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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Autor*in:	Junsheng Li [verfasserIn] Jia Wang [verfasserIn] Dongjing Liu [verfasserIn] Chuming Tao [verfasserIn] Jizong Zhao [verfasserIn] Wen Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	senescence lower-grade glioma signature prognostic model biomarker

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2022.1018942

Katalog-ID:	DOAJ021972877

Internformat


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245	1	0	\|a Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
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520			\|a 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.
650		4	\|a senescence
650		4	\|a lower-grade glioma
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650		4	\|a prognostic model
650		4	\|a biomarker
653		0	\|a Immunologic diseases. Allergy
700	0		\|a Junsheng Li \|e verfasserin \|4 aut
700	0		\|a Junsheng Li \|e verfasserin \|4 aut
700	0		\|a Junsheng Li \|e verfasserin \|4 aut
700	0		\|a Junsheng Li \|e verfasserin \|4 aut
700	0		\|a Jia Wang \|e verfasserin \|4 aut
700	0		\|a Jia Wang \|e verfasserin \|4 aut
700	0		\|a Jia Wang \|e verfasserin \|4 aut
700	0		\|a Jia Wang \|e verfasserin \|4 aut
700	0		\|a Jia Wang \|e verfasserin \|4 aut
700	0		\|a Dongjing Liu \|e verfasserin \|4 aut
700	0		\|a Dongjing Liu \|e verfasserin \|4 aut
700	0		\|a Dongjing Liu \|e verfasserin \|4 aut
700	0		\|a Dongjing Liu \|e verfasserin \|4 aut
700	0		\|a Dongjing Liu \|e verfasserin \|4 aut
700	0		\|a Chuming Tao \|e verfasserin \|4 aut
700	0		\|a Jizong Zhao \|e verfasserin \|4 aut
700	0		\|a Jizong Zhao \|e verfasserin \|4 aut
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700	0		\|a Wen Wang \|e verfasserin \|4 aut
700	0		\|a Wen Wang \|e verfasserin \|4 aut
700	0		\|a Wen Wang \|e verfasserin \|4 aut
700	0		\|a Wen Wang \|e verfasserin \|4 aut
700	0		\|a Wen Wang \|e verfasserin \|4 aut
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952			\|d 13 \|j 2022

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allfields	10.3389/fimmu.2022.1018942 doi (DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678 DE-627 ger DE-627 rakwb eng RC581-607 Junsheng Li verfasserin aut Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Chuming Tao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.1018942 kostenfrei https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
spelling	10.3389/fimmu.2022.1018942 doi (DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678 DE-627 ger DE-627 rakwb eng RC581-607 Junsheng Li verfasserin aut Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Chuming Tao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.1018942 kostenfrei https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfields_unstemmed	10.3389/fimmu.2022.1018942 doi (DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678 DE-627 ger DE-627 rakwb eng RC581-607 Junsheng Li verfasserin aut Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Chuming Tao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.1018942 kostenfrei https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsGer	10.3389/fimmu.2022.1018942 doi (DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678 DE-627 ger DE-627 rakwb eng RC581-607 Junsheng Li verfasserin aut Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Chuming Tao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.1018942 kostenfrei https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsSound	10.3389/fimmu.2022.1018942 doi (DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678 DE-627 ger DE-627 rakwb eng RC581-607 Junsheng Li verfasserin aut Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Junsheng Li verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Jia Wang verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Dongjing Liu verfasserin aut Chuming Tao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Jizong Zhao verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut Wen Wang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.1018942 kostenfrei https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
language	English
source	In Frontiers in Immunology 13(2022) volume:13 year:2022
sourceStr	In Frontiers in Immunology 13(2022) volume:13 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	senescence lower-grade glioma signature prognostic model biomarker Immunologic diseases. Allergy
isfreeaccess_bool	true
container_title	Frontiers in Immunology
authorswithroles_txt_mv	Junsheng Li @@aut@@ Jia Wang @@aut@@ Dongjing Liu @@aut@@ Chuming Tao @@aut@@ Jizong Zhao @@aut@@ Wen Wang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	657998354
id	DOAJ021972877
language_de	englisch
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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&lt;0.001). The high SnG-risk score indicated a worse outcome in LGG (P&lt;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&lt;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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">senescence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">lower-grade glioma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">signature</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognostic model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">biomarker</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Immunologic diseases. 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callnumber-first	R - Medicine
author	Junsheng Li
spellingShingle	Junsheng Li misc RC581-607 misc senescence misc lower-grade glioma misc signature misc prognostic model misc biomarker misc Immunologic diseases. Allergy Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
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topic_title	RC581-607 Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes senescence lower-grade glioma signature prognostic model biomarker
topic	misc RC581-607 misc senescence misc lower-grade glioma misc signature misc prognostic model misc biomarker misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc senescence misc lower-grade glioma misc signature misc prognostic model misc biomarker misc Immunologic diseases. Allergy
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title	Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
ctrlnum	(DE-627)DOAJ021972877 (DE-599)DOAJe6f3b0a0ba974d41be61fdfa5c421678
title_full	Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
author_sort	Junsheng Li
journal	Frontiers in Immunology
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author_browse	Junsheng Li Jia Wang Dongjing Liu Chuming Tao Jizong Zhao Wen Wang
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title_sort	establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
callnumber	RC581-607
title_auth	Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes
url	https://doi.org/10.3389/fimmu.2022.1018942 https://doaj.org/article/e6f3b0a0ba974d41be61fdfa5c421678 https://www.frontiersin.org/articles/10.3389/fimmu.2022.1018942/full https://doaj.org/toc/1664-3224
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author2	Junsheng Li Jia Wang Dongjing Liu Chuming Tao Jizong Zhao Wen Wang
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