Identification of differential splicing genes in gliomas using exon expression profiling
- Authors:
- Published online on: October 27, 2014 https://doi.org/10.3892/mmr.2014.2775
- Pages: 843-850
-
Copyright: © Yu et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].
Metrics: Total
Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )
Abstract
Diffuse gliomas are the most common type of malignant primary brain tumor, and their initiation and/or progression are often associated with alternative splicing. They produce an enormous economic burden on society and greatly impair the quality of life of those affected. The aim of the current study was to explore the differentially expressed genes (DEGs) observed in glioblastoma (GBM) and oligodendroglioma (OD) at the splicing level, and to analyze their functions in order to identify the underlying molecular mechanisms of gliomas. The exon‑level expression profile data GSE9385 was downloaded from the Gene Expression Omnibus database, and included 26 GBM samples, 22 OD samples and 6 control brain samples. The differentially expressed exon‑level probes were analyzed using the microarray detection of alternative splicing algorithm combined with the splicing index method, and the corresponding DEGs were identified. Next, a Gene Ontology enrichment analysis of the DEGs was performed. Additionally, the protein‑protein interaction (PPI) networks were constructed based on the depth‑first search algorithm. A total of 300 DEGs were identified to be shared by GBM and OD, including 97 upregulated and 203 downregulated DEGs. Furthermore, screening with a defined threshold identified 6 genes that were highly expressed in GBM, including AFF2, CACNA2D3 and ARPP21, while the 6 highly expressed genes in OD notably included CNTN2. The TP53 and HIST1H3A genes were the hub nodes in the PPI network of DEGs from GBM, while CNTN2 was linked to the highest degree in the OD PPI network. The present study provides a comprehensive bioinformatics analysis of DEGs in GBM and OD, which may provide a basis for understanding the initiation and/or progression of glioma development.