Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration
Objective: To investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients’ prognosis. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023000452 |
| _version_ | 1811161881547636736 |
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| author | Qisheng Luo Zhenxiu Yang Renzhi Deng Xianhui Pang Xu Han Xinfu Liu Jiahai Du Yingzhao Tian Jingzhan Wu Chunhai Tang |
| author_facet | Qisheng Luo Zhenxiu Yang Renzhi Deng Xianhui Pang Xu Han Xinfu Liu Jiahai Du Yingzhao Tian Jingzhan Wu Chunhai Tang |
| author_sort | Qisheng Luo |
| collection | DOAJ |
| description | Objective: To investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients’ prognosis. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We performed Pearson correlation, univariate Cox regression to screen m6A-related prognostic lncRNA. GMB patients’ samples were separated into different clusters through the ConsensusClusterPlus package. The risk score model was established through LASSO regression analysis. Besides, KEGG pathway enrichment analysis was implemented. CIBERSORT algorithm was used to analyze the difference of 22 types of immune cell infiltration in different cluster of GBM patient. Cox regression analyses were used to verify the independence of the model and correlation analysis was performed to demonstrate the link between our model and clinical characteristics of GBM patients. Experiments were used to validate the differential expression of the model lncRNA in patients with different prognosis. Results: 17 lncRNA related to prognosis were screened from 1021 m6A-related lncRNAs. Further, four m6A-related lncRNAs that were significantly correlated with GBM prognosis were selected to establish our prognostic risk model, which had excellent accuracy and can independently predict the prognosis of GBM patients. The infiltration fractions of T regulatory cells, T cells CD4 memory activated and neutrophils were positively associated with risk score, which suggested a significant relationship between the model and tumor immune microenvironment. Conclusion: The m6A-related RNA risk model offered potential for identifying biomarkers of therapy and predicting prognosis of GBM patients. |
| first_indexed | 2024-04-10T06:22:15Z |
| format | Article |
| id | doaj.art-eda72c0b7c9a439b9893887261437c12 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:22:15Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-eda72c0b7c9a439b9893887261437c122023-03-02T04:59:48ZengElsevierHeliyon2405-84402023-02-0192e12838Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltrationQisheng Luo0Zhenxiu Yang1Renzhi Deng2Xianhui Pang3Xu Han4Xinfu Liu5Jiahai Du6Yingzhao Tian7Jingzhan Wu8Chunhai Tang9Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, BaiSe,Guangxi province, 533000, ChinaDepartment of Oncology, The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, ChinaDepartment of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, China; Corresponding author.Department of Neurosurgery(Trauma Surgery), The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, China; Corresponding author.Objective: To investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients’ prognosis. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We performed Pearson correlation, univariate Cox regression to screen m6A-related prognostic lncRNA. GMB patients’ samples were separated into different clusters through the ConsensusClusterPlus package. The risk score model was established through LASSO regression analysis. Besides, KEGG pathway enrichment analysis was implemented. CIBERSORT algorithm was used to analyze the difference of 22 types of immune cell infiltration in different cluster of GBM patient. Cox regression analyses were used to verify the independence of the model and correlation analysis was performed to demonstrate the link between our model and clinical characteristics of GBM patients. Experiments were used to validate the differential expression of the model lncRNA in patients with different prognosis. Results: 17 lncRNA related to prognosis were screened from 1021 m6A-related lncRNAs. Further, four m6A-related lncRNAs that were significantly correlated with GBM prognosis were selected to establish our prognostic risk model, which had excellent accuracy and can independently predict the prognosis of GBM patients. The infiltration fractions of T regulatory cells, T cells CD4 memory activated and neutrophils were positively associated with risk score, which suggested a significant relationship between the model and tumor immune microenvironment. Conclusion: The m6A-related RNA risk model offered potential for identifying biomarkers of therapy and predicting prognosis of GBM patients.http://www.sciencedirect.com/science/article/pii/S2405844023000452Glioblastoma multiformem6a methylation modificationlncRNAPrognosisImmune cell infiltration |
| spellingShingle | Qisheng Luo Zhenxiu Yang Renzhi Deng Xianhui Pang Xu Han Xinfu Liu Jiahai Du Yingzhao Tian Jingzhan Wu Chunhai Tang Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration Heliyon Glioblastoma multiforme m6a methylation modification lncRNA Prognosis Immune cell infiltration |
| title | Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration |
| title_full | Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration |
| title_fullStr | Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration |
| title_full_unstemmed | Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration |
| title_short | Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration |
| title_sort | comprehensive analysis of prognosis of patients with gbm based on 4 m6a related lncrnas and immune cell infiltration |
| topic | Glioblastoma multiforme m6a methylation modification lncRNA Prognosis Immune cell infiltration |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023000452 |
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