Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis - Li, Zhen; Chen, Zhenyue; Wang, Xiaotan; Li, Zehui; Sun, He; Wei, Jinqiang; Zeng, Xianzhong; Cao, Xuewei; Wan, Chao (Frontiers in Genetics, 2022)
Purpose: Currently, the early diagnosis and treatment of osteoarthritis (OA) remain to be a challenge. In the present study, we attempted to explore the biomarkers for the diagnosis and treatment of OA. Methods: The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from 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 three mRNA datasets and competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) were 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 Connectivity Map database. 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 vitro in chondrocytes. Results: Total 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA-mRNA interactions were predicted. Brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping brown module genes with miRNA-mRNA pairs, 27 miRNA-mRNA pairs were obtained. 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. Anisomycin, MG-132, thapsigargin and lycorine were predicted to be the candidate agents for OA. In vitro, the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results validated in normal or OA knee tissues and in chondrocytes.Furthermore,miR-125a-5p negatively regulated BTG2. Conclusion: BTG2, ABL2 and VEGFA can be suggested as the predictive and treatment biomarkers for OA