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Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma
Metabolic reprogramming is a novel method for the treatment of malignant tumors. The exploration of metabolism procedures between radiosensitive and radioresistant tumors may provide novel perspectives for lung adenocarcinoma (LUAD) patients after radiation therapy. In our study, metabolic reprogram...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262104/ https://www.ncbi.nlm.nih.gov/pubmed/35812404 http://dx.doi.org/10.3389/fimmu.2022.872910 |
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author | Peng, Shun-Li Wang, Rong Zhou, Yu-Ling Wei, Wei Zhong, Gui-Hua Huang, Xiao-Tao Yang, Shuai Liu, Qiao-Dan Liu, Zhi-Gang |
author_facet | Peng, Shun-Li Wang, Rong Zhou, Yu-Ling Wei, Wei Zhong, Gui-Hua Huang, Xiao-Tao Yang, Shuai Liu, Qiao-Dan Liu, Zhi-Gang |
author_sort | Peng, Shun-Li |
collection | PubMed |
description | Metabolic reprogramming is a novel method for the treatment of malignant tumors. The exploration of metabolism procedures between radiosensitive and radioresistant tumors may provide novel perspectives for lung adenocarcinoma (LUAD) patients after radiation therapy. In our study, metabolic reprogramming and immune response changes were found between radioresistant cell line (A549RR) and its parent cells (A549) using gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Nucleotide/amino acid, lipid, and glucose metabolic process, including Alanine, aspartate and glutamate metabolism, Tryptophan/Tyrosine metabolism, Butanoate metabolism, Purine/Pyrimidine metabolism, were screened out. Then molecular signatures database and The Cancer Genome Atlas Program (TCGA) lung adenocarcinoma datasets were used to identify metabolism-related genes (MRGs) between radiosensitive and radioresistant lung adenocarcinoma (LUAD) cells. A metabolism-based prognostic model, receiver operating characteristic (ROC) curve and nomogram were constructed using Metabolism Score calculated by 14 metabolism-related genes (MRGs). Three independent public datasets, (GSE72094, GSE3141, GSE8894) and one immunotherapy cohort (IMvigor210) were used as external validation cohorts. Expression of 14 hub genes in cells, normal and LUAD specimens were explored by Human Protein Atlas, TIMER2.0 and RT-qPCR. Patients with low-Metabolism Scores were correlated with longer survival times, higher response rates to immune checkpoint inhibitors (ICIs), different immune cell infiltrations and drug vulnerability. Our study demonstrated a comprehensive landscape between radiosensitive and radioresistant LUAD, and provide novel targets for NSCLC, especially those patients received radiation therapy. Moreover, this metabolism-based prognostic model may help to investigate connections between radiosensitivity, immune response, metabolic reprogramming, and patients’ prognosis. |
format | Online Article Text |
id | pubmed-9262104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92621042022-07-08 Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma Peng, Shun-Li Wang, Rong Zhou, Yu-Ling Wei, Wei Zhong, Gui-Hua Huang, Xiao-Tao Yang, Shuai Liu, Qiao-Dan Liu, Zhi-Gang Front Immunol Immunology Metabolic reprogramming is a novel method for the treatment of malignant tumors. The exploration of metabolism procedures between radiosensitive and radioresistant tumors may provide novel perspectives for lung adenocarcinoma (LUAD) patients after radiation therapy. In our study, metabolic reprogramming and immune response changes were found between radioresistant cell line (A549RR) and its parent cells (A549) using gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Nucleotide/amino acid, lipid, and glucose metabolic process, including Alanine, aspartate and glutamate metabolism, Tryptophan/Tyrosine metabolism, Butanoate metabolism, Purine/Pyrimidine metabolism, were screened out. Then molecular signatures database and The Cancer Genome Atlas Program (TCGA) lung adenocarcinoma datasets were used to identify metabolism-related genes (MRGs) between radiosensitive and radioresistant lung adenocarcinoma (LUAD) cells. A metabolism-based prognostic model, receiver operating characteristic (ROC) curve and nomogram were constructed using Metabolism Score calculated by 14 metabolism-related genes (MRGs). Three independent public datasets, (GSE72094, GSE3141, GSE8894) and one immunotherapy cohort (IMvigor210) were used as external validation cohorts. Expression of 14 hub genes in cells, normal and LUAD specimens were explored by Human Protein Atlas, TIMER2.0 and RT-qPCR. Patients with low-Metabolism Scores were correlated with longer survival times, higher response rates to immune checkpoint inhibitors (ICIs), different immune cell infiltrations and drug vulnerability. Our study demonstrated a comprehensive landscape between radiosensitive and radioresistant LUAD, and provide novel targets for NSCLC, especially those patients received radiation therapy. Moreover, this metabolism-based prognostic model may help to investigate connections between radiosensitivity, immune response, metabolic reprogramming, and patients’ prognosis. Frontiers Media S.A. 2022-06-23 /pmc/articles/PMC9262104/ /pubmed/35812404 http://dx.doi.org/10.3389/fimmu.2022.872910 Text en Copyright © 2022 Peng, Wang, Zhou, Wei, Zhong, Huang, Yang, Liu and Liu 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 Peng, Shun-Li Wang, Rong Zhou, Yu-Ling Wei, Wei Zhong, Gui-Hua Huang, Xiao-Tao Yang, Shuai Liu, Qiao-Dan Liu, Zhi-Gang Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title | Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title_full | Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title_fullStr | Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title_full_unstemmed | Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title_short | Insight of a Metabolic Prognostic Model to Identify Tumor Environment and Drug Vulnerability for Lung Adenocarcinoma |
title_sort | insight of a metabolic prognostic model to identify tumor environment and drug vulnerability for lung adenocarcinoma |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262104/ https://www.ncbi.nlm.nih.gov/pubmed/35812404 http://dx.doi.org/10.3389/fimmu.2022.872910 |
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