteoarthritis. METHODS: The osteoarthritis-related datasets were downloaded from the GEO database and the GSEA website to obtain hypoxia-related genes. The osteoarthritis datasets were batch-corrected and immune infiltration analyzed using R language, and osteoarthritis hypoxia genes were extracted for differential analysis. Differentially expressed genes were analyzed for GO function and KEGG signaling pathway. Weighted correlation network analysis (WGCNA) and machine learning were also used to screen osteoarthritis hypoxia signature genes, and in vitro cellular experiments were performed to validate expression and correlate immune infiltration analysis using the datasets and qPCR. RESULTS AND CONCLUSION: (1) 8 492 osteoarthritis genes were obtained by batch correction and principal component analysis, mainly strongly associated with immune cells such as Macrophages M2 and Mast cells resting; 200 hypoxia genes were also obtained, resulting in 41 osteoarthritis hypoxia differentially expressed genes. (2) GO analysis involved mainly biological processes such as response to nutrient levels and glucocorticoids; cellular components such as lysosomal lumen and Golgi lumen; and molecular functions such as 14-3-3 protein binding and DNA-binding transcriptional activator activity. (3) KEGG analysis of osteoarthritis hypoxia differentially expressed genes was associated with signaling pathways such as PI3K-Akt, FoxO, and microRNAs in cancer. (4) The characteristic gene KDELR3 was obtained after using WGCNA analysis and machine learning screening. (5) The gene expression of KDELR3 was found to be higher in the test group than in the control group in the synovium (P=0.014) but lower in the meniscus (P=0.024) after validation by gene microarray. (6) In vitro chondrocyte assay showed that the expression of KDELR3 was higher in cartilage than in the control group (P=0.005), while KDELR3 was closely associated with Macrophages M0 (P=0.014) and T cells follicular helper (P=0.014). Using a machine learning approach, we confirmed that KDELR3 can be used as a hypoxic signature gene for osteoarthritis and may intervene in osteoarthritis pathogenesis by improving hypoxia, expecting to provide a new direction for better treatment of osteoarthritis. [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)