The study of an anoikis-related signature to predict glioma prognosis and immune infiltration
Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method W...
Ausführliche Beschreibung
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| Autor*in: |
Zhang, Dongdong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of cancer research and clinical oncology - Berlin : Springer, 1904, 149(2023), 14 vom: 14. Juli, Seite 12659-12676 |
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| Übergeordnetes Werk: |
volume:149 ; year:2023 ; number:14 ; day:14 ; month:07 ; pages:12659-12676 |
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| DOI / URN: |
10.1007/s00432-023-05138-7 |
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| Katalog-ID: |
SPR053456548 |
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| 100 | 1 | |a Zhang, Dongdong |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The study of an anoikis-related signature to predict glioma prognosis and immune infiltration |
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| 520 | |a Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. | ||
| 650 | 4 | |a Anoikis-related genes |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Glioma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Immune microenvironment |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prognostic model |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Wang, Yu |4 aut | |
| 700 | 1 | |a Zhou, Huandi |4 aut | |
| 700 | 1 | |a Han, Xuetao |4 aut | |
| 700 | 1 | |a Hou, Liubing |4 aut | |
| 700 | 1 | |a Lv, Zhongqiang |4 aut | |
| 700 | 1 | |a Xue, Xiaoying |4 aut | |
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10.1007/s00432-023-05138-7 doi (DE-627)SPR053456548 (SPR)s00432-023-05138-7-e DE-627 ger DE-627 rakwb eng Zhang, Dongdong verfasserin aut The study of an anoikis-related signature to predict glioma prognosis and immune infiltration 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 Wang, Yu aut Zhou, Huandi aut Han, Xuetao aut Hou, Liubing aut Lv, Zhongqiang aut Xue, Xiaoying aut Enthalten in Journal of cancer research and clinical oncology Berlin : Springer, 1904 149(2023), 14 vom: 14. Juli, Seite 12659-12676 (DE-627)253769515 (DE-600)1459285-X 1432-1335 nnns volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 https://dx.doi.org/10.1007/s00432-023-05138-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149 2023 14 14 07 12659-12676 |
| spelling |
10.1007/s00432-023-05138-7 doi (DE-627)SPR053456548 (SPR)s00432-023-05138-7-e DE-627 ger DE-627 rakwb eng Zhang, Dongdong verfasserin aut The study of an anoikis-related signature to predict glioma prognosis and immune infiltration 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 Wang, Yu aut Zhou, Huandi aut Han, Xuetao aut Hou, Liubing aut Lv, Zhongqiang aut Xue, Xiaoying aut Enthalten in Journal of cancer research and clinical oncology Berlin : Springer, 1904 149(2023), 14 vom: 14. Juli, Seite 12659-12676 (DE-627)253769515 (DE-600)1459285-X 1432-1335 nnns volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 https://dx.doi.org/10.1007/s00432-023-05138-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149 2023 14 14 07 12659-12676 |
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10.1007/s00432-023-05138-7 doi (DE-627)SPR053456548 (SPR)s00432-023-05138-7-e DE-627 ger DE-627 rakwb eng Zhang, Dongdong verfasserin aut The study of an anoikis-related signature to predict glioma prognosis and immune infiltration 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 Wang, Yu aut Zhou, Huandi aut Han, Xuetao aut Hou, Liubing aut Lv, Zhongqiang aut Xue, Xiaoying aut Enthalten in Journal of cancer research and clinical oncology Berlin : Springer, 1904 149(2023), 14 vom: 14. Juli, Seite 12659-12676 (DE-627)253769515 (DE-600)1459285-X 1432-1335 nnns volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 https://dx.doi.org/10.1007/s00432-023-05138-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149 2023 14 14 07 12659-12676 |
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10.1007/s00432-023-05138-7 doi (DE-627)SPR053456548 (SPR)s00432-023-05138-7-e DE-627 ger DE-627 rakwb eng Zhang, Dongdong verfasserin aut The study of an anoikis-related signature to predict glioma prognosis and immune infiltration 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 Wang, Yu aut Zhou, Huandi aut Han, Xuetao aut Hou, Liubing aut Lv, Zhongqiang aut Xue, Xiaoying aut Enthalten in Journal of cancer research and clinical oncology Berlin : Springer, 1904 149(2023), 14 vom: 14. Juli, Seite 12659-12676 (DE-627)253769515 (DE-600)1459285-X 1432-1335 nnns volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 https://dx.doi.org/10.1007/s00432-023-05138-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149 2023 14 14 07 12659-12676 |
| allfieldsSound |
10.1007/s00432-023-05138-7 doi (DE-627)SPR053456548 (SPR)s00432-023-05138-7-e DE-627 ger DE-627 rakwb eng Zhang, Dongdong verfasserin aut The study of an anoikis-related signature to predict glioma prognosis and immune infiltration 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 Wang, Yu aut Zhou, Huandi aut Han, Xuetao aut Hou, Liubing aut Lv, Zhongqiang aut Xue, Xiaoying aut Enthalten in Journal of cancer research and clinical oncology Berlin : Springer, 1904 149(2023), 14 vom: 14. Juli, Seite 12659-12676 (DE-627)253769515 (DE-600)1459285-X 1432-1335 nnns volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 https://dx.doi.org/10.1007/s00432-023-05138-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149 2023 14 14 07 12659-12676 |
