Dataset Information

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.

ABSTRACT: Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It's necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 contains 120 samples, including 60 WHO II samples and 24 WHO IV samples that were selected in our analysis. We used the GEO2R tool to pick out differently expressed genes (DEGs) between low-grade glioma and high-grade glioma, and then we used the database for annotation, visualization and integrated discovery to perform gene ontology analysis and Kyoto encyclopedia of gene and genome pathway analysis. Furthermore, we used the Cytoscape search tool for the retrieval of interacting genes with molecular complex detection plug-in applied to achieve the visualization of protein-protein interaction (PPI). We selected 15 hub genes with higher degrees of connectivity, including tissue inhibitors metalloproteinases-1 and serum amyloid A1; additionally, we used GSE53733 containing 70 glioblastoma samples to conduct Gene Set Enrichment Analysis. In conclusion, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of glioblastoma.

SUBMITTER: Xu Y

PROVIDER: S-EPMC6409090 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.

Xu Yang Y Geng Rongxin R Yuan Fan'en F Sun Qian Q Liu Baohui B Chen Qianxue Q

PeerJ 20190307

Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It's necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 ...[more]

PMID: 30867991

Similar Datasets

Project description:Primary glioblastoma(pGBM) is the most malignant tumor of the central nervous system. Radiotherapy, chemotherapy and surgical treatment have little effect on the survival of pGBM patients. The prognosis is often poorly once the tumor recurs. It is urgent to develop new therapies for patients. In recent years, studies have been clarified that miRNA have a powerful regulating effect on the genes. However, the main group of miRNAs in regulating long-term survival specific related genes of pGBM is still unclear. Given that the survival period of most glioma patients is relatively short, studying long-term survival patients with pGBM is of great value for this disease. Our study aim to identify key miRNAs with long-term survival related genes present in pGBM and uncover their potential mechanisms. The gene expression profiles of GSE53733, GSE15824, GSE30563, GSE50161 were obtained from the Gene Expression Omnibus database. Firstly, samples were divided into 3 groups according to its survival time and each group compare to the normal control group. Then we obtained differential expression genes (DEGs) with a long-term survival specific (LTSDEGs) and a short-term survival specific DEGs (STSDEGs). Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted with LTSDEGs and STSDEGs together. Moreover, we used the UALCAN database to verify LTSDEGs and STSDEGs, and obtained long-term verified survival specific DEGs(LTVSDEGs) and short-term verified survival specific DEGs(STVSDEGs). Finally, we established the predicted key miRNAs-LTVSDEGs interaction network. The protein expressions of the top 4 LTVSDEGs were verified in the HPA database with immunohistochemical staining. In total, we found 260 genes changed in LTSDEGs and 822 genes changed in STSDEGs. GO and KEGG results shown that the major changes are focused on tumor metabolism. 9 LTVSDEGs and 18 STVSDEGs were verified in UALCAN database. As for protein expression verification in top 4 LTVSDEGs, ZNF630, BLVRB and RPA3 were verified, while TPBG was not detected. We obtained 59 key miRNA from the predicted key miRNAs-LTVSDEGs interaction network. 25 key miRNAs were verified using GSE90603. Finally, we constructed the key miRNAs-LTVSDEGs network using a Sankey diagram, including 25 miRNAs and 7 LTVSDEGs. In conclusion, our study shows that there is a close relationship between metabolic changes and survival in pGBM. Besides, we established a key miRNAs-LTVSDEGs network for pGBM, which could be the key path in prolonging the life of pGBM patients.

Project description:BackgroundMethylation plays a key role in the aetiology and pathogenesis of prostate cancer (PCa). This study aimed to identify aberrantly methylated differentially expressed genes (DEGs) and pathways in PCa and explore the underlying mechanisms of tumourigenesis.MethodsExpression profile (GSE29079) and methylation profile (GSE76938) datasets were obtained from the Gene Expression Omnibus (GEO). We used R 3.4.4 software to assess aberrantly methylated DEGs. The Cancer Genome Atlas (TCGA) RNA sequencing and Illumina HumanMethylation450 DNA methylation data were utilized to validate screened genes. Functional enrichment analysis of the screened genes was performed, and a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Gens (STRING). The results were visualized in Cytoscape. After confirmation using TCGA, cBioPortal was used to examine alterations in genes of interest. Then, protein localization in PCa cells was observed using immunohistochemistry.ResultsOverall, 536 hypomethylated upregulated genes were identified that were enriched in biological processes such as negative regulation of transcription, osteoblast differentiation, intracellular signal transduction, and the Wnt signalling pathway. Pathway enrichment showed significant changes in factors involved in AMPK signalling, cancer, and adherens junction pathways. The hub oncogenes were AKT1, PRDM10, and FASN. Additionally, 322 hypermethylated downregulated genes were identified that demonstrated enrichment in biological processes including positive regulation of the MAPK cascade, muscle contraction, ageing, and signal transduction. Pathway analysis indicated enrichment in arrhythmogenic right ventricular cardiomyopathy (ARVC), focal adhesion, dilated cardiomyopathy, and PI3K-AKT signalling. The hub tumour suppressor gene was FLNA. Immunohistochemistry showed that AKT1, FASN, and FLNA were mainly expressed in PCa cell cytoplasm, while PRDM10 was mainly expressed in nuclei.ConclusionsOur results identify numerous novel genetic and epigenetic regulatory networks and offer molecular evidence crucial to understanding the pathogenesis of PCa. Aberrantly methylated hub genes, including AKT1, PRDM10, FASN, and FLNA, can be used as biomarkers for accurate PCa diagnosis and treatment. In conclusion, our study suggests that AKT1, PRDM10, and FASN may be tumour promoters and that FLNA may be a tumour suppressor in PCa. We hope these findings will draw more attention to these hub genes in future cancer studies.

Dataset Information

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.

Publications

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets