[Bioinformatics analysis of differently expressed genes in osteoblastic sarcoma and screening of key genes]

Objective: To screen the different expressed genes between osteosarcoma and normal osteoblasts, and find the key genes for the occurrence and development of osteosarcoma. Methods: The gene expression dataset GSE33382 of normal osteoblasts and osteosarcoma was obtained from Gene Expression Omnibus (GEO) database. The different expressed genes between normal osteoblasts and osteosarcoma were screened by limma package of R language, and the different expressed genes were analyzed by Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The protein interaction network was constructed by the String database, and the network modules in the interaction network were screened by the molecular complex detection (MCODE) plug-in of Cytoscape software. The different expressed genes contained in the first three main modules screened by MCODE were analyzed by gene ontology (GO) using the BiNGO module of Cytoscape software. The MCC algorithm was used to screen the top 10 key genes in the protein interaction network. The gene expression and survival dataset GSE39055 of osteosarcoma was obtained from GEO database, and the survival analysis was performed by Kaplan-Meier method. The data of 48 patients with osteosarcoma treated in the First Affiliated Hospital of Fujian Medical University from January 2005 to December 2015 were selected for verification. The expression of STC2 protein in osteosarcoma was detected by immunohistochemical method, and the survival analysis was carried out combined with the clinical data of the patients. Results: A total of 874 different expressed genes were identified from GSE33382 dataset, including 402 down-regulated genes and 472 up-regulated genes. KEGG enrichment analysis showed that different expressed genes were mainly related to p53 signal pathway, glutathione metabolism, extracellular matrix receptor interaction, cell adhesion molecules, folate tolerance, and cell senescence. The top 10 key genes in the interaction network were GAS6, IL6, RCN1, MXRA8, STC2, EVA1A, PNPLA2, CYR61, SPARCL1 and FSTL3. STC2 was related to the survival rate of patients with osteosarcoma (P<0.05). The results showed that the expression of STC2 protein was related to tumor size and Enneking stage in 48 cases of osteosarcoma. The median survival time of 25 cases with STC2 high expression was 21.4 months, and that of 23 cases with STC2 low expression was 65.4 months. The survival rate of patients with high expression of STC2 was lower than that of patients with low expression of STC2 (P<0.05). Conclusions: Bioinformatics analysis can effectively screen the different expressed genes between osteosarcoma and normal osteoblasts. STC2 is one of the important predictors for the prognosis of osteosarcoma.