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Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics

Existing studies suggest that m(6)A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m(6)A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six...

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Autores principales: Li, Huijun, Liu, Song-Bai, Shen, Junjie, Bai, Lu, Zhang, Xinyan, Cao, Jianping, Yi, Nengjun, Lu, Ke, Tang, Zaixiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243308/
https://www.ncbi.nlm.nih.gov/pubmed/35785155
http://dx.doi.org/10.3389/fonc.2022.895148
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author Li, Huijun
Liu, Song-Bai
Shen, Junjie
Bai, Lu
Zhang, Xinyan
Cao, Jianping
Yi, Nengjun
Lu, Ke
Tang, Zaixiang
author_facet Li, Huijun
Liu, Song-Bai
Shen, Junjie
Bai, Lu
Zhang, Xinyan
Cao, Jianping
Yi, Nengjun
Lu, Ke
Tang, Zaixiang
author_sort Li, Huijun
collection PubMed
description Existing studies suggest that m(6)A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m(6)A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m(6)A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m(6)A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/.
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spelling pubmed-92433082022-07-01 Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics Li, Huijun Liu, Song-Bai Shen, Junjie Bai, Lu Zhang, Xinyan Cao, Jianping Yi, Nengjun Lu, Ke Tang, Zaixiang Front Oncol Oncology Existing studies suggest that m(6)A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m(6)A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m(6)A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m(6)A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/. Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9243308/ /pubmed/35785155 http://dx.doi.org/10.3389/fonc.2022.895148 Text en Copyright © 2022 Li, Liu, Shen, Bai, Zhang, Cao, Yi, Lu and Tang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Li, Huijun
Liu, Song-Bai
Shen, Junjie
Bai, Lu
Zhang, Xinyan
Cao, Jianping
Yi, Nengjun
Lu, Ke
Tang, Zaixiang
Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title_full Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title_fullStr Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title_full_unstemmed Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title_short Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics
title_sort development and validation of prognostic model for lung adenocarcinoma patients based on m6a methylation related transcriptomics
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243308/
https://www.ncbi.nlm.nih.gov/pubmed/35785155
http://dx.doi.org/10.3389/fonc.2022.895148
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