Mao Wangnan, Bao Zhengsheng, Zhang Bingbing, Wu Lianguo
The Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Orthopedic Surgery, The Second Affiliated Hospital of Zhejiang, Chinese Medical University, Hangzhou, China.
J Orthop Surg Res. 2025 Jul 11;20(1):639. doi: 10.1186/s13018-025-06072-9.
Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly contributes to global disability. Glycolysis, a fundamental process in cellular energy metabolism, is particularly vital for chondrocytes in OA. This study aims to explore the intrinsic relationship between glycolysis-related genes (GRGs) and OA.
We incorporated three publicly available datasets from the Gene Expression Omnibus (GEO) database, which included 64 OA samples and 34 normal controls. By utilizing differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning methods, we identified three diagnostic biomarkers of OA patients. The expression levels of these biomarkers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC). Additionally, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory interactions.
Through bioinformatics and machine learning approaches, three glycolysis-related biomarkers-HMGB2, SLC7A5, and ADM-were identified. The diagnostic model based on these GRGs demonstrated high predictive accuracy, with an AUC of 0.92 in the training set and 0.85 in the validation set. Subsequently, qRT-PCR and IHC confirmed the differential expression of hub genes in human cartilage samples. Furthermore, immunocyte infiltration analysis revealed distinct immune cell infiltration profiles between OA and HC groups. Notably, lncRNA XIST was found to regulate all three biomarkers, indicating its potential as a therapeutic target for OA.
This study provides novel insights into the role of glycolysis in OA pathogenesis and highlights its potential as a target for diagnosis, prevention, and treatment strategies.
骨关节炎（OA）是一种常见的退行性关节疾病，对全球残疾问题有重大影响。糖酵解是细胞能量代谢的基本过程，对OA中的软骨细胞尤为重要。本研究旨在探讨糖酵解相关基因（GRGs）与OA之间的内在关系。
我们纳入了来自基因表达综合数据库（GEO）的三个公开可用数据集，其中包括64个OA样本和34个正常对照。通过利用差异表达分析、加权基因共表达网络分析、蛋白质-蛋白质相互作用网络和机器学习方法，我们确定了OA患者的三个诊断生物标志物。这些生物标志物的表达水平通过定量逆转录聚合酶链反应（qRT-PCR）和免疫组织化学（IHC）进行了验证。此外，构建了一个竞争性内源性RNA（ceRNA）网络以探索潜在的调控相互作用。
通过生物信息学和机器学习方法，确定了三个糖酵解相关生物标志物——高迁移率族蛋白B2（HMGB2）、溶质载体家族7成员5（SLC7A5）和肾上腺髓质素（ADM）。基于这些GRGs的诊断模型显示出较高的预测准确性，训练集的曲线下面积（AUC）为0.92，验证集的AUC为0.85。随后，qRT-PCR和IHC证实了人类软骨样本中核心基因的差异表达。此外，免疫细胞浸润分析揭示了OA组和健康对照组之间不同的免疫细胞浸润特征。值得注意的是，长链非编码RNA XIST被发现可调控所有三个生物标志物，表明其作为OA治疗靶点的潜力。
本研究为糖酵解在OA发病机制中的作用提供了新的见解，并突出了其作为诊断、预防和治疗策略靶点的潜力。
