DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-...
Ausführliche Beschreibung
Autor*in: |
Xu, Duoduo [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Anmerkung: |
© The Author(s) 2022 |
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Übergeordnetes Werk: |
Enthalten in: BMC pulmonary medicine - London : BioMed Central, 2001, 22(2022), 1 vom: 07. Apr. |
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Übergeordnetes Werk: |
volume:22 ; year:2022 ; number:1 ; day:07 ; month:04 |
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DOI / URN: |
10.1186/s12890-022-01924-0 |
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Katalog-ID: |
SPR050625691 |
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520 | |a Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. | ||
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10.1186/s12890-022-01924-0 doi (DE-627)SPR050625691 (SPR)s12890-022-01924-0-e DE-627 ger DE-627 rakwb eng Xu, Duoduo verfasserin aut DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Li, Cheng aut Zhang, Youjing aut Zhang, Jizhou aut Enthalten in BMC pulmonary medicine London : BioMed Central, 2001 22(2022), 1 vom: 07. Apr. (DE-627)335489125 (DE-600)2059871-3 1471-2466 nnns volume:22 year:2022 number:1 day:07 month:04 https://dx.doi.org/10.1186/s12890-022-01924-0 kostenfrei 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_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 22 2022 1 07 04 |
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10.1186/s12890-022-01924-0 doi (DE-627)SPR050625691 (SPR)s12890-022-01924-0-e DE-627 ger DE-627 rakwb eng Xu, Duoduo verfasserin aut DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Li, Cheng aut Zhang, Youjing aut Zhang, Jizhou aut Enthalten in BMC pulmonary medicine London : BioMed Central, 2001 22(2022), 1 vom: 07. Apr. (DE-627)335489125 (DE-600)2059871-3 1471-2466 nnns volume:22 year:2022 number:1 day:07 month:04 https://dx.doi.org/10.1186/s12890-022-01924-0 kostenfrei 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_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 22 2022 1 07 04 |
allfields_unstemmed |
10.1186/s12890-022-01924-0 doi (DE-627)SPR050625691 (SPR)s12890-022-01924-0-e DE-627 ger DE-627 rakwb eng Xu, Duoduo verfasserin aut DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Li, Cheng aut Zhang, Youjing aut Zhang, Jizhou aut Enthalten in BMC pulmonary medicine London : BioMed Central, 2001 22(2022), 1 vom: 07. Apr. (DE-627)335489125 (DE-600)2059871-3 1471-2466 nnns volume:22 year:2022 number:1 day:07 month:04 https://dx.doi.org/10.1186/s12890-022-01924-0 kostenfrei 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_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 22 2022 1 07 04 |
allfieldsGer |
10.1186/s12890-022-01924-0 doi (DE-627)SPR050625691 (SPR)s12890-022-01924-0-e DE-627 ger DE-627 rakwb eng Xu, Duoduo verfasserin aut DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Li, Cheng aut Zhang, Youjing aut Zhang, Jizhou aut Enthalten in BMC pulmonary medicine London : BioMed Central, 2001 22(2022), 1 vom: 07. Apr. (DE-627)335489125 (DE-600)2059871-3 1471-2466 nnns volume:22 year:2022 number:1 day:07 month:04 https://dx.doi.org/10.1186/s12890-022-01924-0 kostenfrei 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_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 22 2022 1 07 04 |
allfieldsSound |
10.1186/s12890-022-01924-0 doi (DE-627)SPR050625691 (SPR)s12890-022-01924-0-e DE-627 ger DE-627 rakwb eng Xu, Duoduo verfasserin aut DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Li, Cheng aut Zhang, Youjing aut Zhang, Jizhou aut Enthalten in BMC pulmonary medicine London : BioMed Central, 2001 22(2022), 1 vom: 07. Apr. (DE-627)335489125 (DE-600)2059871-3 1471-2466 nnns volume:22 year:2022 number:1 day:07 month:04 https://dx.doi.org/10.1186/s12890-022-01924-0 kostenfrei 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_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 22 2022 1 07 04 |
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Enthalten in BMC pulmonary medicine 22(2022), 1 vom: 07. Apr. volume:22 year:2022 number:1 day:07 month:04 |
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DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma LUAD (dpeaa)DE-He213 DNA methylation (dpeaa)DE-He213 TCGA (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 |
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DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma |
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DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma |
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dna methylation molecular subtypes for prognosis prediction in lung adenocarcinoma |
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DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma |
abstract |
Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. © The Author(s) 2022 |
abstractGer |
Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. © The Author(s) 2022 |
abstract_unstemmed |
Aims Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. Methods In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation. Results Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. Conclusion The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. © The Author(s) 2022 |
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DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma |
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