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Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, th...

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Autores principales: Ma, Weijun, Li, Weidong, Xu, Lei, Liu, Lu, Xia, Yu, Yang, Liping, Da, Mingxu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460769/
https://www.ncbi.nlm.nih.gov/pubmed/34566519
http://dx.doi.org/10.3389/pore.2021.1609852
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author Ma, Weijun
Li, Weidong
Xu, Lei
Liu, Lu
Xia, Yu
Yang, Liping
Da, Mingxu
author_facet Ma, Weijun
Li, Weidong
Xu, Lei
Liu, Lu
Xia, Yu
Yang, Liping
Da, Mingxu
author_sort Ma, Weijun
collection PubMed
description Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC). Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration. Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4(+) memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group. Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.
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spelling pubmed-84607692021-09-25 Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden Ma, Weijun Li, Weidong Xu, Lei Liu, Lu Xia, Yu Yang, Liping Da, Mingxu Pathol Oncol Res Pathology and Oncology Archive Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC). Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration. Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4(+) memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group. Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy. Frontiers Media S.A. 2021-09-10 /pmc/articles/PMC8460769/ /pubmed/34566519 http://dx.doi.org/10.3389/pore.2021.1609852 Text en Copyright © 2021 Ma, Li, Xu, Liu, Xia, Yang and Da. 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 Pathology and Oncology Archive
Ma, Weijun
Li, Weidong
Xu, Lei
Liu, Lu
Xia, Yu
Yang, Liping
Da, Mingxu
Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title_full Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title_fullStr Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title_full_unstemmed Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title_short Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden
title_sort identification of a gene prognostic model of gastric cancer based on analysis of tumor mutation burden
topic Pathology and Oncology Archive
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460769/
https://www.ncbi.nlm.nih.gov/pubmed/34566519
http://dx.doi.org/10.3389/pore.2021.1609852
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