Dataset Information

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value.

ABSTRACT: Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC6500689 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value.

Zhou Yangmei Y Yang Li L Zhang Xiaoxi X Chen Rui R Chen Xiuqiong X Tang Wenhua W Zhang Mengxian M

BioMed research international 20190415

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on T ...[more]

PMID: 31119182

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Project description:Background Glioblastoma (GBM) is one of the most common and malignant primary brain tumors in adults, with high mortality rates and limited treatment. Based on bioinformatic analyses, this study aimed to identify biomarkers and relevant molecular pathways that may serve as potential targets for the treatment of GBM. Methods Expression profiles were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database; nine GBM samples and three normal samples were extracted from the GSE104267 dataset. Differentially-expressed messenger RNA (mRNA) and long non-coding RNA (lncRNA) were screened from the preprocessed dataset. The clusterProfiler package in R was used to perform a biological process (BP) analysis of gene ontology (GO), and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed separately in upregulated and downregulated groups. A competing endogenous RNA (ceRNA) network was constructed using Cytoscape. Based on data downloaded from The Cancer Genome Atlas (TCGA), Kaplan-Meier (K-M) survival curves were established. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to evaluate IL10RB antisense RNA 1 (IL10RB-AS1) expression in GBM tissue compared with that in normal brain tissue. Results A total of 253 differentially-expressed genes (DEGs) were obtained. Based on BP and KEGG enrichment annotation analyses, 11 lncRNA-related pathways were identified through function prediction analysis. A competing endogenous RNA (ceRNA) subnetwork, including 21 nodes and 29 regulatory pairs, was then constructed. Based on the clinical data of GBM in TCGA, one survival-related DEG, IL10RB-AS1, was identified using the log-rank statistical test. K-M survival curves of IL10RB-AS1 and expression levels of IL10RB-AS1 in both GBM and normal brain tissue were obtained. Conclusions Through the combination of bioinformatic analyses, one survival-related differentially-expressed lncRNA, IL10RB-AS1, was identified. This, along with several related signaling pathways and ceRNA systems that were elucidated in GBM have potential prognostic value and might offer new possibilities for the treatment of GBM.

Project description:Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

Dataset Information

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value.

Publications

Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value.

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