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Bioinformatics Analyses of Significant Genes, Related Pathways and Candidate Prognostic Biomarkers in Glioblastoma

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Bioinformatics Analyses of Significant Genes, Related Pathways and Candidate Prognostic Biomarkers in Glioblastoma MOLECULAR MEDICINE REPORTS 18: 4185-4196, 2018 Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma LINGQI ZHOU1,2*, HAI TANG1-3*, FANG WANG4, LIZHI CHEN1,2, SHANSHAN OU1,2, TONG WU1,2, JIE XU1,2 and KAIHUA GUO1,2 1Department of Anatomy and Neurobiology; 2Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080; 3Guangdong Jiangmen Chinese Traditional Medicine College, Jiangmen, Guangdong 529000; 4Department of Molecular Diagnostics, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P.R. China Received February 1, 2018; Accepted June 6, 2018 DOI: 10.3892/mmr.2018.9411 Abstract. Glioblastoma (GBM) is the most common type and prognostic biomarker analyses. The GO results showed of malignant tumor of the central nervous system. The that the upregulated DEGs were significantly enriched in prognosis of patients with GBM is very poor, with a survival biological processes (BP), including immune response, cell time of ~15 months. GBM is highly heterogeneous and division and cell proliferation, and the downregulated DEGs highly aggressive. Surgical removal of intracranial tumors were also significantly enriched in BP, including cell growth, does provide a good advantage for patients as there is a high intracellular signal transduction and signal transduction by rate of recurrence. The understanding of this type of cancer protein phosphorylation. KEGG pathway analysis showed that needs to be strengthened, and the aim of the present study the upregulated DEGs were enriched in circadian entrainment, was to identify gene signatures present in GBM and uncover cytokine-cytokine receptor interaction and maturity onset their potential mechanisms. The gene expression profiles diabetes of the young, while the downregulated DEGs were of GSE15824 and GSE51062 were downloaded from the enriched in the TGF-β signaling pathway, MAPK signaling Gene Expression Omnibus database. Normalization of the pathway and pathways in cancer. All of the downregulated data from primary GBM samples and normal samples in the genes and the top 1,000 upregulated genes were selected to two databases was conducted using R software. Then, joint establish the PPI network, and the sub-networks revealed analysis of the data was performed. Gene ontology (GO) and that these genes were involved in significant pathways, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway including olfactory transduction, neuroactive ligand-receptor enrichment analyses were performed, and the protein-protein interaction and viral carcinogenesis. In total, seven genes interaction (PPI) network of the differentially expressed were identified as good prognostic biomarkers. In conclusion, genes (DEGs) was constructed using Cytoscape software. the identified DEGs and hub genes contribute to the Identification of prognostic biomarkers was conducted understanding of the molecular mechanisms underlying the using UALCAN. In total, 9,341 DEGs were identified in the development of GBM and they may be used as diagnostic GBM samples, including 9,175 upregulated genes and 166 and prognostic biomarkers and molecular targets for the downregulated genes. The top 1,000 upregulated DEGs and treatment of patients with GBM in the future. all of the downregulated DEGs were selected for GO, KEGG Introduction Glioma is the most common type of tumor in the central Correspondence to: Professor Kaihua Guo or Dr Jie Xu, nervous system. According to the literature (1,2), the incidence Department of Anatomy and Neurobiology, Zhongshan School of of gliomas is ~12 per 100,000 people and the proportion of Medicine, Sun Yat-sen University, 74 Zhongshan Road, Guangzhou, intracranial tumors that are gliomas is ~35-60%, which is a Guangdong 510080, P.R. China serious harm to human health. As stated in the pathological E-mail: [email protected] classification of the WHO (2007) (3), the degree of differ- E-mail: [email protected] entiation of tumor cells can be divided into four levels, with *Contributed equally the higher the grade of the tumor, the worse the prognosis of the patient with GBM. Level IV of the WHO classifica- Key words: glioblastoma, bioinformatics, key genes, pathways, tion has a very poor prognosis. According to statistics, prognosis each year ~77,000 new cases of patients with glioblastoma (GBM) are diagnosed in the United States and Europe, but the follow-up 5-year survival rate is <3% (4). Compared with other pathological types of glioma, the survival of patients with GBM is also calculated weekly and the majority of 4186 ZHOU et al: BIOINFORMATICS ANALYSES OF CANDIDATE