DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns
Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Ling-Hu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© 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: Functional & integrative genomics - Berlin : Springer, 2000, 23(2023), 4 vom: 09. Sept. |
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| Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:4 ; day:09 ; month:09 |
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| DOI / URN: |
10.1007/s10142-023-01222-w |
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SPR053032527 |
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| 520 | |a Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. | ||
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| 650 | 4 | |a Radiation biomarkers |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Trauma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Burn |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mass casualty radiation exposure |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Chen, Xiang-Yu |4 aut | |
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| 700 | 1 | |a Liu, Zhen |4 aut | |
| 700 | 1 | |a Li, Yue |4 aut | |
| 700 | 1 | |a Li, Tian |4 aut | |
| 700 | 1 | |a Liu, Ming-Hua |4 aut | |
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10.1007/s10142-023-01222-w doi (DE-627)SPR053032527 (SPR)s10142-023-01222-w-e DE-627 ger DE-627 rakwb eng Cai, Ling-Hu verfasserin aut DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns 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. Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 Chen, Xiang-Yu aut Qian, Wei aut Liu, Chuan-Chuan aut Yuan, Li-Jia aut Zhang, Liang aut Nie, Chao aut Liu, Zhen aut Li, Yue aut Li, Tian aut Liu, Ming-Hua aut Enthalten in Functional & integrative genomics Berlin : Springer, 2000 23(2023), 4 vom: 09. Sept. (DE-627)312404492 (DE-600)2010402-9 1438-7948 nnns volume:23 year:2023 number:4 day:09 month:09 https://dx.doi.org/10.1007/s10142-023-01222-w 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_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 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_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_2548 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 23 2023 4 09 09 |
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10.1007/s10142-023-01222-w doi (DE-627)SPR053032527 (SPR)s10142-023-01222-w-e DE-627 ger DE-627 rakwb eng Cai, Ling-Hu verfasserin aut DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns 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. Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 Chen, Xiang-Yu aut Qian, Wei aut Liu, Chuan-Chuan aut Yuan, Li-Jia aut Zhang, Liang aut Nie, Chao aut Liu, Zhen aut Li, Yue aut Li, Tian aut Liu, Ming-Hua aut Enthalten in Functional & integrative genomics Berlin : Springer, 2000 23(2023), 4 vom: 09. Sept. (DE-627)312404492 (DE-600)2010402-9 1438-7948 nnns volume:23 year:2023 number:4 day:09 month:09 https://dx.doi.org/10.1007/s10142-023-01222-w 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_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 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_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_2548 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 23 2023 4 09 09 |
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10.1007/s10142-023-01222-w doi (DE-627)SPR053032527 (SPR)s10142-023-01222-w-e DE-627 ger DE-627 rakwb eng Cai, Ling-Hu verfasserin aut DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns 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. Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 Chen, Xiang-Yu aut Qian, Wei aut Liu, Chuan-Chuan aut Yuan, Li-Jia aut Zhang, Liang aut Nie, Chao aut Liu, Zhen aut Li, Yue aut Li, Tian aut Liu, Ming-Hua aut Enthalten in Functional & integrative genomics Berlin : Springer, 2000 23(2023), 4 vom: 09. Sept. (DE-627)312404492 (DE-600)2010402-9 1438-7948 nnns volume:23 year:2023 number:4 day:09 month:09 https://dx.doi.org/10.1007/s10142-023-01222-w 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_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 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_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_2548 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 23 2023 4 09 09 |
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10.1007/s10142-023-01222-w doi (DE-627)SPR053032527 (SPR)s10142-023-01222-w-e DE-627 ger DE-627 rakwb eng Cai, Ling-Hu verfasserin aut DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns 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. Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 Chen, Xiang-Yu aut Qian, Wei aut Liu, Chuan-Chuan aut Yuan, Li-Jia aut Zhang, Liang aut Nie, Chao aut Liu, Zhen aut Li, Yue aut Li, Tian aut Liu, Ming-Hua aut Enthalten in Functional & integrative genomics Berlin : Springer, 2000 23(2023), 4 vom: 09. Sept. (DE-627)312404492 (DE-600)2010402-9 1438-7948 nnns volume:23 year:2023 number:4 day:09 month:09 https://dx.doi.org/10.1007/s10142-023-01222-w 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_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 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_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_2548 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 23 2023 4 09 09 |
