[Identification of key genes and functions in lung metastasis of osteosarcoma based on bioinformatics].

OBJECTIVE

To screen the differentially expressed genes of lung metastasis of osteosarcoma by bioinformatics, and explore their functions and regulatory networks.

METHODS

The data set of GSE14359 was screened from GEO database(http://www.ncbi.nlm.nih.gov/gds) and the differentially expressed gene(DEG) was identified using GEO2R online tool. Download osteosarcoma disease related miRNAs from the online HMMD database(http://www.cuilab.cn/hmdd) and then FunRich software was used to predict the target gene, intersects with DEG to obtains the target gene. The miRNA-mRNA relationship pairs were formed according to the targeted joints, then the data was imported into Cytoscape for visualization, DAVID was used to performe GO and KEGG analysis on target genes, STRING was used to construct PPI network, Cytoscape visualization, CytoHubba plug-in screening central genes and online website for expression and survival analysis.

RESULTS

Total 704 DEGs were identified, consisting of 477 up-regulated genes and 227 down regulated genes. FunRich predicted 7 888 mRNAs and 343 target genes were obtained through intersection of the two. KEGG analysis showed that it was mainly involved in focal adhesion, ECM receptor interaction, TNF signal pathway, PI3K-Akt signal pathway, IL-17 signal pathway and MAPK signal pathway. Ten central genes (CCNB1, CHEK1, AURKA, DTL, RRM2, MELK, CEP55, FEN1, KPNA2, TYMS) were identified as potential key genes. Among them, CCNB1, DTL, MELK were highly correlated with poor prognosis.

CONCLUSION

The key genes and functional pathways identified in this study may be helpful to understand the molecular mechanism of the occurrence and progression of lung metastases from osteosarcoma, and provide potential therapeutic targets.