Department of Orthopaedics, Hangzhou Ninth People's Hospital, Hangzhou, Zhejiang, China.
Community Health Service Center, Hangzhou, Zhejiang, China.
Front Endocrinol (Lausanne). 2023 Mar 16;14:1144258. doi: 10.3389/fendo.2023.1144258. eCollection 2023.
Osteoarthritis (OA) is one of the most prevalent chronic diseases, leading to degeneration of joints, chronic pain, and disability in the elderly. Little is known about the role of immune-related genes (IRGs) and immune cells in OA.
Hub IRGs of OA were identified by differential expression analysis and filtered by three machine learning strategies, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). A diagnostic nomogram model was then constructed by using these hub IRGs, with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its performance and clinical impact. Hierarchical clustering analysis was then conducted by setting the hub IRGs as input information. Differences in immune cell infiltration and activities of immune pathways were revealed between different immune subtypes.
Five hub IRGs of OA were identified, including TNFSF11, SCD1, PGF, EDNRB, and IL1R1. Of them, TNFSF11 and SCD1 contributed the most to the diagnostic nomogram model with area under the curve (AUC) values of 0.904 and 0.864, respectively. Two immune subtypes were characterized. The immune over-activated subtype showed excessively activated cellular immunity with a higher proportion of activated B cells and activated CD8 T cells. The two phenotypes were also seen in two validation cohorts.
The present study comprehensively investigated the role of immune genes and immune cells in OA. Five hub IRGs and two immune subtypes were identified. These findings will provide novel insights into the diagnosis and treatment of OA.
骨关节炎（OA）是最常见的慢性疾病之一，导致老年人关节退化、慢性疼痛和残疾。目前对于免疫相关基因（IRGs）和免疫细胞在 OA 中的作用知之甚少。
通过差异表达分析鉴定 OA 的枢纽 IRGs，并通过三种机器学习策略（随机森林（RF）、最小绝对收缩和选择算子（LASSO）和支持向量机（SVM））进行过滤。然后使用这些枢纽 IRGs 构建诊断列线图模型，通过接收者操作特征（ROC）曲线、决策曲线分析（DCA）和临床影响曲线分析（CICA）评估其性能和临床影响。然后通过将枢纽 IRGs 作为输入信息进行层次聚类分析。揭示了不同免疫亚型之间免疫细胞浸润和免疫途径活性的差异。
确定了 OA 的 5 个枢纽 IRGs，包括 TNFSF11、SCD1、PGF、EDNRB 和 IL1R1。其中，TNFSF11 和 SCD1 对诊断列线图模型的贡献最大，曲线下面积（AUC）值分别为 0.904 和 0.864。两个免疫亚型特征明显。免疫过度激活亚型表现出过度激活的细胞免疫，具有更高比例的活化 B 细胞和活化 CD8 T 细胞。这两种表型也在两个验证队列中得到证实。
本研究全面研究了免疫基因和免疫细胞在 OA 中的作用。确定了 5 个枢纽 IRGs 和 2 个免疫亚型。这些发现将为 OA 的诊断和治疗提供新的见解。