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10.1007/s10142-023-01222-w doi (DE-627)SPR053032527 (SPR)s10142-023-01222-w-e DE-627 ger DE-627 rakwb eng Cai, Ling-Hu verfasserin aut DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns 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. Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 Chen, Xiang-Yu aut Qian, Wei aut Liu, Chuan-Chuan aut Yuan, Li-Jia aut Zhang, Liang aut Nie, Chao aut Liu, Zhen aut Li, Yue aut Li, Tian aut Liu, Ming-Hua aut Enthalten in Functional & integrative genomics Berlin : Springer, 2000 23(2023), 4 vom: 09. Sept. (DE-627)312404492 (DE-600)2010402-9 1438-7948 nnns volume:23 year:2023 number:4 day:09 month:09 https://dx.doi.org/10.1007/s10142-023-01222-w 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_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 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_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_2548 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 23 2023 4 09 09 |
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Enthalten in Functional & integrative genomics 23(2023), 4 vom: 09. Sept. volume:23 year:2023 number:4 day:09 month:09 |
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Cai, Ling-Hu @@aut@@ Chen, Xiang-Yu @@aut@@ Qian, Wei @@aut@@ Liu, Chuan-Chuan @@aut@@ Yuan, Li-Jia @@aut@@ Zhang, Liang @@aut@@ Nie, Chao @@aut@@ Liu, Zhen @@aut@@ Li, Yue @@aut@@ Li, Tian @@aut@@ Liu, Ming-Hua @@aut@@ |
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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">Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. 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Cai, Ling-Hu |
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Cai, Ling-Hu misc Biodosimetry misc Radiation biomarkers misc Radiation diagnosis misc Trauma misc Burn misc Mass casualty radiation exposure DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns |
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DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns Biodosimetry (dpeaa)DE-He213 Radiation biomarkers (dpeaa)DE-He213 Radiation diagnosis (dpeaa)DE-He213 Trauma (dpeaa)DE-He213 Burn (dpeaa)DE-He213 Mass casualty radiation exposure (dpeaa)DE-He213 |
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10.1007/s10142-023-01222-w |
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ddb2 and mdm2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns |
| title_auth |
DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns |
| abstract |
Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. © 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 |
Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. © 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 |
Abstract In the field of biodosimetry, the current accepted method for evaluating radiation dose fails to meet the need of rapid, large-scale screening, and most RNA marker–related studies of biodosimetry are concentrating on a single type of ray, while some other potential factors, such as trauma and burns, have not been covered. Microarray datasets that contain the data of human peripheral blood samples exposed to X-ray, neutron, and γ-ray radiation were obtained from the GEO database. Totally, 33 multi-type ray co-induced genes were obtained at first from the differentially expressed genes (DEGs) and key genes identified by weighted gene co-expression network analysis (WGCNA), and these genes were mainly enriched in DNA damage, cellular apoptosis, and p53 signaling pathway. Following transcriptome sequencing of blood samples from 11 healthy volunteers, 13 patients with severe burns, and 37 patients with severe trauma, 6635 trauma-related DEGs and 7703 burn-related DEGs were obtained. Through the exclusion method, a total of 12 radiation-specific genes independent of trauma and burns were identified. ROC curve analysis revealed that the DDB2 gene performed the best in diagnosis of all three types of ray radiation, while correlation analysis showed that the MDM2 gene was the best in assessment of radiation dose. The results of multiple-linear regression analysis indicated that such analysis could improve the accuracy in assessment of radiation dose. Moreover, the DDB2 and MDM2 genes remained effective in radiation diagnosis and assessment of radiation dose in an external dataset. In general, the study brings new insights into radiation biodosimetry. © 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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DDB2 and MDM2 genes are promising markers for radiation diagnosis and estimation of radiation dose independent of trauma and burns |
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https://dx.doi.org/10.1007/s10142-023-01222-w |
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Chen, Xiang-Yu Qian, Wei Liu, Chuan-Chuan Yuan, Li-Jia Zhang, Liang Nie, Chao Liu, Zhen Li, Yue Li, Tian Liu, Ming-Hua |
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Chen, Xiang-Yu Qian, Wei Liu, Chuan-Chuan Yuan, Li-Jia Zhang, Liang Nie, Chao Liu, Zhen Li, Yue Li, Tian Liu, Ming-Hua |
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| score |
7.3998566 |