LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features

Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their base...
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Autor*in:	Jiaxian Yu [verfasserIn] Yueming Hu [verfasserIn] Yafei Xu [verfasserIn] Jue Wang [verfasserIn] Jiajie Kuang [verfasserIn] Wei Zhang [verfasserIn] Jianlin Shao [verfasserIn] Dianjing Guo [verfasserIn] Yejun Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning

Übergeordnetes Werk:	In: BMC Cancer - BMC, 2003, 19(2019), 1, Seite 10
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12885-019-5433-7

Katalog-ID:	DOAJ074722514

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520			\|a Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
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allfields	10.1186/s12885-019-5433-7 doi (DE-627)DOAJ074722514 (DE-599)DOAJ52d80d7b41d846f0822380258f61847e DE-627 ger DE-627 rakwb eng RC254-282 Jiaxian Yu verfasserin aut LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yueming Hu verfasserin aut Yafei Xu verfasserin aut Jue Wang verfasserin aut Jiajie Kuang verfasserin aut Wei Zhang verfasserin aut Jianlin Shao verfasserin aut Dianjing Guo verfasserin aut Yejun Wang verfasserin aut In BMC Cancer BMC, 2003 19(2019), 1, Seite 10 (DE-627)326643710 (DE-600)2041352-X 14712407 nnns volume:19 year:2019 number:1 pages:10 https://doi.org/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/article/52d80d7b41d846f0822380258f61847e kostenfrei http://link.springer.com/article/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/toc/1471-2407 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 10
spelling	10.1186/s12885-019-5433-7 doi (DE-627)DOAJ074722514 (DE-599)DOAJ52d80d7b41d846f0822380258f61847e DE-627 ger DE-627 rakwb eng RC254-282 Jiaxian Yu verfasserin aut LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yueming Hu verfasserin aut Yafei Xu verfasserin aut Jue Wang verfasserin aut Jiajie Kuang verfasserin aut Wei Zhang verfasserin aut Jianlin Shao verfasserin aut Dianjing Guo verfasserin aut Yejun Wang verfasserin aut In BMC Cancer BMC, 2003 19(2019), 1, Seite 10 (DE-627)326643710 (DE-600)2041352-X 14712407 nnns volume:19 year:2019 number:1 pages:10 https://doi.org/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/article/52d80d7b41d846f0822380258f61847e kostenfrei http://link.springer.com/article/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/toc/1471-2407 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 10
allfields_unstemmed	10.1186/s12885-019-5433-7 doi (DE-627)DOAJ074722514 (DE-599)DOAJ52d80d7b41d846f0822380258f61847e DE-627 ger DE-627 rakwb eng RC254-282 Jiaxian Yu verfasserin aut LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yueming Hu verfasserin aut Yafei Xu verfasserin aut Jue Wang verfasserin aut Jiajie Kuang verfasserin aut Wei Zhang verfasserin aut Jianlin Shao verfasserin aut Dianjing Guo verfasserin aut Yejun Wang verfasserin aut In BMC Cancer BMC, 2003 19(2019), 1, Seite 10 (DE-627)326643710 (DE-600)2041352-X 14712407 nnns volume:19 year:2019 number:1 pages:10 https://doi.org/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/article/52d80d7b41d846f0822380258f61847e kostenfrei http://link.springer.com/article/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/toc/1471-2407 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 10
allfieldsGer	10.1186/s12885-019-5433-7 doi (DE-627)DOAJ074722514 (DE-599)DOAJ52d80d7b41d846f0822380258f61847e DE-627 ger DE-627 rakwb eng RC254-282 Jiaxian Yu verfasserin aut LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yueming Hu verfasserin aut Yafei Xu verfasserin aut Jue Wang verfasserin aut Jiajie Kuang verfasserin aut Wei Zhang verfasserin aut Jianlin Shao verfasserin aut Dianjing Guo verfasserin aut Yejun Wang verfasserin aut In BMC Cancer BMC, 2003 19(2019), 1, Seite 10 (DE-627)326643710 (DE-600)2041352-X 14712407 nnns volume:19 year:2019 number:1 pages:10 https://doi.org/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/article/52d80d7b41d846f0822380258f61847e kostenfrei http://link.springer.com/article/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/toc/1471-2407 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 10
allfieldsSound	10.1186/s12885-019-5433-7 doi (DE-627)DOAJ074722514 (DE-599)DOAJ52d80d7b41d846f0822380258f61847e DE-627 ger DE-627 rakwb eng RC254-282 Jiaxian Yu verfasserin aut LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine. Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Yueming Hu verfasserin aut Yafei Xu verfasserin aut Jue Wang verfasserin aut Jiajie Kuang verfasserin aut Wei Zhang verfasserin aut Jianlin Shao verfasserin aut Dianjing Guo verfasserin aut Yejun Wang verfasserin aut In BMC Cancer BMC, 2003 19(2019), 1, Seite 10 (DE-627)326643710 (DE-600)2041352-X 14712407 nnns volume:19 year:2019 number:1 pages:10 https://doi.org/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/article/52d80d7b41d846f0822380258f61847e kostenfrei http://link.springer.com/article/10.1186/s12885-019-5433-7 kostenfrei https://doaj.org/toc/1471-2407 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 19 2019 1 10
language	English
source	In BMC Cancer 19(2019), 1, Seite 10 volume:19 year:2019 number:1 pages:10
sourceStr	In BMC Cancer 19(2019), 1, Seite 10 volume:19 year:2019 number:1 pages:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	BMC Cancer
authorswithroles_txt_mv	Jiaxian Yu @@aut@@ Yueming Hu @@aut@@ Yafei Xu @@aut@@ Jue Wang @@aut@@ Jiajie Kuang @@aut@@ Wei Zhang @@aut@@ Jianlin Shao @@aut@@ Dianjing Guo @@aut@@ Yejun Wang @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	326643710
id	DOAJ074722514
language_de	englisch
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Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. 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callnumber-first	R - Medicine
author	Jiaxian Yu
spellingShingle	Jiaxian Yu misc RC254-282 misc Lung adenocarcinomas misc Somatic mutational misc Personal medicine misc Support vector machine model misc Machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
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topic_title	RC254-282 LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features Lung adenocarcinomas Somatic mutational Personal medicine Support vector machine model Machine learning
topic	misc RC254-282 misc Lung adenocarcinomas misc Somatic mutational misc Personal medicine misc Support vector machine model misc Machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc Lung adenocarcinomas misc Somatic mutational misc Personal medicine misc Support vector machine model misc Machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc Lung adenocarcinomas misc Somatic mutational misc Personal medicine misc Support vector machine model misc Machine learning misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
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title_full	LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
author_sort	Jiaxian Yu
journal	BMC Cancer
journalStr	BMC Cancer
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author_browse	Jiaxian Yu Yueming Hu Yafei Xu Jue Wang Jiajie Kuang Wei Zhang Jianlin Shao Dianjing Guo Yejun Wang
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title_sort	luadpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
callnumber	RC254-282
title_auth	LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
abstract	Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
abstractGer	Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
abstract_unstemmed	Abstract Background Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation. Methods We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis. Results Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves. Conclusion The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.
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title_short	LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features
url	https://doi.org/10.1186/s12885-019-5433-7 https://doaj.org/article/52d80d7b41d846f0822380258f61847e http://link.springer.com/article/10.1186/s12885-019-5433-7 https://doaj.org/toc/1471-2407
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author2Str	Yueming Hu Yafei Xu Jue Wang Jiajie Kuang Wei Zhang Jianlin Shao Dianjing Guo Yejun Wang
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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features

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