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Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape

PURPOSE: To investigate the impact of N6-methyladenosine- (m6A) and neutrophil extracellular traps- (NETs) related lncRNAs (MNlncRNAs) on the prognosis of hepatocellular carcinoma (HCC). METHODS: We collected m6A and NETs-related genes from published studies. We identified the MNlncRNAs by correlati...

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Autores principales: Zhan, Tian, Wang, Wei, Guan, Xiao, Bao, Wei, Lu, Na, Zhang, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585104/
https://www.ncbi.nlm.nih.gov/pubmed/37868992
http://dx.doi.org/10.3389/fimmu.2023.1231543
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author Zhan, Tian
Wang, Wei
Guan, Xiao
Bao, Wei
Lu, Na
Zhang, Jianping
author_facet Zhan, Tian
Wang, Wei
Guan, Xiao
Bao, Wei
Lu, Na
Zhang, Jianping
author_sort Zhan, Tian
collection PubMed
description PURPOSE: To investigate the impact of N6-methyladenosine- (m6A) and neutrophil extracellular traps- (NETs) related lncRNAs (MNlncRNAs) on the prognosis of hepatocellular carcinoma (HCC). METHODS: We collected m6A and NETs-related genes from published studies. We identified the MNlncRNAs by correlation analysis. Cox regression and the least absolute selection operator (LASSO) method were used to select predictive MNlncRNAs. The expressions of predictive MNlncRNAs were detected by cell and tissue experiments. Survival, medication sensitivity, and immunological microenvironment evaluations were used to assess the model’s prognostic utility. Finally, we performed cellular experiments to further validate the model’s prognostic reliability. RESULTS: We obtained a total of 209 MNlncRNAs. 7 MNlncRNAs comprised the prognostic model, which successfully stratifies HCC patients, with the area under the curve (AUC) ranging from 0.7 to 0.8. In vitro tests confirmed that higher risk patients had worse prognosis. Risk score, immunological microenvironment, and immune checkpoint gene expression were all significantly correlated with each other in HCC. In the group at high risk, immunotherapy could be more successful. Cellular assays confirmed that HCC cells with high risk scores have a higher proliferation and invasive capacity. CONCLUSION: The MNlncRNAs-related prognostic model aided in determining HCC prognosis, revealing novel therapeutic options, notably immunotherapy.
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spelling pubmed-105851042023-10-20 Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape Zhan, Tian Wang, Wei Guan, Xiao Bao, Wei Lu, Na Zhang, Jianping Front Immunol Immunology PURPOSE: To investigate the impact of N6-methyladenosine- (m6A) and neutrophil extracellular traps- (NETs) related lncRNAs (MNlncRNAs) on the prognosis of hepatocellular carcinoma (HCC). METHODS: We collected m6A and NETs-related genes from published studies. We identified the MNlncRNAs by correlation analysis. Cox regression and the least absolute selection operator (LASSO) method were used to select predictive MNlncRNAs. The expressions of predictive MNlncRNAs were detected by cell and tissue experiments. Survival, medication sensitivity, and immunological microenvironment evaluations were used to assess the model’s prognostic utility. Finally, we performed cellular experiments to further validate the model’s prognostic reliability. RESULTS: We obtained a total of 209 MNlncRNAs. 7 MNlncRNAs comprised the prognostic model, which successfully stratifies HCC patients, with the area under the curve (AUC) ranging from 0.7 to 0.8. In vitro tests confirmed that higher risk patients had worse prognosis. Risk score, immunological microenvironment, and immune checkpoint gene expression were all significantly correlated with each other in HCC. In the group at high risk, immunotherapy could be more successful. Cellular assays confirmed that HCC cells with high risk scores have a higher proliferation and invasive capacity. CONCLUSION: The MNlncRNAs-related prognostic model aided in determining HCC prognosis, revealing novel therapeutic options, notably immunotherapy. Frontiers Media S.A. 2023-10-05 /pmc/articles/PMC10585104/ /pubmed/37868992 http://dx.doi.org/10.3389/fimmu.2023.1231543 Text en Copyright © 2023 Zhan, Wang, Guan, Bao, Lu and Zhang 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 Immunology
Zhan, Tian
Wang, Wei
Guan, Xiao
Bao, Wei
Lu, Na
Zhang, Jianping
Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title_full Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title_fullStr Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title_full_unstemmed Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title_short Construction of an m6A- and neutrophil extracellular traps-related lncRNA model to predict hepatocellular carcinoma prognosis and immune landscape
title_sort construction of an m6a- and neutrophil extracellular traps-related lncrna model to predict hepatocellular carcinoma prognosis and immune landscape
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585104/
https://www.ncbi.nlm.nih.gov/pubmed/37868992
http://dx.doi.org/10.3389/fimmu.2023.1231543
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