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Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach

Radiotherapy (RT) plays an important role in the prognosis of lung adenocarcinoma (LUAD) patients, but the radioresistance (RR) of LUAD is still a challenge that needs to be overcome. The current study aimed to investigate LUAD patients with RR to illuminate the underlying mechanisms. We utilized ge...

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Autores principales: Wang, Junhao, Han, Qizheng, Liu, Huizi, Luo, Haihua, Li, Lei, Liu, Aihua, Jiang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239180/
https://www.ncbi.nlm.nih.gov/pubmed/34212001
http://dx.doi.org/10.3389/fmolb.2021.624575
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author Wang, Junhao
Han, Qizheng
Liu, Huizi
Luo, Haihua
Li, Lei
Liu, Aihua
Jiang, Yong
author_facet Wang, Junhao
Han, Qizheng
Liu, Huizi
Luo, Haihua
Li, Lei
Liu, Aihua
Jiang, Yong
author_sort Wang, Junhao
collection PubMed
description Radiotherapy (RT) plays an important role in the prognosis of lung adenocarcinoma (LUAD) patients, but the radioresistance (RR) of LUAD is still a challenge that needs to be overcome. The current study aimed to investigate LUAD patients with RR to illuminate the underlying mechanisms. We utilized gene set variation analysis (GSVA) and The Cancer Immunome Atlas (TCIA) database to characterize the differences in biological functions and neoantigen-coding genes between RR and radiosensitive (RS) patients. Weighted Gene co-expression network analysis (WGCNA) was used to explore the relationship between RT-related traits and hub genes in two modules, i.e., RR and RS; two representative hub genes for RR (MZB1 and DERL3) and two for RS (IFI35 and PSMD3) were found to be related to different RT-related traits. Further analysis of the hub genes with the Lung Cancer Explorer (LCE), PanglaoDB and GSVA resources revealed the differences in gene expression levels, cell types and potential functions. On this basis, the Tumor and Immune System Interaction Database (TISIDB) was used to identify the potential association between RR genes and B cell infiltration. Finally, we used the Computational Analysis of Resistance (CARE) database to identify specific gene-associated drugs for RR patients and found that GSK525762A and nilotinib might be promising candidates for RR treatment. Taken together, these results demonstrate that B cells in TME may have a significant impact on the RT and that these two drug candidates, GSK525762A and nilotinib, might be helpful for the treatment of RR patients.
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spelling pubmed-82391802021-06-30 Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach Wang, Junhao Han, Qizheng Liu, Huizi Luo, Haihua Li, Lei Liu, Aihua Jiang, Yong Front Mol Biosci Molecular Biosciences Radiotherapy (RT) plays an important role in the prognosis of lung adenocarcinoma (LUAD) patients, but the radioresistance (RR) of LUAD is still a challenge that needs to be overcome. The current study aimed to investigate LUAD patients with RR to illuminate the underlying mechanisms. We utilized gene set variation analysis (GSVA) and The Cancer Immunome Atlas (TCIA) database to characterize the differences in biological functions and neoantigen-coding genes between RR and radiosensitive (RS) patients. Weighted Gene co-expression network analysis (WGCNA) was used to explore the relationship between RT-related traits and hub genes in two modules, i.e., RR and RS; two representative hub genes for RR (MZB1 and DERL3) and two for RS (IFI35 and PSMD3) were found to be related to different RT-related traits. Further analysis of the hub genes with the Lung Cancer Explorer (LCE), PanglaoDB and GSVA resources revealed the differences in gene expression levels, cell types and potential functions. On this basis, the Tumor and Immune System Interaction Database (TISIDB) was used to identify the potential association between RR genes and B cell infiltration. Finally, we used the Computational Analysis of Resistance (CARE) database to identify specific gene-associated drugs for RR patients and found that GSK525762A and nilotinib might be promising candidates for RR treatment. Taken together, these results demonstrate that B cells in TME may have a significant impact on the RT and that these two drug candidates, GSK525762A and nilotinib, might be helpful for the treatment of RR patients. Frontiers Media S.A. 2021-06-15 /pmc/articles/PMC8239180/ /pubmed/34212001 http://dx.doi.org/10.3389/fmolb.2021.624575 Text en Copyright © 2021 Wang, Han, Liu, Luo, Li, Liu and Jiang. 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 Molecular Biosciences
Wang, Junhao
Han, Qizheng
Liu, Huizi
Luo, Haihua
Li, Lei
Liu, Aihua
Jiang, Yong
Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title_full Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title_fullStr Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title_full_unstemmed Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title_short Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach
title_sort identification of radiotherapy-associated genes in lung adenocarcinoma by an integrated bioinformatics analysis approach
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239180/
https://www.ncbi.nlm.nih.gov/pubmed/34212001
http://dx.doi.org/10.3389/fmolb.2021.624575
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