Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods
Glioma peritumoral brain edema (GPTBE) is a frequent complication in patients with glioma. The severity of peritumoral edema endangers patients’ life and prognosis. However, there are still questions concerning the process of GPTBE formation and evolution. In this study, the patients were split into...
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MDPI AG
2022-06-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/12/6/805 |
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| author | Yuxi Wu Zesheng Peng Haofei Wang Wei Xiang |
| author_facet | Yuxi Wu Zesheng Peng Haofei Wang Wei Xiang |
| author_sort | Yuxi Wu |
| collection | DOAJ |
| description | Glioma peritumoral brain edema (GPTBE) is a frequent complication in patients with glioma. The severity of peritumoral edema endangers patients’ life and prognosis. However, there are still questions concerning the process of GPTBE formation and evolution. In this study, the patients were split into two groups based on edema scoring findings in the cancer imaging archive (TCIA) comprising 186 TCGA-LGG patients. Using mRNA sequencing data, differential gene (DEG) expression analysis was performed, comparing the two groups to find the key genes affecting GPTBE. A functional enrichment analysis of differentially expressed genes was performed. Then, a protein–protein interaction (PPI) network was established, and important genes were screened. Gene set variation analysis (GSVA) scores were calculated for major gene sets and comparatively correlated with immune cell infiltration. Overall survival (OS) was analyzed using the Kaplan–Meier curve. A total of 59 DEGs were found, with 10 of them appearing as important genes. DEGs were shown to be closely linked to inflammatory reactions. According to the network score, IL10 was in the middle of the network. The presence of the IL10 protein in glioma tissues was verified using the human protein atlas (HPA). Furthermore, the gene sets’ GSVA scores were favorably linked with immune infiltration, particularly, with macrophages. The high-edema group had higher GSVA scores than the low-edema group. Finally, Kaplan–Meier analysis revealed no differences in OS between the two groups, and eight genes were found to be related to prognosis, whereas two genes were not. GPTBE is linked to the expression of inflammatory genes. |
| first_indexed | 2024-03-10T00:15:59Z |
| format | Article |
| id | doaj.art-6d3599662a244975b20013ace39502cd |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T00:15:59Z |
| publishDate | 2022-06-01 |
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| series | Brain Sciences |
| spelling | doaj.art-6d3599662a244975b20013ace39502cd2023-11-23T15:51:43ZengMDPI AGBrain Sciences2076-34252022-06-0112680510.3390/brainsci12060805Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical MethodsYuxi Wu0Zesheng Peng1Haofei Wang2Wei Xiang3Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, ChinaDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, ChinaGlioma peritumoral brain edema (GPTBE) is a frequent complication in patients with glioma. The severity of peritumoral edema endangers patients’ life and prognosis. However, there are still questions concerning the process of GPTBE formation and evolution. In this study, the patients were split into two groups based on edema scoring findings in the cancer imaging archive (TCIA) comprising 186 TCGA-LGG patients. Using mRNA sequencing data, differential gene (DEG) expression analysis was performed, comparing the two groups to find the key genes affecting GPTBE. A functional enrichment analysis of differentially expressed genes was performed. Then, a protein–protein interaction (PPI) network was established, and important genes were screened. Gene set variation analysis (GSVA) scores were calculated for major gene sets and comparatively correlated with immune cell infiltration. Overall survival (OS) was analyzed using the Kaplan–Meier curve. A total of 59 DEGs were found, with 10 of them appearing as important genes. DEGs were shown to be closely linked to inflammatory reactions. According to the network score, IL10 was in the middle of the network. The presence of the IL10 protein in glioma tissues was verified using the human protein atlas (HPA). Furthermore, the gene sets’ GSVA scores were favorably linked with immune infiltration, particularly, with macrophages. The high-edema group had higher GSVA scores than the low-edema group. Finally, Kaplan–Meier analysis revealed no differences in OS between the two groups, and eight genes were found to be related to prognosis, whereas two genes were not. GPTBE is linked to the expression of inflammatory genes.https://www.mdpi.com/2076-3425/12/6/805gliomaperitumoral edemainflammatorygenedata integration analysis |
| spellingShingle | Yuxi Wu Zesheng Peng Haofei Wang Wei Xiang Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods Brain Sciences glioma peritumoral edema inflammatory gene data integration analysis |
| title | Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods |
| title_full | Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods |
| title_fullStr | Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods |
| title_full_unstemmed | Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods |
| title_short | Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods |
| title_sort | identifying the hub genes of glioma peritumoral brain edema using bioinformatical methods |
| topic | glioma peritumoral edema inflammatory gene data integration analysis |
| url | https://www.mdpi.com/2076-3425/12/6/805 |
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