BACKGROUND: Osteosarcoma is the most frequent primary bone cancer derived from primitive mesenchymal cells. The aim of this study was to explore the molecular mechanism of the development and progression of osteosarcoma.

MATERIAL AND METHODS: The gene expression profiles of osteosarcoma from 17 specimens (3 normal and 14 osteosarcoma) were downloaded from the GEO database. The differentially expressed genes were identified by use of the Limma package. DAVID and Enrichment Map were used to perform GO and KEGG pathways enrichment analysis and to integrate enrichment results of differentially expressed genes (DEGs). Protein-protein interaction network was constructed and analyzed to screen out the potential regulatory proteins using the STRING online tools.

RESULTS: A total of 417 DEGs were screened, including 215 up-regulated and 202 down-regulated ones, accounting for 51.56% and 48.4%, respectively. In GO term, a total of 12 up-regulated expression genes were enriched in Cellular Component. The up-regulated DEGs were enriched in 6 KEGG pathways while the down-regulated expression genes were enriched in 2 KEGG pathways. The constructed PPI network was aggregated with 1006 PPI relationships and 238 nodes, accounting for 57.07% of DEGs. We found that CD20, MCM, and CCNB1 (down-regulated) in cell cycle and ECM, ITGA, RTKin (up-regulated) in focal adhesion had important roles in the progression of osteosarcoma.

CONCLUSIONS: The identified DEGs and their enriched pathways provide references for the exploration of the molecular mechanism of the development and progression of osteosarcoma. Moreover, the key genes (CD20, ECM, and ITGA) may be useful in treatment and diagnosis of osteosarcoma.

Keywords: Biomarkers, Tumor - metabolism, Antigens, CD20 - genetics, Bone Neoplasms - metabolism, Databases, Genetic, Extracellular Matrix Proteins - metabolism, Gene Expression Profiling - methods, Integrins - metabolism, Oligonucleotide Array Sequence Analysis, Osteoporosis - genetics, Osteosarcoma - metabolism, Protein Interaction Maps, Signal Transduction - genetics, Transcription Factors - metabolism