BIOMARKERS FOR GLIOBLASTOMA patients with GBM die due to tumor recurrence. It is therefore GO and pathway enrichment analyses of DEGs. GO analysis imperative to find a new solution in order to treat patients is a common and useful method for annotating genes and gene with GBM. products and for identifying characteristic biological attributes Due to the popularization and development of supercom- of high-throughput genome or transcriptome data (14,15). puter technology, high-throughput platforms for the analysis KEGG (http://www.genome.jp/) is a well-known database of gene expression are growing. Microarray analysis and for systematic analysis of gene functions in biological path- next-generation sequencing are increasingly valued as essential ways, which links genomic information with higher-order tools in the field of medical oncology, with great clinical appli- functional information (16). The DEGs were mapped to the cations such as molecular classification of cancers, molecular relevant biological annotation using the DAVID online data- diagnosis, prognosis prediction, patient stratification, tumor base (https://david.ncifcrf.gov/) (17). DAVID is an essential response prediction and new drug targets discovery (5-7). A online tool for high-throughput gene functional analysis, number of tumor-related databases have been established, which provides the functionality to perform simultaneous such as Gene Expression Omnibus (GEO) (8) and TCGA (9). GO and KEGG analysis. P<0.05 was considered to indicate a Further, the uses of bioinformatics analysis are changing statistically significant difference. constantly. The 2016 edition of the WHO pathological grading criteria was the first to include guidelines for the molecular Integration of protein‑protein interaction (PPI) network and markers of glioma, indicating that genetic molecular diagnosis module analysis. Search Tool for the Retrieval of Interacting and gene analysis of the disease play an increasingly important Genes (18) (STRING, version 10.0, http://string-db.org/) was role (10). used to conduct PPI network and module analysis. STRING In the present study, original data (GSE15824 and (version 10.0) includes 9.6 million proteins from 2,031 organ- GSE51062) were downloaded from the GEO database isms. These proteins have 184 million interactions. The top (http://www.ncbi.nlm.nih.gov/geo/). A combined analysis 1,000 upregulated DEGs and all of the downregulated DEGs was conducted using limma package (11) in R software were uploaded to the search tool. Only experimentally-validated (version 3.3.2). In total, 74 GBM samples and 5 normal interactions with a combined score >0.4 were selected as samples were selected to identify differentially expressed significant. The whole giant network was then inputted into genes (DEGs). Subsequently, the DEGs were screened using Cytoscape (19) software (version 3.4) to evaluate primary Morpheus online software (https://software.broadinstitute. modules of the PPI sub-network using the plug-in Molecular org/morpheus/), followed by Gene Ontology (GO) and Kyoto Complex Detection (MCODE). The criteria were set as default. Encyclopedia of Genes and Genomes (KEGG) pathway GO analysis and KEGG pathway enrichment analyses were enrichment analyses. By analyzing the biological functions performed for DEGs in the modules using DAVID. P<0.05 and pathways of the DEGs, the present study provides insight was considered to indicate a statistically significant difference. into the development of GBM at the molecular level and iden- tifies candidate biomarkers for the future treatment, diagnosis Identification of prognostic biomarker. UALCAN (20) and prognosis of patients with GBM. (http://ualcan.path.uab.edu) was used to identify prognostic biomarkers in the top 100 regulated DEGs. UALCAN was used Materials and methods to construct an algorithm based on the TCGA level 3 RNA-seq database. All data were divided into 31 types according to the Microarray data. GSE15824 and GSE51062 were down- type of cancer. The top 100 upregulated and downregulated loaded from the GEO database. Both datasets were based DEGs, respectively, were uploaded to the UALCAN GBM on the Affymetrix GPL570 platform (Affymetrix Human database. P<0.05 was considered to indicate a statistically Genome U133 Plus 2.0 Array). In total, 5 normal samples and significant difference. The influence of genes and sex as an 22 primary GBM samples from GSE15824 and 52 primary influencing factor on the prognosis of disease were retrieved. GBM samples from GSE51062 were selected. Genes with insufficient data were automatically eliminated. Data quality control. Affy package (12) R software was used Results to perform quality control of all of the microarray CEL files and RNA degradation plots were drawn at the same time. Microarray chip quality
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