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Enthalten in Journal of cancer research and clinical oncology 149(2023), 14 vom: 14. Juli, Seite 12659-12676 volume:149 year:2023 number:14 day:14 month:07 pages:12659-12676 |
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Zhang, Dongdong @@aut@@ Wang, Yu @@aut@@ Zhou, Huandi @@aut@@ Han, Xuetao @@aut@@ Hou, Liubing @@aut@@ Lv, Zhongqiang @@aut@@ Xue, Xiaoying @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053456548</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231020064702.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231020s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00432-023-05138-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053456548</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00432-023-05138-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Dongdong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The study of an anoikis-related signature to predict glioma prognosis and immune infiltration</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Anoikis-related genes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Glioma</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Immune microenvironment</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prognostic model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Immune checkpoint</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Huandi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Xuetao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hou, Liubing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lv, Zhongqiang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xue, Xiaoying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of cancer research and clinical oncology</subfield><subfield code="d">Berlin : Springer, 1904</subfield><subfield code="g">149(2023), 14 vom: 14. 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Zhang, Dongdong |
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Zhang, Dongdong misc Anoikis-related genes misc Glioma misc Immune microenvironment misc Prognostic model misc Immune checkpoint The study of an anoikis-related signature to predict glioma prognosis and immune infiltration |
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The study of an anoikis-related signature to predict glioma prognosis and immune infiltration Anoikis-related genes (dpeaa)DE-He213 Glioma (dpeaa)DE-He213 Immune microenvironment (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Immune checkpoint (dpeaa)DE-He213 |
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Journal of cancer research and clinical oncology |
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Zhang, Dongdong Wang, Yu Zhou, Huandi Han, Xuetao Hou, Liubing Lv, Zhongqiang Xue, Xiaoying |
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study of an anoikis-related signature to predict glioma prognosis and immune infiltration |
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The study of an anoikis-related signature to predict glioma prognosis and immune infiltration |
| abstract |
Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Background Gliomas are the most common highly aggressive primary malignant brain tumors in adults with different biological behaviors and clinically heterogeneous features. About the extremely poor prognosis of gliomas, the search for potential therapeutic modalities and targets is crucial. Method We extracted the anoikis-related genes (ARG) from GeneCards and obtained differentially expressed genes in normal and glioma tissues from the GSE4290 dataset to obtain intersect differentially expressed ARG in gliomas by differential analysis. KEGG and GO analyses were used to evaluate the potential pathways and molecular processes of these genes. Based on The Cancer Genome Atlas (TCGA) training cohort, we performed the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression to construct an ARG prognostic model and validated them in the TCGA testing cohort and the Chinese Glioma Genome Atlas (CGGA) validation cohort. Subsequently, we further explored the differences in clinical characteristics, tumor mutation burden (TMB), and the immune microenvironment in the high- and low-risk groups. Univariate and multifactorial regression analyses and nomogram construction were also performed. Moreover, we evaluated the expression levels of key genes via public databases, qPCR analysis and IHC staining, and further assessed the clinical prognostic value. Results The regulatory model based on quantitative ARG prognostic models showed that patients in the high-risk group were associated with poorer survival prognosis, poorer clinical characteristics, and higher TMB levels. Moreover, the high-risk group had high levels of immune infiltration and upregulated immune checkpoint gene expression. The ARG prognostic model and the Nomogram showed good predictive performance. Expression and survival analysis of five prognostic ARG signatures (ETV4, HMOX1, MYC, NFE2L2, and UBE2C) showed that these genes have potential prognostic value. Conclusion Our constructed ARG prognostic risk model provides a potential therapeutic target and theoretical basis for predicting the prognosis of glioma patients and guiding individualized immunotherapy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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The study of an anoikis-related signature to predict glioma prognosis and immune infiltration |
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https://dx.doi.org/10.1007/s00432-023-05138-7 |
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Wang, Yu Zhou, Huandi Han, Xuetao Hou, Liubing Lv, Zhongqiang Xue, Xiaoying |
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| score |
7.4010878 |