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Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

BACKGROUND: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by...

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Autores principales: Zhang, Pengpeng, Pei, Shengbin, Wu, Leilei, Xia, Zhijia, Wang, Qi, Huang, Xufeng, Li, Zhangzuo, Xie, Jiaheng, Du, Mingjun, Lin, Haoran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229769/
https://www.ncbi.nlm.nih.gov/pubmed/37265698
http://dx.doi.org/10.3389/fendo.2023.1196372
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author Zhang, Pengpeng
Pei, Shengbin
Wu, Leilei
Xia, Zhijia
Wang, Qi
Huang, Xufeng
Li, Zhangzuo
Xie, Jiaheng
Du, Mingjun
Lin, Haoran
author_facet Zhang, Pengpeng
Pei, Shengbin
Wu, Leilei
Xia, Zhijia
Wang, Qi
Huang, Xufeng
Li, Zhangzuo
Xie, Jiaheng
Du, Mingjun
Lin, Haoran
author_sort Zhang, Pengpeng
collection PubMed
description BACKGROUND: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). METHODS: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. RESULTS: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model’s performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. CONCLUSION: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.
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spelling pubmed-102297692023-06-01 Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma Zhang, Pengpeng Pei, Shengbin Wu, Leilei Xia, Zhijia Wang, Qi Huang, Xufeng Li, Zhangzuo Xie, Jiaheng Du, Mingjun Lin, Haoran Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). METHODS: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. RESULTS: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model’s performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. CONCLUSION: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD. Frontiers Media S.A. 2023-05-17 /pmc/articles/PMC10229769/ /pubmed/37265698 http://dx.doi.org/10.3389/fendo.2023.1196372 Text en Copyright © 2023 Zhang, Pei, Wu, Xia, Wang, Huang, Li, Xie, Du and Lin 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 Endocrinology
Zhang, Pengpeng
Pei, Shengbin
Wu, Leilei
Xia, Zhijia
Wang, Qi
Huang, Xufeng
Li, Zhangzuo
Xie, Jiaheng
Du, Mingjun
Lin, Haoran
Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title_full Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title_fullStr Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title_full_unstemmed Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title_short Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
title_sort integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229769/
https://www.ncbi.nlm.nih.gov/pubmed/37265698
http://dx.doi.org/10.3389/fendo.2023.1196372
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