Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis

BACKGROUND: Searching for the production mechanism of synovial lesions helps to find precise therapeutic targets and improve prognosis. The previous identification and screening of differential genes in osteoarthritis (OA) pathogenesis were well combined to further build a risk prognosis model of OA, which is beneficial to the diagnosis and treatment of patients with OA. METHODS: The synovia-related chip data sets GSE82107, GSE12021, GSE55457, and GSE55235 were downloaded from the public database of Gene Expression Omnibus (GEO), and 40 cases of synovial tissues of OA and 36 cases of normal synovial tissues were included. R software was used to screen differentially expressed genes (DEGs), Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The STRING online analysis tool and Cytoscape software were used to further screen key genes, and a prognostic model of OA susceptibility risk was constructed. RESULTS: The results showed 1,921 differential genes, including 762 upregulated genes and 1,159 downregulated genes, which were mainly involved cell growth, cell adhesion, skeletal muscle growth, iron ion binding, ubiquitin protein ligase binding, and hormone receptor binding. Co-acquisition based on 10 key target genes of the protein interaction network, containing CTNNB1, GSK3B, STAT1, RHOC, HDAC9, PSEN1, KDM5C, BACE1, JAK3, and CUL1. The area under the concentration-time curve (AUC) was used to evaluate the prognostic model of OA risk, and the curve results showed that the prognostic model had high accuracy and validity (AUC =0.690). CONCLUSIONS: Bioinformatics analysis was applied to screen out the DEG profiles of OA. This may provide functional predictions to provide new ideas for treatment of the disease and may be a biological marker for its diagnosis and a potential target for treatment. The construction of the risk and prognosis model is beneficial to the risk assessment of rehabilitation function recovery of patients with OA, the evaluation of the severity of the disease and the subsequent treatment guidance.