Identification of Differentially Expressed Genes and Signaling Pathways in Glioma by Integrated Bioinformatics Analysis.

BACKGROUND

Gene alterations are very vital when it comes to the molecular pathogenesis of glioma. In this study, there was the design of the probable candidate genes in the glioma.

METHODS

Gene Expression Omnibus (GEO) database data sets of glioma tissue were retrieved and the differentially expressed genes (DEGs) from the individual microarray were merged. The following were performed: Gene Ontology; enrichment analysis; Kyoto Encyclopedia of Genes and Genomes (KEGG); pathway analysis; protein-protein interaction networks analysis.

RESULTS

The following were selected: 4 GEO data sets that included 370 high-grade glioma samples as well as 169 low-grade glioma samples. Identification of a total of 174 DEGs was done. Out of the identified DEGs, 82 were upregulated and 92 were downregulated genes. According to the Gene Ontology analysis, the primary biologic focus of DEGs included passive transmembrane transporter activity, regulation of channel activity, as well as the revelation that the biologic roles of DEGs aimed primarily on regulating channel activity, as well as the monovalent inorganic cation transmembrane transporter activity. The most significant pathway in KEGG analysis was PI3K-AKT signaling pathway. Some of the significant hub genes as per the protein-protein interaction network analysis included CDC20, NDC80, DLGAP5, CENPF, CENPE, ASPM, TPX2, TOP2A, RRM2, and PRC1.

CONCLUSION

From this study, it is evidenced that the use of integrated bioinformatics analyses in screening for pathways and DEGs in glioma can help us understand the clinical significance of understanding glioma, the molecular mechanism that underlies the development of glioma, as well as the provision of an effective target to treat glioma.