Glioblastoma multiforme (GBM) is the most malignant type of human glioma, and has a poor prognosis. Screening differentially expressed genes (DEGs) in brain tumor samples and normal brain samples is of importance for identifying GBM and to design specific-targeting drugs. The transcriptional profile of GSE30563, containing three genechips of brain tumor samples and three genechips of normal brain samples, was downloaded from Gene Expression Omnibus to identify the DEGs. The differences in the expression of the DEGs in the two different samples were compared through hierarchical biclustering. The co-expression coefficient of the DEGs was calculated using the information from COXPRESdb, the network of the DEGs was constructed and functional enrichment and pathway analysis were performed. Finally, the transcription factors of important DEGs were predicted. A total of 1,006 DEGs, including 368 upregulated and 638 downregulated DEGs, were identified. A close correlation was demonstrated between six important genes, associated with immune response, HLA-DQB1, HLA-DRB1, HLA-DPA1, HLA-B, HLA-DMA and HLA-DRA, and the immune response. Allograft rejection was selected as the most significant pathway. A total of 17 transcription factors, including nuclear factor (NF)-κB and NF-κB1, and their binding sites containing these six DEGs, were also identified. The DEGs, including major histocompatibility complex (MHC) class II, DQβ1, MHC class II, DRβ1, MHC class IB, MHC class II, DMα, MHC class II, DPα1, MHC class II, DRα, may provide novel targets for the diagnosis and treatment of GBM. The transcription factors of these six genes and their binding sites may also provide evidence and direction for identifying target-specific drugs.

The present study aimed to investigate changes at the transcript level that are associated with spontaneous astrocytoma progression, and further, to discover novel targets for glioma diagnosis and therapy. GSE4290 microarray data downloaded from Gene Expression Omnibus were used to identify the differentially expressed genes (DGEs) by significant analysis of microarray (SAM). The Short Time Series Expression Miner (STEM) method was then applied to class these DEGs based on their degrees of differentiation in the process of tumor progression. Finally, EnrichNet was used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis based on a protein-protein interaction (PPI) network. A total of 4,506 DEGs were detected, and the number of DEGs was the highest in grade IV cells (2,580 DEGs). These DEGs were classified into nine clusters by the STEM method. In total, 11 KEGG pathways with XD-scores larger than the threshold (0.96) were obtained. The DEGs enriched in pathways 1 (extracellular matrix-receptor interaction), 3 (phagosome) and 6 (type I diabetes mellitus) mainly belonged to cluster 5. Pathway 2 (long-term potentiation), 4 (Vibrio cholerae infection) and 5 (epithelial cell signaling in Helicobacter pylori infection) was involved with DEGs that belonged to different clusters. Significant changes in gene expression occurred during glioma progression. Pathways 1, 3 and 6 may be important for the deterioration of glioma into glioblastoma, and pathways 2, 4 and 5 may have a role at each stage during glioma progression. The associated DEGs, including SV2, NMDAR and mGluRs, may be suitable as biomarkers or therapeutic targets for gliomas.

To improve treatment strategies of glioma, microarray data were applied to screen target molecules that were regulated by microRNAs (miRNAs). GSE31262 was downloaded from Gene Expression Omnibus, including five neural stem cells samples from normal human and nine stem cells samples from glioma patients. Differentially expressed genes (DEGs) were identified with Multtest package and Limma package of R language, and false discovery rate < 0.05 and |log2FC (fold change)| >1 were chosen as cut-off criterion. Hierarchical clustering and pathway enrichment analysis of DEGs were performed using pheatmap package of R language and KOBAS software, respectively. miRNAs related to up- and down-regulated DEGs were, respectively, predicted by WebGestalt software and its miRNAs-target DEGs interaction network were, respectively, constructed by STRING database. Bingo plug-in in Cytoscape software was applied to analyze Gene Ontology functional enrichment analysis for up- and down-regulated DEGs in network, respectively. A total of 428 DEGs were selected, including 331 down-regulated and 97 up-regulated DEGs. Hierarchical clustering analysis showed that glioma samples and normal samples were completely separated. Pathway analysis indicated that CDK2 and WEE1 participated in the cell cycle. miR-124 could simultaneously regulate up-regulated (ELAVL1 and EZH2) and down-regulated (BACE1) DEGs. The down-regulated genes (KIF23, WEE1 and CDK2) were associated with cell division, while the up-regulated genes (PLP1 and MBP) were related to myelination of neurons. miR-124 might participate in development of glioma by regulating BACE1, ELAVL1 and EZH2. The biomarkers (KIF23, WEE1, CDK2, PLP1 and MBP) were considered as therapeutic targets of glioma.

The present study aimed to identify differentially expressed genes (DEGs) regulated by transcription factors (TFs) in glioblastoma, by conducting a bioinformatics analysis. The results of the present study may provide potential therapeutic targets that are involved in the development of glioblastoma. The GSE4290 raw data set was downloaded from the Gene Expression Omnibus database, and consisted of 23 non‑tumor samples and 77 glioblastoma (grade 4) tumor samples. Robust Multichip Averaging was used to identify DEGs between the glioblastoma and non‑tumor samples. Functional enrichment analysis of the DEGs was also performed. Based on the TRANSFAC® database, TFs associated with the glioblastoma gene expression profile were used to construct a regulatory network. Furthermore, trimmed subnets were identified according to calculated Z‑scores. A total of 676 DEGs were identified, of which 190 were upregulated and 496 were downregulated. Gene Ontology analysis demonstrated that the majority of these DEGs were functionally enriched in synaptic transmission, regulation of vesicle‑mediated transport and ion‑gated channel activity. In addition, the enriched Kyoto Encyclopedia of Genes and Genomes pathway included neuroactive ligand‑receptor interaction, calcium signaling pathway, p53 signaling pathway and cell cycle. Based on the TRANSFAC® database, transcriptional regulatory networks with 2,246 nodes and 4,515 regulatory pairs were constructed. According to the Z‑scores, the following candidate TFs were identified: TP53, SP1, JUN, STAT3 and SPI1; alongside their downstream DEGs. TP53 was the only differentially expressed TF. These candidate TFs and their downstream DEGs may have important roles in the progression of glioblastoma, and could be potential biomarkers for clinical treatment.