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Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma

Lung cancer is a major cause of cancer-related deaths globally, with a dismal prognosis. N7-methylguanosine (m7G) is essential for the transcriptional phenotypic modification of messenger RNA (mRNA) and long noncoding RNA (lncRNA). However, research on m7G-related lncRNAs involved in lung adenocarci...

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Autores principales: Zhang, Chuanhao, Zhou, Dong, Wang, Zhe, Ju, Zaishuang, He, Jiabei, Zhao, Genghao, Wang, Ruoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214213/
https://www.ncbi.nlm.nih.gov/pubmed/35754819
http://dx.doi.org/10.3389/fgene.2022.907754
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author Zhang, Chuanhao
Zhou, Dong
Wang, Zhe
Ju, Zaishuang
He, Jiabei
Zhao, Genghao
Wang, Ruoyu
author_facet Zhang, Chuanhao
Zhou, Dong
Wang, Zhe
Ju, Zaishuang
He, Jiabei
Zhao, Genghao
Wang, Ruoyu
author_sort Zhang, Chuanhao
collection PubMed
description Lung cancer is a major cause of cancer-related deaths globally, with a dismal prognosis. N7-methylguanosine (m7G) is essential for the transcriptional phenotypic modification of messenger RNA (mRNA) and long noncoding RNA (lncRNA). However, research on m7G-related lncRNAs involved in lung adenocarcinoma (LUAD) regulation is still limited. Herein, we aim to establish a prognostic model of m7G-related lncRNAs and investigate their immune properties. Eight prognostic m7G-related lncRNAs were identified using univariate Cox analysis. Six m7G-related lncRNAs were identified using LASSO-Cox regression analysis to construct risk models, and all LUAD patients in The Cancer Genome Atlas (TCGA) cohort was divided into low-risk and high-risk subgroups. The accuracy of the model was verified by Kaplan-Meier analysis, time-dependent receiver operating characteristic, principal component analysis, independent prognostic analysis, nomogram, and calibration curve. Further studies were conducted on the gene set enrichment and disease ontology enrichment analyses. The gene set enrichment analysis (GSEA) revealed that the high-risk group enriched for cancer proliferation pathways, and the enrichment analysis of disease ontology (DO) revealed that lung disease was enriched, rationally explaining the superiority of the risk model. Finally, we found that the low-risk group had higher immune infiltration and checkpoint expression. It can be speculated that the low-risk group has a better effect on immunotherapy. Susceptibility to antitumor drugs in different risk subgroups was assessed, and it found that the high-risk group showed high sensitivity to first-line treatment drugs for non-small cell lung cancer. In conclusion, a risk model based on 6 m7G-related lncRNAs can not only predict the overall survival (OS) rate of LUAD patients but also guide individualized treatment for these patients.
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spelling pubmed-92142132022-06-23 Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma Zhang, Chuanhao Zhou, Dong Wang, Zhe Ju, Zaishuang He, Jiabei Zhao, Genghao Wang, Ruoyu Front Genet Genetics Lung cancer is a major cause of cancer-related deaths globally, with a dismal prognosis. N7-methylguanosine (m7G) is essential for the transcriptional phenotypic modification of messenger RNA (mRNA) and long noncoding RNA (lncRNA). However, research on m7G-related lncRNAs involved in lung adenocarcinoma (LUAD) regulation is still limited. Herein, we aim to establish a prognostic model of m7G-related lncRNAs and investigate their immune properties. Eight prognostic m7G-related lncRNAs were identified using univariate Cox analysis. Six m7G-related lncRNAs were identified using LASSO-Cox regression analysis to construct risk models, and all LUAD patients in The Cancer Genome Atlas (TCGA) cohort was divided into low-risk and high-risk subgroups. The accuracy of the model was verified by Kaplan-Meier analysis, time-dependent receiver operating characteristic, principal component analysis, independent prognostic analysis, nomogram, and calibration curve. Further studies were conducted on the gene set enrichment and disease ontology enrichment analyses. The gene set enrichment analysis (GSEA) revealed that the high-risk group enriched for cancer proliferation pathways, and the enrichment analysis of disease ontology (DO) revealed that lung disease was enriched, rationally explaining the superiority of the risk model. Finally, we found that the low-risk group had higher immune infiltration and checkpoint expression. It can be speculated that the low-risk group has a better effect on immunotherapy. Susceptibility to antitumor drugs in different risk subgroups was assessed, and it found that the high-risk group showed high sensitivity to first-line treatment drugs for non-small cell lung cancer. In conclusion, a risk model based on 6 m7G-related lncRNAs can not only predict the overall survival (OS) rate of LUAD patients but also guide individualized treatment for these patients. Frontiers Media S.A. 2022-06-08 /pmc/articles/PMC9214213/ /pubmed/35754819 http://dx.doi.org/10.3389/fgene.2022.907754 Text en Copyright © 2022 Zhang, Zhou, Wang, Ju, He, Zhao and Wang. 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 Genetics
Zhang, Chuanhao
Zhou, Dong
Wang, Zhe
Ju, Zaishuang
He, Jiabei
Zhao, Genghao
Wang, Ruoyu
Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title_full Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title_fullStr Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title_full_unstemmed Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title_short Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma
title_sort risk model and immune signature of m7g-related lncrna based on lung adenocarcinoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214213/
https://www.ncbi.nlm.nih.gov/pubmed/35754819
http://dx.doi.org/10.3389/fgene.2022.907754
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