Li Zhen, Chen Zhenyue, Wang Xiaotan, Li Zehui, Sun He, Wei Jinqiang, Zeng Xianzhong, Cao Xuewei, Wan Chao
The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Genet. 2022 Jun 17;13:814645. doi: 10.3389/fgene.2022.814645. eCollection 2022.
Currently, the early diagnosis and treatment of osteoarthritis (OA) remain a challenge. In the present study, we attempted to explore potential biomarkers for the diagnosis and treatment of OA. The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for evaluating gene function related categories. Then, miRNA sequencing was performed for differentially expressed miRNAs' identification. Finally, weighted gene co-expression network analysis (WGCNA) was performed for genes detected by the three mRNA datasets and a competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) was constructed for central genes identification. In addition, the relationship between central gene expression and immune infiltration was analyzed, and the candidate agents for OA were predicted based on the Connectivity Map database. Quantitative RT-PCR (qRT-PCR), Western blotting analysis, and immunofluorescent staining were performed to validate the expression levels of differentially expressed miRNAs and differentially expressed target genes in normal and OA tissues and chondrocytes. MiRNA-mRNA network was also validated in chondrocytes . A total of 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA-mRNA interactions were predicted. The brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping the brown module genes with miRNA-mRNA pairs, 27 miRNA-mRNA pairs were obtained. A ceRNA network was constructed with 5505 lncRNA-miRNA-mRNA interactions. B-cell translocation gene 2(BTG2), Abelson-related gene (ABL2), and vascular endothelial growth factor A (VEGFA) were identified to be the central genes with good predictive performance, which were significantly correlated with immune cell infiltration in OA, reflected by declined activated dendritic cells (aDCs), and elevated contents of B cells, macrophages, neutrophils, and T helper cells. Anisomycin, MG-132, thapsigargin, and lycorine were predicted to be the potential candidate agents for OA intervention. , the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results obtained in normal or OA knee cartilage tissues and chondrocytes. Furthermore, BTG2 was identified to be negatively regulated by miR-125a-5p. BTG2, ABL2, and VEGFA can be regarded as potential predictive and treatment biomarkers for OA, which might guide the clinical therapy of OA.
目前，骨关节炎（OA）的早期诊断和治疗仍然是一项挑战。在本研究中，我们试图探索用于OA诊断和治疗的潜在生物标志物。基于从基因表达综合数据库（GEO）下载的OA患者和正常对照的滑膜组织的三个mRNA数据集，鉴定差异表达基因（DEGs）。此外，使用基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析来评估基因功能相关类别。然后，进行miRNA测序以鉴定差异表达的miRNA。最后，对三个mRNA数据集检测到的基因进行加权基因共表达网络分析（WGCNA），并构建具有DEGs和差异表达的微小RNA（miRNAs）的竞争性内源RNA（ceRNA）网络以鉴定核心基因。此外，分析了核心基因表达与免疫浸润之间的关系，并基于连通性图谱数据库预测了OA的候选药物。进行定量逆转录PCR（qRT-PCR）、蛋白质免疫印迹分析和免疫荧光染色，以验证正常组织和OA组织及软骨细胞中差异表达的miRNAs和差异表达的靶基因的表达水平。miRNA-mRNA网络也在软骨细胞中得到验证。共鉴定出259个DEGs和26个差异表达的miRNAs，其中预测了94个miRNA-mRNA相互作用。WGCNA中的棕色模块与OA的临床特征相关性最高。将棕色模块基因与miRNA-mRNA对重叠后，获得27个miRNA-mRNA对。构建了一个具有5505个lncRNA-miRNA-mRNA相互作用的ceRNA网络。鉴定出B细胞易位基因2（BTG2）、阿贝尔森相关基因（ABL2）和血管内皮生长因子A（VEGFA）为具有良好预测性能的核心基因，它们与OA中的免疫细胞浸润显著相关，表现为活化树突状细胞（aDCs）减少，B细胞、巨噬细胞、中性粒细胞和辅助性T细胞含量升高。茴香霉素、MG-132、毒胡萝卜素和石蒜碱被预测为OA干预的潜在候选药物。本研究中鉴定的差异表达的miRNAs和生物标志物的表达水平与在正常或OA膝关节软骨组织及软骨细胞中获得的结果一致。此外，鉴定出BTG2受miR-125a-5p负调控。BTG2、ABL2和VEGFA可被视为OA的潜在预测和治疗生物标志物，可能指导OA的临床治疗。
