Teaching Department, First Affiliated Hospital of the Guangxi University of Chinese Medicine, Nanning, China.
Postgraduate Schools, Guangxi University of Chinese Medicine, Nanning, China.
Front Immunol. 2023 Sep 21;14:1178794. doi: 10.3389/fimmu.2023.1178794. eCollection 2023.
Osteoarthritis (OA) is a prevalent chronic joint disease with an obscure underlying molecular signature. Cuproptosis plays a crucial role in various biological processes. However, the association between cuproptosis-mediated immune infifiltration and OA progression remains unexplored. Therefore, this study elucidates the pathological process and potential mechanisms underlying cuproptosis in OA by constructing a columnar line graph model and performing consensus clustering analysis.
Gene expression profifile datasets GSE12021, GSE32317, GSE55235, and GSE55457 of OA were obtained from the comprehensive gene expression database. Cuproptosis signature genes were screened by random forest (RF) and support vector machine (SVM). A nomogram was developed based on cuproptosis signature genes. A consensus clustering was used to distinguish OA patients into different cuproptosis patterns. To quantify the cuproptosis pattern, a principal component analysis was developed to generate the cuproptosis score for each sample. Single-sample gene set enrichment analysis (ssGSEA) was used to provide the abundance of immune cells in each sample and the relationship between these significant cuproptosis signature genes and immune cells.To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Cuproptosis-related genes were extracted and subjected to differential expression analysis to construct a disease prediction model and confifirmed by RT-qPCR.
Seven cuproptosis signature genes were screened (DBT, LIPT1, GLS, PDHB, FDX1, DLAT, and PDHA1) to predict the risk of OA disease. A column line graph model was developed based on these seven cuproptosis signature genes, which may assist patients based on decision curve analysis. A consensus clustering method was used to distinguish patients with disorder into two cuproptosis patterns (clusters A and B). To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Furthermore, the OA characteristics of patients in cluster A were associated with the inflflammatory factors IL-1b, IL-17, IL-21, and IL-22, suggesting that the cuproptosis signature genes play a vital role in the development of OA.
In this study, a risk prediction model based on cuproptosis signature genes was established for the fifirst time, and accurately predicted OA risk. In addition, patients with OA were classifified into two cuproptosis molecule subtypes (clusters A and B); cluster A was highly associated with Th17 immune responses, with higher IL-1b, IL-17, and IL-21 IL-22 expression levels, while cluster B had a higher correlation with cuproptosis. Our analysis will help facilitate future research related cuproptosis-associated OA immunotherapy. However, the specifific mechanisms remain to be elucidated.
骨关节炎（OA）是一种普遍存在的慢性关节疾病，其潜在的分子特征尚不明确。铜死亡在各种生物学过程中起着关键作用。然而，铜死亡介导的免疫浸润与 OA 进展之间的关联仍未得到探索。因此，本研究通过构建柱状线图模型和进行共识聚类分析，阐明 OA 中铜死亡的病理过程和潜在机制。
从综合基因表达数据库中获取 OA 的基因表达谱数据集 GSE12021、GSE32317、GSE55235 和 GSE55457。通过随机森林（RF）和支持向量机（SVM）筛选铜死亡特征基因。基于铜死亡特征基因构建列线图。使用共识聚类将 OA 患者分为不同的铜死亡模式。为了量化铜死亡模式，开发了主成分分析来为每个样本生成铜死亡评分。单样本基因集富集分析（ssGSEA）用于提供每个样本中免疫细胞的丰度，并分析这些显著的铜死亡特征基因与免疫细胞之间的关系。为了量化铜死亡模式，开发了主成分分析技术来为每个样本生成铜死亡评分。提取铜死亡相关基因并进行差异表达分析，构建疾病预测模型，并通过 RT-qPCR 进行验证。
筛选出 7 个铜死亡特征基因（DBT、LIPT1、GLS、PDHB、FDX1、DLAT 和 PDHA1）来预测 OA 疾病的风险。基于这 7 个铜死亡特征基因构建了柱状线图模型，该模型可能有助于基于决策曲线分析的患者。使用共识聚类方法将患者分为两种铜死亡模式（集群 A 和 B）。为了量化铜死亡模式，开发了主成分分析技术来为每个样本生成铜死亡评分。此外，集群 A 中患者的 OA 特征与促炎因子 IL-1b、IL-17、IL-21 和 IL-22 相关，表明铜死亡特征基因在 OA 的发展中起着重要作用。
本研究首次建立了基于铜死亡特征基因的风险预测模型，准确预测了 OA 风险。此外，OA 患者被分为两种铜死亡分子亚型（集群 A 和 B）；集群 A 与 Th17 免疫反应高度相关，IL-1b、IL-17 和 IL-21、IL-22 表达水平较高，而集群 B 与铜死亡相关性更高。我们的分析将有助于促进未来与铜死亡相关的 OA 免疫治疗相关的研究。然而，具体机制仍有待阐明。
