Exploring the key genes and pathways of side population cells in human osteosarcoma using gene expression array analysis

Abstract

Background: Human osteosarcoma (OS) is one of the most common primary bone sarcoma, because of early metastasis and few treatment strategies. It has been reported that the tumorigenicity and self-renewal capacity of side population (SP) cells play roles in human OS via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) that regulated between the SP cells and the non-SP cells from primary human OS and identify their functions and molecular pathways associated with OS.

Methods: The gene expression profile GSE63390 was downloaded, and bioinformatics analysis was made.

Results: One hundred forty-one DEGs totally were identified. Among them, 72 DEGs (51.06%) were overexpressed, and the remaining 69 DEGs (48.94%) were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene-gene interaction network analysis such as EIF4E, FAU, HSPD1, IL-6, and KISS1, which may have a relationship with the development process of OS. We also discovered that EIF4E/mTOR signaling pathway could be a potential research target for therapy and tumorigenesis of OS.

Conclusion: This analysis provides a comprehensive understanding of the roles of DEGs coming from SP cells in the development of OS. However, these predictions need further experimental validation in future studies.

Keywords: Bioinformatics analysis; Differentially expressed genes; Osteosarcoma; Side population cells.

Fig. 1

Gene ontology (GO) enrichment analysis of biological processes (a), molecular functions (b), and cellular components (c). The red star in GO terms means term P value < 0.05, and the double red stars in GO terms mean term P value < 0.01

Fig. 2

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs). The different node colors mean different pathways, and the closer the colors are, the closer the function clustering of pathways are

Fig. 3

The distribution of core genes in the interaction network. The black node means the core gene. The red line means the fitted line, and the blue line means the power law. The correlation between the data points and corresponding points on the line is approximately 0.932. The R-squared value is 0.846, giving a relatively high confidence that the underlying model is indeed linear

Fig. 4

The top 10 modules from the gene–gene interaction network. The squares represent the differentially expressed genes (DEGs) in modules, and the lines show the interaction between the DEGs

Fig. 5

The interaction network of the top 10 core genes. The nodes indicated the top core genes, and the edges indicated the interactions between the core genes