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Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes...

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Autores principales: Cui, Yuan, Chen, Zhike, Pan, Bin, Chen, Tong, Ding, Hao, Li, Qifan, Wan, Li, Luo, Gaomeng, Sun, Lang, Ding, Cheng, Yang, Jian, Tong, Xin, Zhao, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513323/
https://www.ncbi.nlm.nih.gov/pubmed/36176276
http://dx.doi.org/10.3389/fcell.2022.979262
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author Cui, Yuan
Chen, Zhike
Pan, Bin
Chen, Tong
Ding, Hao
Li, Qifan
Wan, Li
Luo, Gaomeng
Sun, Lang
Ding, Cheng
Yang, Jian
Tong, Xin
Zhao, Jun
author_facet Cui, Yuan
Chen, Zhike
Pan, Bin
Chen, Tong
Ding, Hao
Li, Qifan
Wan, Li
Luo, Gaomeng
Sun, Lang
Ding, Cheng
Yang, Jian
Tong, Xin
Zhao, Jun
author_sort Cui, Yuan
collection PubMed
description Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation. Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach. Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses. Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment.
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spelling pubmed-95133232022-09-28 Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma Cui, Yuan Chen, Zhike Pan, Bin Chen, Tong Ding, Hao Li, Qifan Wan, Li Luo, Gaomeng Sun, Lang Ding, Cheng Yang, Jian Tong, Xin Zhao, Jun Front Cell Dev Biol Cell and Developmental Biology Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation. Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach. Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses. Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment. Frontiers Media S.A. 2022-09-13 /pmc/articles/PMC9513323/ /pubmed/36176276 http://dx.doi.org/10.3389/fcell.2022.979262 Text en Copyright © 2022 Cui, Chen, Pan, Chen, Ding, Li, Wan, Luo, Sun, Ding, Yang, Tong and Zhao. 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 Cell and Developmental Biology
Cui, Yuan
Chen, Zhike
Pan, Bin
Chen, Tong
Ding, Hao
Li, Qifan
Wan, Li
Luo, Gaomeng
Sun, Lang
Ding, Cheng
Yang, Jian
Tong, Xin
Zhao, Jun
Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title_full Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title_fullStr Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title_full_unstemmed Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title_short Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
title_sort neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513323/
https://www.ncbi.nlm.nih.gov/pubmed/36176276
http://dx.doi.org/10.3389/fcell.2022.979262
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