Dataset Information

Bioinformatical analysis of gene expression signatures of different glioma subtypes.

ABSTRACT: The aim of the present study was to identify the common molecular mechanisms of multiple glioma subtypes, including astrocytoma, glioblastoma and oligodendroglioma, in addition to the specific mechanisms of different types. The gene expression profile set GSE4290 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from three types of glioma, relative to non-tumor tissue, were calculated by the t-test method with a linear regression model. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was performed. GeneVenn online analysis software was used for the comparison of the DEGs between subtypes. A total of 795 DEGs, including 619 up and 176 downregulated DEGs were screened from the astrocytoma expression profiles; these were enriched in the KEGG pathways of 'neuroactive ligand-receptor interaction' (upregulated) and 'Wnt signaling pathway' (downregulated). Protein-protein interaction networks for astrocytoma, glioblastoma and oligodendroglioma were constructed with 1,617, 7,027 and 1,172 pairs, respectively. A total of 595 common DEGs were obtained between the three subtypes, which were enriched in pathways associated with neural signaling. Glioblastoma is a subtype of astrocytoma; there were 195 DEGs common between these subtypes that were not also associated with oligodendroglioma. DEGs unique to astrocytoma, glioblastoma and oligodendroglioma were associated with the development of the nervous system, the cell cycle and cell matrix components, respectively. The screened DEG p53 gene is likely to be critical for glioma development, including via the Wnt and p53 signaling pathways. Brain-derived neurotrophic factor and cyclin-dependent kinase 1 genes were also likely to be important in the mechanism of glioma development, and were associated with the cell cycle and p53 signaling pathways. Immune system-associated and cell matrix component pathways may be unique signaling pathways associated with astrocytoma and oligodendroglioma, respectively.

SUBMITTER: Wang R

PROVIDER: S-EPMC5778919 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Bioinformatical analysis of gene expression signatures of different glioma subtypes.

Wang Rui R Wei Jun J Li Zhaohui Z Tian Yu Y Du Chao C

Oncology letters 20171220 3

The aim of the present study was to identify the common molecular mechanisms of multiple glioma subtypes, including astrocytoma, glioblastoma and oligodendroglioma, in addition to the specific mechanisms of different types. The gene expression profile set GSE4290 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from three types of glioma, relative to non-tumor tissue, were calculated by the t-test method with a linear regression model. Kyoto Encyclo ...[more]

PMID: 29435008

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Project description:Aims:To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE). Methods:Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764. Results:A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. Conclusion:By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

Dataset Information

Bioinformatical analysis of gene expression signatures of different glioma subtypes.

Publications

Bioinformatical analysis of gene expression signatures of different glioma subtypes.

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