METHODS: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed.
RESULTS: A total of 88 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were significantly enriched in leucocyte migration and interleukin (IL)-17 signalling pathways. Disease ontology (DO) indicated that DEGs were mostly enriched in rheumatoid arthritis. Six hub genes including FosB proto-oncogene, AP-1 transcription factor subunit (FOSB); C-X-C motif chemokine ligand 2 (CXCL2); CXCL8; IL-6; Jun proto-oncogene, AP-1 transcription factor subunit (JUN); and Activating transcription factor 3 (ATF3) were identified and verified by GEO datasets. ATF3 (area under the curve = 0.975) turned out to be a potential biomarker for the diagnosis of early OA. Several infiltrating immune cells varied significantly between early-stage OA and end-stage OA, such as resting NK cells (p = 0.016), resting dendritic cells (p = 0.043), and plasma cells (p = 0.043). Additionally, ATF3 was significantly correlated with resting NK cells (p = 0.034), resting dendritic cells (p = 0.026), and regulatory T cells (Tregs, p = 0.018).
CONCLUSIONS: ATF3 may be a potential diagnostic marker for early diagnosis and treatment of OA, and immune cell infiltration provides new perspectives for understanding the mechanism during OA progression.Cite this article: Bone Joint Res 2022;11(9):679-689.
方法:从基因表达综合(GEO)数据库下载基因表达谱。通过R软件鉴定差异表达基因(DEGs)。进行了功能富集分析,并构建了蛋白质-蛋白质相互作用网络(PPI)。然后筛选hub基因。通过GEO数据集验证了对早期骨关节炎(OA)诊断具有较高价值的生物标志物。最后,CIBERSORT算法用于评估早期OA和终末期OA之间的免疫浸润,并分析诊断标记物与浸润免疫细胞的相关性。
结果:共确认了88个DEG。基因本体论(GO)和京都基因和基因组百科全书(KEGG)富集分析表明,DEGs在白细胞迁移和白介素(IL)-17信号通路中显着富集。疾病本体论(DO)表明DEGs主要富集在类风湿性关节炎中。六个hub基因,包括FosB原癌基因,AP-1转录因子亚基(FOSB);C-X-C基序趋化因子配体2(CXCL2);CXCL8;IL-6;Jun原癌基因,AP-1转录因子亚基(JUN);和激活转录因子3(ATF3)通过GEO数据集进行鉴定和验证。ATF3(曲线下面积=0.975)被证明是诊断早期OA的潜在生物标志物。几种浸润免疫细胞在早期OA和终末期OA之间差异显著,例如静息NK细胞(p=0.016),静息树突状细胞(p=0.043),和浆细胞(p=0.043)。此外,ATF3与静息NK细胞显著相关(p=0.034),静息树突状细胞(p=0.026),和调节性T细胞(Tregs,p=0.018)。
结论:ATF3可能是OA早期诊断和治疗的潜在诊断标志物。免疫细胞浸润为理解OA进展过程中的机制提供了新的视角。引用这篇文章:骨关节Res2022;11(9):679-689。