Identification of potential crucial genes and molecular mechanisms in glioblastoma multiforme by bioinformatics analysis

Glioblastoma multiforme (GBM) is the most common and malignant brain tumor of the adult central nervous system and is associated with poor prognosis. The present study aimed to identify the hub genes in GBM in order to improve the current understanding of the underlying mechanism of GBM. The RNA-seq data were downloaded from The Cancer Genome Atlas database. The edgeR package in R software was used to identify differentially expressed genes (DEGs) between two groups: Glioblastoma samples and normal brain samples. Gene Ontology (GO) functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using Database for Annotation, Visualization and Integrated Discovery software. Additionally, Cytoscape and Search Tool for the Retrieval of Interacting Genes/Proteins tools were used for the protein-protein interaction network, while the highly connected modules were extracted from this network using the Minimal Common Oncology Data Elements plugin. Next, the prognostic significance of the candidate hub genes was analyzed using UALCAN. In addition, the identified hub genes were verified by reverse transcription-quantitative (RT-q) PCR. In total, 1,483 DEGs were identified between GBM and control samples, including 954 upregulated genes and 529 downregulated genes (P<0.01; fold-change >16) and these genes were involved in different GO terms and signaling pathways. Furthermore, CDK1, BUB1, BUB1B, CENPA and GNG3 were identified as key genes in the GBM samples. The UALCAN tool verified that higher expression level of CENPA was relevant to poorer overall survival rates. In conclusion, CDK1, BUB1, BUB1B, CENPA and GNG3 were found to be potential biomarkers for GBM. Additionally, ‘cell cycle’ and ‘γ-aminobutyric acid signaling’ pathways may serve a significant role in the pathogenesis of GBM.

Prediction of crucial epigenetically‑associated, differentially expressed genes by integrated bioinformatics analysis and the identification of S100A9 as a novel biomarker in psoriasis

Psoriasis is one of the most common immune-mediated inflammatory diseases of the skin. The identification of the pivotal molecular mechanisms responsible for the disease pathogenesis may lead to the development of novel therapeutic options. The present study aimed to identify pivotal differentially expressed genes (DEGs) and methylated DEGs in psoriasis. The raw data from gene microarrays were obtained from the Gene Expression Omnibus database. The data were processed using packages in Bioconductor. In total, 352 upregulated and 137 downregulated DEGs were identified. The upregulated DEGs were primarily enriched in the 'innate immune defense' response and the 'cell cycle'. The down-regulated DEGs were primarily enriched in 'cell adhesion' and 'tight junction pathways'. A total of 95 methylated DEGs were identified, which were significantly enriched in the 'interleukin (IL)-17 signaling pathway' and the 'response to interferon'. Based on a comprehensive evaluation of all algorithms in cytoHubba, the key epigenetic-associated hub genes (S100A9, SELL, FCGR3B, MMP9, S100A7, IL7R, IRF7, CCR7, IFI44, CXCL1 and LCN2) were screened out. In order to further validate these genes, the present study constructed a model of imiquimod (IMQ)-induced psoriasiform dermatitis using mice. The levels of these hub genes were increased in the IMQ group. The knockdown of methylation-regulating enzyme ten-eleven translocation (TET) 2 expression in mice attenuated the expression levels of S100A9, SELL, IL7R, MMP9, CXCL1 and LCN2. Furthermore, the hydroxymethylated level of S100A9 was highly expressed in the IMQ group and was significantly decreased by TET2 deficiency in mice. On the whole, using an integrative system bioinformatics approach, the present study identified a series of characteristic enrichment pathways and key genes that may serve as potential biomarkers in psoriasis.