He Qiwang, Liu Lingling, Hu Xinyu, Lin Lixia, Song Zhenyu, Xia Yuyang, Lin Qianming, Wei Jihua, Li Shanlang
Hubei University of Chinese Medicine, Hubei Shizhen Laboratory, Wuhan, 430061, People's Republic of China.
Women and Children's Hospital, Qingdao University, Qingdao, 266034, People's Republic of China.
J Multidiscip Healthc. 2025 Aug 1;18:4589-4612. doi: 10.2147/JMDH.S537507. eCollection 2025.
Osteoarthritis (OA) is the most common type of arthritis and early detection is crucial to improving prognosis. In this study, we identified crucial genes associated with macrophage polarization in OA and constructed a diagnostic model to provide novel insights for diagnostic and therapeutic strategies.
The GSE55235 and GSE55457 datasets were merged through the GEO database to identify genes related to macrophage polarization by conducting weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. Additionally, various analyses, including immune infiltration, gene set enrichment analysis, competing endogenous RNA (ceRNA) construction, and drug prediction, were conducted. Finally, clinical samples were clinically validated through RT-qPCR (OA: Control = 10: 5) and IHC (6: 5) experiments.
Three hub genes (MYC, SIK1, and NFIL3) were identified, and the diagnostic model constructed using them demonstrated good diagnostic efficacy in both internal and external datasets (internal AUC = 0.965, external AUC = 0.847). In vitro experiments revealed that the hub genes in the synovial tissue of OA patients were significantly down-regulated ( < 0.01), confirming their potential as diagnostic biomarkers.
We constructed an OA diagnostic model related to macrophage polarization through comprehensive bioinformatics analysis, and the results indicated that these genes have high diagnostic value. However, further clinical studies and experimental assessments are needed to validate these findings.
骨关节炎（OA）是最常见的关节炎类型，早期检测对于改善预后至关重要。在本研究中，我们鉴定了与OA中巨噬细胞极化相关的关键基因，并构建了一个诊断模型，为诊断和治疗策略提供新的见解。
通过GEO数据库合并GSE55235和GSE55457数据集，通过加权基因共表达网络分析（WGCNA）和差异表达分析来鉴定与巨噬细胞极化相关的基因。使用最小绝对收缩和选择算子（LASSO）、随机森林（RF）和支持向量机递归特征消除（SVM-RFE）算法来鉴定枢纽基因，并构建一个通过内部数据集以及多个外部批量RNA测序和单细胞RNA测序数据验证的诊断模型。此外，还进行了各种分析，包括免疫浸润、基因集富集分析、竞争性内源性RNA（ceRNA）构建和药物预测。最后，通过RT-qPCR（OA：对照 = 10：5）和免疫组化（IHC）（6：5）实验对临床样本进行临床验证。
鉴定出三个枢纽基因（MYC、SIK1和NFIL3），使用它们构建的诊断模型在内部和外部数据集中均显示出良好的诊断效能（内部AUC = 0.965，外部AUC = 0.847）。体外实验表明，OA患者滑膜组织中的枢纽基因显著下调（<0.01），证实了它们作为诊断生物标志物的潜力。
我们通过综合生物信息学分析构建了一个与巨噬细胞极化相关的OA诊断模型，结果表明这些基因具有较高的诊断价值。然而，需要进一步的临床研究和实验评估来验证这些发现。
