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DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. DNA repair genes (DRGs) is important in lung cancer. The relationship between the immune environment and the expression levels of DRGs in LUAD remains unclear. The purpose of this study is to assess the relationship betwe...

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Autores principales: Li, Jiayin, Zhou, Jingxu, Zhang, Jing, Xiao, Zhiwei, Wang, Wenping, Chen, Hanrui, Lin, Lizhu, Yang, Qiuye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339776/
https://www.ncbi.nlm.nih.gov/pubmed/34422373
http://dx.doi.org/10.21037/jtd-21-949
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author Li, Jiayin
Zhou, Jingxu
Zhang, Jing
Xiao, Zhiwei
Wang, Wenping
Chen, Hanrui
Lin, Lizhu
Yang, Qiuye
author_facet Li, Jiayin
Zhou, Jingxu
Zhang, Jing
Xiao, Zhiwei
Wang, Wenping
Chen, Hanrui
Lin, Lizhu
Yang, Qiuye
author_sort Li, Jiayin
collection PubMed
description BACKGROUND: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. DNA repair genes (DRGs) is important in lung cancer. The relationship between the immune environment and the expression levels of DRGs in LUAD remains unclear. The purpose of this study is to assess the relationship between DRGs and the immune environment and clinical characteristics of LUAD. METHODS: Data of 169 LUAD cases were obtained from cbioportal. The RNA-seq data came from the The Cancer Genome Atlas (TCGA) database. We collected DRGs from the Reactom database (KW0037, Reactom.org). The 302 genes expressed in each sample were analyzed by hierarchical clustering and grouped using the Gene Cluster 3.0 program. The Java Treeview program was used to generate heat maps of cluster indications and tumor staging patterns. GraphPad Prism 8 was used to draw survival curves and compare overall survival (OS). For single genes, an OS difference analysis between low and high expression populations was performed in GraphPad Prism 8. RESULTS: Matrix clustering showed no difference in the prognosis of the two clusters. The comparison of subgroups showed that Subcluster 1 (SC1) had the best prognosis, and Subcluster 2 (SC2) had the worst. There was a significant difference in tumor grades between Cluster 1 and Cluster 2 (P=0.01). There were significant differences in smoking status, histological grade and adenocarcinoma subtype among subgroups. In Subcluster 3 (SC3), the proportion of poorly differentiated cases was highest. Immunological index analysis showed that there were significant differences between Cluster 1 and Cluster 2 in interferon, macrophages, monocytes, neutrophils, natural killer (NK) cells, and T cells. Tumor purity, interferon, macrophages, monocytes, neutrophils, NK cells, T cells, translation, and proliferation all showed significant differences between subgroups. In SC2, the proliferation index increased (0.082 vs. 0.070); the protein translation index decreased (0.134 vs. 0.137); and the interferon level increased (0.099 vs. 0.097). In SC3, the proliferation index decreased (0.076 vs. 0.071); the protein translation index decreased (0.140 vs. 0.136); and the level of neutrophils increased (0.083 vs. 0.086). CONCLUSIONS: The differences of DRGs in LUAD are related to tissue differentiation and immune indicators but not to prognosis.
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spelling pubmed-83397762021-08-20 DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data Li, Jiayin Zhou, Jingxu Zhang, Jing Xiao, Zhiwei Wang, Wenping Chen, Hanrui Lin, Lizhu Yang, Qiuye J Thorac Dis Original Article BACKGROUND: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. DNA repair genes (DRGs) is important in lung cancer. The relationship between the immune environment and the expression levels of DRGs in LUAD remains unclear. The purpose of this study is to assess the relationship between DRGs and the immune environment and clinical characteristics of LUAD. METHODS: Data of 169 LUAD cases were obtained from cbioportal. The RNA-seq data came from the The Cancer Genome Atlas (TCGA) database. We collected DRGs from the Reactom database (KW0037, Reactom.org). The 302 genes expressed in each sample were analyzed by hierarchical clustering and grouped using the Gene Cluster 3.0 program. The Java Treeview program was used to generate heat maps of cluster indications and tumor staging patterns. GraphPad Prism 8 was used to draw survival curves and compare overall survival (OS). For single genes, an OS difference analysis between low and high expression populations was performed in GraphPad Prism 8. RESULTS: Matrix clustering showed no difference in the prognosis of the two clusters. The comparison of subgroups showed that Subcluster 1 (SC1) had the best prognosis, and Subcluster 2 (SC2) had the worst. There was a significant difference in tumor grades between Cluster 1 and Cluster 2 (P=0.01). There were significant differences in smoking status, histological grade and adenocarcinoma subtype among subgroups. In Subcluster 3 (SC3), the proportion of poorly differentiated cases was highest. Immunological index analysis showed that there were significant differences between Cluster 1 and Cluster 2 in interferon, macrophages, monocytes, neutrophils, natural killer (NK) cells, and T cells. Tumor purity, interferon, macrophages, monocytes, neutrophils, NK cells, T cells, translation, and proliferation all showed significant differences between subgroups. In SC2, the proliferation index increased (0.082 vs. 0.070); the protein translation index decreased (0.134 vs. 0.137); and the interferon level increased (0.099 vs. 0.097). In SC3, the proliferation index decreased (0.076 vs. 0.071); the protein translation index decreased (0.140 vs. 0.136); and the level of neutrophils increased (0.083 vs. 0.086). CONCLUSIONS: The differences of DRGs in LUAD are related to tissue differentiation and immune indicators but not to prognosis. AME Publishing Company 2021-07 /pmc/articles/PMC8339776/ /pubmed/34422373 http://dx.doi.org/10.21037/jtd-21-949 Text en 2021 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Li, Jiayin
Zhou, Jingxu
Zhang, Jing
Xiao, Zhiwei
Wang, Wenping
Chen, Hanrui
Lin, Lizhu
Yang, Qiuye
DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title_full DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title_fullStr DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title_full_unstemmed DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title_short DNA repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
title_sort dna repair genes are associated with tumor tissue differentiation and immune environment in lung adenocarcinoma: a bioinformatics analysis based on big data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339776/
https://www.ncbi.nlm.nih.gov/pubmed/34422373
http://dx.doi.org/10.21037/jtd-21-949
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