Zhu Yifang, Deng Lin, Xia Junxiang, Yang Jing, Zhao Dan, Li Min
Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People's Republic of China.
Department of Rheumatoid Osteoarthropathy, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People's Republic of China.
Open Access Rheumatol. 2025 Aug 16;17:157-171. doi: 10.2147/OARRR.S541568. eCollection 2025.
Osteoarthritis (OA) is a degenerative disorder associated with glycolysis. However, the precise mechanisms remain unclear. This study aimed to identify glycolysis-associated biomarkers and elucidate how glycolysis-related genes interact with the synovial immune microenvironment in OA progression.
Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma package. Gene Ontology (GO) and KEGG enrichment analyses were conducted to explore biological functions. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify OA-associated genes, which were intersected with glycolysis genes from The Molecular Signatures Database (MSigDB) and DEGs to obtain key genes. Lasso regression and random forest models were employed to establish a risk model, and its predictive performance was evaluated using nomogram, Receiver Operating Characteristic (ROC) analysis, and Decision Curve Analysis (DCA). Gene Set Enrichment Analysis (GSEA) and Cibersort analysis were conducted to explore pathways and immune infiltration correlations.
A total of 239 OA-associated genes were identified through WGCNA. Six hub genes were obtained by intersecting with glycolysis genes and DEGs. Four key glycolytic genes were selected by Lasso regression and random forest models. The nomogram showed that three genes (DDIT4, SLC16A7, SLC2A3) could predict OA risk accurately. The ROC analysis demonstrated an area under the curve (AUC) of 0.85, indicating good predictive performance. Distinct immune cell distribution patterns were observed in OA groups. Interaction networks were constructed for the key genes with related miRNAs, transcription factors (TFs), and small molecule drugs.
This study identified three key glycolysis-related genes (DDIT4, SLC16A7, SLC2A3) in OA, revealing their potential roles in disease progression and immune infiltration. These findings may provide new insights into the pathogenesis and therapeutic targets for OA, based on the identified genes and their interactions with the immune microenvironment.
骨关节炎（OA）是一种与糖酵解相关的退行性疾病。然而，其确切机制仍不清楚。本研究旨在鉴定与糖酵解相关的生物标志物，并阐明糖酵解相关基因在OA进展过程中如何与滑膜免疫微环境相互作用。
从基因表达综合数据库（GEO）获取正常和OA滑膜基因表达谱微阵列。使用limma软件包鉴定差异表达基因（DEG）。进行基因本体（GO）和KEGG富集分析以探索生物学功能。采用加权基因共表达网络分析（WGCNA）鉴定与OA相关的基因，将其与来自分子特征数据库（MSigDB）的糖酵解基因和DEG进行交集分析以获得关键基因。采用套索回归和随机森林模型建立风险模型，并使用列线图、受试者工作特征（ROC）分析和决策曲线分析（DCA）评估其预测性能。进行基因集富集分析（GSEA）和Cibersort分析以探索通路和免疫浸润相关性。
通过WGCNA共鉴定出239个与OA相关的基因。通过与糖酵解基因和DEG进行交集分析获得了6个枢纽基因。通过套索回归和随机森林模型选择了4个关键糖酵解基因。列线图显示3个基因（DDIT4、SLC16A7、SLC2A3）可准确预测OA风险。ROC分析显示曲线下面积（AUC）为0.85，表明预测性能良好。在OA组中观察到不同的免疫细胞分布模式。构建了关键基因与相关miRNA、转录因子（TF）和小分子药物的相互作用网络。
本研究在OA中鉴定出3个关键的糖酵解相关基因（DDIT4、SLC16A7、SLC2A3），揭示了它们在疾病进展和免疫浸润中的潜在作用。基于所鉴定的基因及其与免疫微环境的相互作用，这些发现可能为OA的发病机制和治疗靶点提供新的见解。
