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

Gespeichert in:

Autor*in:	Xu, Duoduo [verfasserIn] Li, Cheng Zhang, Youjing Zhang, Jizhou

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	LUAD DNA methylation TCGA Prognosis

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: BMC pulmonary medicine - London : BioMed Central, 2001, 22(2022), 1 vom: 07. Apr.
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:1 ; day:07 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s12890-022-01924-0

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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allfields	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
spelling	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
language	English
source	Enthalten in BMC pulmonary medicine 22(2022), 1 vom: 07. Apr. volume:22 year:2022 number:1 day:07 month:04
sourceStr	Enthalten in BMC pulmonary medicine 22(2022), 1 vom: 07. Apr. volume:22 year:2022 number:1 day:07 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	LUAD DNA methylation TCGA Prognosis
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authorswithroles_txt_mv	Xu, Duoduo @@aut@@ Li, Cheng @@aut@@ Zhang, Youjing @@aut@@ Zhang, Jizhou @@aut@@
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author	Xu, Duoduo
spellingShingle	Xu, Duoduo misc LUAD misc DNA methylation misc TCGA misc Prognosis DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
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topic_title	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
topic	misc LUAD misc DNA methylation misc TCGA misc Prognosis
topic_unstemmed	misc LUAD misc DNA methylation misc TCGA misc Prognosis
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title	DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
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title_full	DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
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author_browse	Xu, Duoduo Li, Cheng Zhang, Youjing Zhang, Jizhou
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author-letter	Xu, Duoduo
doi_str_mv	10.1186/s12890-022-01924-0
title_sort	dna methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
title_auth	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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title_short	DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma
url	https://dx.doi.org/10.1186/s12890-022-01924-0
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author2	Li, Cheng Zhang, Youjing Zhang, Jizhou
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up_date	2024-07-03T16:44:27.385Z
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