A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical...
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Autor*in:	Xianming Liu [verfasserIn] Xinjie Hui [verfasserIn] Huayu Kang [verfasserIn] Qiongfang Fang [verfasserIn] Aiyue Chen [verfasserIn] Yueming Hu [verfasserIn] Desheng Lu [verfasserIn] Xianxiong Chen [verfasserIn] Yejun Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 11(2020)
Übergeordnetes Werk:	volume:11 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2020.00940

Katalog-ID:	DOAJ068319932

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520			\|a BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
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allfields	10.3389/fgene.2020.00940 doi (DE-627)DOAJ068319932 (DE-599)DOAJb09fe58c362947a9ad92b9110cbfbaba DE-627 ger DE-627 rakwb eng QH426-470 Xianming Liu verfasserin aut A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts. stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics Xinjie Hui verfasserin aut Huayu Kang verfasserin aut Qiongfang Fang verfasserin aut Aiyue Chen verfasserin aut Yueming Hu verfasserin aut Desheng Lu verfasserin aut Xianxiong Chen verfasserin aut Yejun Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00940 kostenfrei https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_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 11 2020
spelling	10.3389/fgene.2020.00940 doi (DE-627)DOAJ068319932 (DE-599)DOAJb09fe58c362947a9ad92b9110cbfbaba DE-627 ger DE-627 rakwb eng QH426-470 Xianming Liu verfasserin aut A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts. stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics Xinjie Hui verfasserin aut Huayu Kang verfasserin aut Qiongfang Fang verfasserin aut Aiyue Chen verfasserin aut Yueming Hu verfasserin aut Desheng Lu verfasserin aut Xianxiong Chen verfasserin aut Yejun Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00940 kostenfrei https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_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 11 2020
allfields_unstemmed	10.3389/fgene.2020.00940 doi (DE-627)DOAJ068319932 (DE-599)DOAJb09fe58c362947a9ad92b9110cbfbaba DE-627 ger DE-627 rakwb eng QH426-470 Xianming Liu verfasserin aut A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts. stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics Xinjie Hui verfasserin aut Huayu Kang verfasserin aut Qiongfang Fang verfasserin aut Aiyue Chen verfasserin aut Yueming Hu verfasserin aut Desheng Lu verfasserin aut Xianxiong Chen verfasserin aut Yejun Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00940 kostenfrei https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_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 11 2020
allfieldsGer	10.3389/fgene.2020.00940 doi (DE-627)DOAJ068319932 (DE-599)DOAJb09fe58c362947a9ad92b9110cbfbaba DE-627 ger DE-627 rakwb eng QH426-470 Xianming Liu verfasserin aut A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts. stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics Xinjie Hui verfasserin aut Huayu Kang verfasserin aut Qiongfang Fang verfasserin aut Aiyue Chen verfasserin aut Yueming Hu verfasserin aut Desheng Lu verfasserin aut Xianxiong Chen verfasserin aut Yejun Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00940 kostenfrei https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_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 11 2020
allfieldsSound	10.3389/fgene.2020.00940 doi (DE-627)DOAJ068319932 (DE-599)DOAJb09fe58c362947a9ad92b9110cbfbaba DE-627 ger DE-627 rakwb eng QH426-470 Xianming Liu verfasserin aut A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts. stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics Xinjie Hui verfasserin aut Huayu Kang verfasserin aut Qiongfang Fang verfasserin aut Aiyue Chen verfasserin aut Yueming Hu verfasserin aut Desheng Lu verfasserin aut Xianxiong Chen verfasserin aut Yejun Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00940 kostenfrei https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_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 11 2020
language	English
source	In Frontiers in Genetics 11(2020) volume:11 year:2020
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topic_facet	stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity Genetics
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container_title	Frontiers in Genetics
authorswithroles_txt_mv	Xianming Liu @@aut@@ Xinjie Hui @@aut@@ Huayu Kang @@aut@@ Qiongfang Fang @@aut@@ Aiyue Chen @@aut@@ Yueming Hu @@aut@@ Desheng Lu @@aut@@ Xianxiong Chen @@aut@@ Yejun Wang @@aut@@
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It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stomach adenocarcinoma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-gene model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">heterogeneity</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield 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callnumber-first	Q - Science
author	Xianming Liu
spellingShingle	Xianming Liu misc QH426-470 misc stomach adenocarcinoma misc prognosis misc prediction misc multi-gene model misc heterogeneity misc Genetics A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features
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topic_title	QH426-470 A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features stomach adenocarcinoma prognosis prediction multi-gene model heterogeneity
topic	misc QH426-470 misc stomach adenocarcinoma misc prognosis misc prediction misc multi-gene model misc heterogeneity misc Genetics
topic_unstemmed	misc QH426-470 misc stomach adenocarcinoma misc prognosis misc prediction misc multi-gene model misc heterogeneity misc Genetics
topic_browse	misc QH426-470 misc stomach adenocarcinoma misc prognosis misc prediction misc multi-gene model misc heterogeneity misc Genetics
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title	A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features
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title_full	A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features
author_sort	Xianming Liu
journal	Frontiers in Genetics
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author_browse	Xianming Liu Xinjie Hui Huayu Kang Qiongfang Fang Aiyue Chen Yueming Hu Desheng Lu Xianxiong Chen Yejun Wang
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title_sort	multi-gene model effectively predicts the overall prognosis of stomach adenocarcinomas with large genetic heterogeneity using somatic mutation features
callnumber	QH426-470
title_auth	A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features
abstract	BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
abstractGer	BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
abstract_unstemmed	BackgroundStomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations.MethodsIn this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association.ResultsTypical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly.ConclusionThe study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
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title_short	A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features
url	https://doi.org/10.3389/fgene.2020.00940 https://doaj.org/article/b09fe58c362947a9ad92b9110cbfbaba https://www.frontiersin.org/article/10.3389/fgene.2020.00940/full https://doaj.org/toc/1664-8021
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author2	Xinjie Hui Huayu Kang Qiongfang Fang Aiyue Chen Yueming Hu Desheng Lu Xianxiong Chen Yejun Wang
author2Str	Xinjie Hui Huayu Kang Qiongfang Fang Aiyue Chen Yueming Hu Desheng Lu Xianxiong Chen Yejun Wang
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doi_str	10.3389/fgene.2020.00940
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up_date	2024-07-03T17:05:53.019Z
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A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features

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