Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
ObjectiveIncreasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we est...
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| Format: | Article |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full |
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| author | Junsheng Li Junsheng Li Junsheng Li Junsheng Li Junsheng Li Jia Wang Jia Wang Jia Wang Jia Wang Jia Wang Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Chuming Tao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Wen Wang Wen Wang Wen Wang Wen Wang Wen Wang |
| author_facet | Junsheng Li Junsheng Li Junsheng Li Junsheng Li Junsheng Li Jia Wang Jia Wang Jia Wang Jia Wang Jia Wang Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Chuming Tao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Wen Wang Wen Wang Wen Wang Wen Wang Wen Wang |
| author_sort | Junsheng Li |
| collection | DOAJ |
| description | ObjectiveIncreasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.MethodsThe gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.ResultsThe 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).ConclusionThe SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets. |
| first_indexed | 2024-04-12T16:03:51Z |
| format | Article |
| id | doaj.art-e6f3b0a0ba974d41be61fdfa5c421678 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T16:03:51Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-e6f3b0a0ba974d41be61fdfa5c4216782022-12-22T03:26:08ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-10-011310.3389/fimmu.2022.10189421018942Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genesJunsheng Li0Junsheng Li1Junsheng Li2Junsheng Li3Junsheng Li4Jia Wang5Jia Wang6Jia Wang7Jia Wang8Jia Wang9Dongjing Liu10Dongjing Liu11Dongjing Liu12Dongjing Liu13Dongjing Liu14Chuming Tao15Jizong Zhao16Jizong Zhao17Jizong Zhao18Jizong Zhao19Jizong Zhao20Jizong Zhao21Wen Wang22Wen Wang23Wen Wang24Wen Wang25Wen Wang26Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaSavaid Medical School, University of the Chinese Academy of Sciences, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaChina National Clinical Research Center for Neurological Diseases, Beijing, ChinaCenter of Stroke, Beijing Institute for Brain Disorders, Beijing, ChinaBeijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, ChinaBeijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, ChinaObjectiveIncreasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.MethodsThe gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.ResultsThe 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).ConclusionThe SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/fullsenescencelower-grade gliomasignatureprognostic modelbiomarker |
| spellingShingle | Junsheng Li Junsheng Li Junsheng Li Junsheng Li Junsheng Li Jia Wang Jia Wang Jia Wang Jia Wang Jia Wang Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Dongjing Liu Chuming Tao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Jizong Zhao Wen Wang Wen Wang Wen Wang Wen Wang Wen Wang Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes Frontiers in Immunology senescence lower-grade glioma signature prognostic model biomarker |
| title | Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes |
| title_full | Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes |
| title_fullStr | Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes |
| title_full_unstemmed | Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes |
| title_short | Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes |
| title_sort | establishment and validation of a novel prognostic model for lower grade glioma based on senescence related genes |
| topic | senescence lower-grade glioma signature prognostic model biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full |
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