The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma
BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), charac...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1220100/full |
| _version_ | 1828717295965306880 |
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| author | Xu Han Zihan Yan Kaiyu Fan Xueyi Guan Bohan Hu Xiang Li Yunwei Ou Bing Cui Lingxuan An Yaohua Zhang Jian Gong Jian Gong |
| author_facet | Xu Han Zihan Yan Kaiyu Fan Xueyi Guan Bohan Hu Xiang Li Yunwei Ou Bing Cui Lingxuan An Yaohua Zhang Jian Gong Jian Gong |
| author_sort | Xu Han |
| collection | DOAJ |
| description | BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG. |
| first_indexed | 2024-03-12T14:26:05Z |
| format | Article |
| id | doaj.art-341744c8d7434d99a57cce1ff7c1c087 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-12T14:26:05Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-341744c8d7434d99a57cce1ff7c1c0872023-08-18T06:59:27ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-08-011410.3389/fimmu.2023.12201001220100The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in gliomaXu Han0Zihan Yan1Kaiyu Fan2Xueyi Guan3Bohan Hu4Xiang Li5Yunwei Ou6Bing Cui7Lingxuan An8Yaohua Zhang9Jian Gong10Jian Gong11Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, ChinaDepartment of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, GermanyBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaBeijing Neurosurgical Institute, Capital Medical University, Beijing, ChinaBackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1220100/fulltelomere related genesgliomatumor microenvironmentprognosisimmune response |
| spellingShingle | Xu Han Zihan Yan Kaiyu Fan Xueyi Guan Bohan Hu Xiang Li Yunwei Ou Bing Cui Lingxuan An Yaohua Zhang Jian Gong Jian Gong The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma Frontiers in Immunology telomere related genes glioma tumor microenvironment prognosis immune response |
| title | The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| title_full | The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| title_fullStr | The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| title_full_unstemmed | The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| title_short | The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| title_sort | combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma |
| topic | telomere related genes glioma tumor microenvironment prognosis immune response |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1220100/full |
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