The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis
Glioma is the most common malignant tumor in the central nervous system. This study aims to explore the potential mechanism and identify gene signatures of glioma. The glioma gene expression profile GSE4290 was analyzed for differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclop...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2017/1278081 |
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| author | Mingfa Liu Zhennan Xu Zepeng Du Bingli Wu Tao Jin Ke Xu Liyan Xu Enmin Li Haixiong Xu |
| author_facet | Mingfa Liu Zhennan Xu Zepeng Du Bingli Wu Tao Jin Ke Xu Liyan Xu Enmin Li Haixiong Xu |
| author_sort | Mingfa Liu |
| collection | DOAJ |
| description | Glioma is the most common malignant tumor in the central nervous system. This study aims to explore the potential mechanism and identify gene signatures of glioma. The glioma gene expression profile GSE4290 was analyzed for differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathways. A protein-protein interaction (PPI) network was constructed to find the hub genes. Survival analysis was conducted to screen and validate critical genes. In this study, 775 downregulated DEGs were identified. GO analysis demonstrated that the DEGs were enriched in cellular protein modification, regulation of cell communication, and regulation of signaling. KEGG analysis indicated that the DEGs were enriched in the MAPK signaling pathway, endocytosis, oxytocin signaling, and calcium signaling. PPI network and module analysis found 12 hub genes, which were enriched in synaptic vesicle cycling rheumatoid arthritis and collecting duct acid secretion. The four key genes CDK17, GNA13, PHF21A, and MTHFD2 were identified in both generation (GSE4412) and validation (GSE4271) dataset, respectively. Regression analysis showed that CDK13, PHF21A, and MTHFD2 were independent predictors. The results suggested that CDK17, GNA13, PHF21A, and MTHFD2 might play important roles and potentially be valuable in the prognosis and treatment of glioma. |
| format | Article |
| id | doaj-art-5d7644794d064a779c5ea0dd0cebc1e0 |
| institution | Kabale University |
| issn | 2314-8861 2314-7156 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-5d7644794d064a779c5ea0dd0cebc1e02025-02-03T01:12:11ZengWileyJournal of Immunology Research2314-88612314-71562017-01-01201710.1155/2017/12780811278081The Identification of Key Genes and Pathways in Glioma by Bioinformatics AnalysisMingfa Liu0Zhennan Xu1Zepeng Du2Bingli Wu3Tao Jin4Ke Xu5Liyan Xu6Enmin Li7Haixiong Xu8Department of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaDepartment of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaDepartment of Pathology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaDepartment of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, ChinaDepartment of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaDepartment of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaInstitute of Oncologic Pathology, Shantou University Medical College, Shantou 515041, ChinaDepartment of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, ChinaDepartment of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, ChinaGlioma is the most common malignant tumor in the central nervous system. This study aims to explore the potential mechanism and identify gene signatures of glioma. The glioma gene expression profile GSE4290 was analyzed for differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathways. A protein-protein interaction (PPI) network was constructed to find the hub genes. Survival analysis was conducted to screen and validate critical genes. In this study, 775 downregulated DEGs were identified. GO analysis demonstrated that the DEGs were enriched in cellular protein modification, regulation of cell communication, and regulation of signaling. KEGG analysis indicated that the DEGs were enriched in the MAPK signaling pathway, endocytosis, oxytocin signaling, and calcium signaling. PPI network and module analysis found 12 hub genes, which were enriched in synaptic vesicle cycling rheumatoid arthritis and collecting duct acid secretion. The four key genes CDK17, GNA13, PHF21A, and MTHFD2 were identified in both generation (GSE4412) and validation (GSE4271) dataset, respectively. Regression analysis showed that CDK13, PHF21A, and MTHFD2 were independent predictors. The results suggested that CDK17, GNA13, PHF21A, and MTHFD2 might play important roles and potentially be valuable in the prognosis and treatment of glioma.http://dx.doi.org/10.1155/2017/1278081 |
| spellingShingle | Mingfa Liu Zhennan Xu Zepeng Du Bingli Wu Tao Jin Ke Xu Liyan Xu Enmin Li Haixiong Xu The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis Journal of Immunology Research |
| title | The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis |
| title_full | The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis |
| title_fullStr | The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis |
| title_full_unstemmed | The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis |
| title_short | The Identification of Key Genes and Pathways in Glioma by Bioinformatics Analysis |
| title_sort | identification of key genes and pathways in glioma by bioinformatics analysis |
| url | http://dx.doi.org/10.1155/2017/1278081 |
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