Department of Hematology, The First People's Hospital of Changzhou, Third Affiliated Hospital of Soochow University, Changzhou, China.
Department of Traditional Chinese Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Immunol. 2023 Jul 21;14:1149686. doi: 10.3389/fimmu.2023.1149686. eCollection 2023.
Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis.
A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx.
A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results.
In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.
骨关节炎（OA）是一种高发病率和高致残率的慢性疾病，其发病机制尚不清楚。本研究通过生物信息学分析，寻找与滑膜炎和软骨细胞凋亡相关的 OA 标志物。
从基因表达综合数据库中选择了 5 个基因表达谱。我们结合基因表达数据库和基因卡片数据库进行基因本体论和京都基因与基因组百科全书分析；然后，使用最小绝对收缩和选择算子（LASSO）算法识别特征基因，并建立预测风险评分。我们使用统一流形逼近和投影（UMAP）方法来识别 OA 患者的亚型，同时使用 CytoHubba 算法和 GOSemSim R 包筛选出枢纽基因。接下来，使用单样本基因集富集分析和 CIBERSORTx 进行免疫评估。
共筛选出 56 个 OA 相关差异基因，通过 LASSO 算法识别出 10 个特征基因。通过 UMAP 将 OA 样本分为 cluster1 和 cluster2 亚型，聚类结果表明两组间特征基因差异显著。在 cluster2 中，MYOC、CYP4B1、P2RY14、ADIPOQ、PLIN1、MFAP5 和 LYVE1 高表达，在 cluster1 中，ANKHLRC15、CEMIP、GPR88、CSN1S1、TAC1 和 SPP1 高表达。蛋白质-蛋白质相互作用网络分析显示，MMP9、COL1A 和 IGF1 为高节点，差异基因影响白细胞介素-17 途径和肿瘤坏死因子途径。GOSemSim R 包显示 ADIPOQ、COL1A 和 SPP1 与 31 个枢纽基因的功能密切相关。此外，确定 mmp9 和 Fos 与多个转录因子相互作用，ssGSEA 和 CIBERSORTx 算法显示两种 OA 亚型间免疫浸润存在显著差异。最后，进行 qPCR 实验以探索大鼠软骨和滑膜组织中的重要基因；qPCR 结果表明，COL1A 和 IL-17A 在 OA 大鼠的滑膜炎组织和软骨组织中均高表达，与预测结果一致。
未来，可能会找到同时缓解 OA 两种表型的共同治疗靶点。
