Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment. Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein–protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations. A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations. Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

中文翻译：

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利用生物信息学分析和实验验证鉴定和验证骨关节炎中铜死亡相关基因与铁死亡相关基因相结合的新特征

探讨骨关节炎（OA）的发病机制对于其预防、诊断和治疗具有重要意义。因此，我们的目的是构建结合铜死亡相关基因（CRG）和铁死亡相关基因（FRG）的新特征基因（c-FRG），以探索OA的发病机制并帮助其治疗。使用R软件获得差异表达的c-FRG（c-FDEG）。基于这些 c-FDEG 进行了富集分析并构建了蛋白质-蛋白质相互作用 (PPI) 网络。然后，筛选了七个枢纽基因。采用三种机器学习方法和验证实验从c-FDEG中鉴定出四种特征生物标志物，然后进行基因集富集分析、基因集变异分析、单样本基因集富集分析、免疫功能分析、药物预测和ceRNA网络分析是根据这些特征生物标志物进行的。随后，使用这些生物标志物构建了 OA 疾病模型，并在 GSE82107 数据集上进行了验证。最后，我们分析了这些 c-FDEG 在不同细胞群中的表达分布。总共发现 63 个 FRG 与 11 个 CRG 密切相关，并鉴定出 40 个 c-FDEG。生物富集分析表明它们主要与炎症、外部细胞刺激和自噬有关。 CDKN1A、FZD7、GABARAPL2 和 SLC39A14 被确定为 OA 特征生物标志物，并预测了它们相应的 miRNA 和 lncRNA。最后，scRNA-seq数据分析表明差异表达的c-FRG在细胞群中具有显着不同的表达分布。 CDKN1A、FZD7、GABARAPL2 和 SLC39A14 四种基因是 OA 的优良生物标志物和前瞻性治疗靶点。