Identification of biomarkers and construction of a microRNA-mRNA regulatory network for ependymoma using integrated bioinformatics analysis.

Ependymomas (EPNs) are one of the most common types of malignant neuroepithelial tumors. In an effort to identify potential biomarkers involved in the pathogenesis of EPN, the mRNA expression profiles of the GSE25604, GSE50161, GSE66354, GSE74195 and GSE86574 datasets, in addition to the microRNA (miRNA/miR) expression profiles of GSE42657 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between EPN and normal brain tissue samples were identified using the Limma package in R and GEO2R, respectively. Functional and pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed using the Search Tool for Retrieval of Interacting Genes database, which was visualized using Cytoscape. The targeted genes of DEMs were predicted using miRWalk2.0 and a miRNA-mRNA regulatory network was constructed. Following analysis, a total of 948 DEGs and 129 DEMs were identified. Functional enrichment analysis revealed that 609 upregulated DEGs were significantly enriched in 'PI3K-Akt signaling pathway', while 339 downregulated DEGs were primarily involved in 'cell junction' and 'retrograde endocannabinoid signaling'. In addition, 6 hub genes [cyclin dependent kinase 1, CD44 molecule (Indian blood group) (CD44), proliferating cell nuclear antigen (PCNA), MYC, synaptotagmin 1 (SYT1) and kinesin family member 4A] and 6 crucial miRNAs [homo sapiens (hsa)-miR-34a-5p, hsa-miR-449a, hsa-miR-106a-5p, hsa-miR-124-3p, hsa-miR-128-3p and hsa-miR-330-3p] were identified as biomarkers and potential therapeutic targets for EPN. Furthermore, a microRNA-mRNA regulatory network was constructed to highlight the interactions between DEMs and their target DEGs; this included the hsa-miR-449a-SYT1, hsa-miR-34a-5p-SYT1, hsa-miR-330-3p-CD44 and hsa-miR-124-3p-PCNA pairs, whose expression levels were confirmed using reverse transcription-quantitative polymerase chain reaction. In conclusion, the present study may provide important data for the investigation of the molecular mechanisms of EPN pathogenesis.

Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis.

Medulloblastoma (MB) is the most common malignant brain tumor in children. The aim of the present study was to predict biomarkers and reveal their potential molecular mechanisms in MB. The gene expression profiles of GSE35493, GSE50161, GSE74195 and GSE86574 were downloaded from the Gene Expression Omnibus (GEO) database. Using the Limma package in R, a total of 1,006 overlapped differentially expressed genes (DEGs) with the cut-off criteria of P<0.05 and |log2fold-change (FC)|>1 were identified between MB and normal samples, including 540 upregulated and 466 downregulated genes. Furthermore, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool to analyze functional and pathway enrichment. The Search Tool for Retrieval of Interacting Genes database was subsequently used to construct a protein-protein interaction (PPI) network and the network was visualized in Cytoscape. The top 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified from the PPI network. The survival curves for hub genes in the dataset GSE85217 predicted the association between the genes and survival of patients with MB. The top 3 modules were identified by the Molecular Complex Detection plugin. The results indicated that the pathways of DEGs in module 1 were primarily enriched in cell cycle, progesterone-mediated oocyte maturation and oocyte meiosis; and the most significant functional pathways in modules 2 and 3 were primarily enriched in mismatch repair and ubiquitin-mediated proteolysis, respectively. These results may help elucidate the pathogenesis and design novel treatments for MB.

Large animal models of traumatic brain injury.

Purpose/Aim: Animal models of traumatic brain injury (TBI) provide powerful tools to study TBI in a controlled, rigorous and cost-efficient manner. The mostly used animals in TBI studies so far are rodents. However, compared with rodents, large animals (e.g. swine, rabbit, sheep, ferret, etc.) show great advantages in modeling TBI due to the similarity of their brains to human brain. The aim of our review was to summarize the development and progress of common large animal TBI models in past 30 years. MATERIALS AND METHODS: Mixed published articles and books associated with large animal models of TBI were researched and summarized. RESULTS: We majorly sumed up current common large animal models of TBI, including discussion on the available research methodologies in previous studies, several potential therapies in large animal trials of TBI as well as advantages and disadvantages of these models. CONCLUSIONS: Large animal models of TBI play crucial role in determining the underlying mechanisms and screening putative therapeutic targets of TBI.