Identification of hub genes specific to pulmonary metastasis in osteosarcoma through integrated bioinformatics analysis.

Abstract

Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis. The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected. Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database. In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient. Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.