Cargando…

Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer

BACKGROUND: Tumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated. AIM: To explore the landscape of mutation profiles and determine the...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Dong-Yan, Sun, Xi-Zhen, Yao, Shu-Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805270/
https://www.ncbi.nlm.nih.gov/pubmed/33510848
http://dx.doi.org/10.4251/wjgo.v13.i1.37
_version_ 1783636287172902912
author Zhao, Dong-Yan
Sun, Xi-Zhen
Yao, Shu-Kun
author_facet Zhao, Dong-Yan
Sun, Xi-Zhen
Yao, Shu-Kun
author_sort Zhao, Dong-Yan
collection PubMed
description BACKGROUND: Tumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated. AIM: To explore the landscape of mutation profiles and determine the correlation between TMB and microRNA (miRNA) expression in GC. METHODS: Genomic, transcriptomic, and clinical data from The Cancer Genome Atlas were used to obtain mutational profiles and investigate the statistical correlation between mutational burden and the overall survival of GC patients. The difference in immune infiltration between high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. Furthermore, miRNAs differentially expressed between the high- and low-TMB subgroups were identified and the least absolute shrinkage and selection operator method was employed to construct a miRNA-based signature for TMB prediction. The biological functions of the predictive miRNAs were identified with DIANA-miRPath v3.0. RESULTS: C>T single nucleotide mutations exhibited the highest mutation incidence, and the top three mutated genes were TTN, TP53, and MUC16 in GC. High TMB values (top 20%) were markedly correlated with better survival outcome, and multivariable regression analysis indicated that TMB remained prognostic independent of TNM stage, histological grade, age, and gender. Different TMB levels exhibited different immune infiltration patterns. Significant differences between the high- and low-TMB subgroups were observed in the infiltration of CD8+ T cells, M1 macrophages, regulatory T cells, and CD4+ T cells. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients. The predictive performance of the signature was confirmed in the testing and the whole set. Receiver operating characteristic curve analysis demonstrated the optimal performance of the signature. Finally, enrichment analysis demonstrated that the set of miRNAs was significantly enriched in many key cancer and immune-related pathways. CONCLUSION: TMB:
format Online
Article
Text
id pubmed-7805270
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Baishideng Publishing Group Inc
record_format MEDLINE/PubMed
spelling pubmed-78052702021-01-27 Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer Zhao, Dong-Yan Sun, Xi-Zhen Yao, Shu-Kun World J Gastrointest Oncol Observational Study BACKGROUND: Tumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated. AIM: To explore the landscape of mutation profiles and determine the correlation between TMB and microRNA (miRNA) expression in GC. METHODS: Genomic, transcriptomic, and clinical data from The Cancer Genome Atlas were used to obtain mutational profiles and investigate the statistical correlation between mutational burden and the overall survival of GC patients. The difference in immune infiltration between high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. Furthermore, miRNAs differentially expressed between the high- and low-TMB subgroups were identified and the least absolute shrinkage and selection operator method was employed to construct a miRNA-based signature for TMB prediction. The biological functions of the predictive miRNAs were identified with DIANA-miRPath v3.0. RESULTS: C>T single nucleotide mutations exhibited the highest mutation incidence, and the top three mutated genes were TTN, TP53, and MUC16 in GC. High TMB values (top 20%) were markedly correlated with better survival outcome, and multivariable regression analysis indicated that TMB remained prognostic independent of TNM stage, histological grade, age, and gender. Different TMB levels exhibited different immune infiltration patterns. Significant differences between the high- and low-TMB subgroups were observed in the infiltration of CD8+ T cells, M1 macrophages, regulatory T cells, and CD4+ T cells. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients. The predictive performance of the signature was confirmed in the testing and the whole set. Receiver operating characteristic curve analysis demonstrated the optimal performance of the signature. Finally, enrichment analysis demonstrated that the set of miRNAs was significantly enriched in many key cancer and immune-related pathways. CONCLUSION: TMB: Baishideng Publishing Group Inc 2021-01-15 2021-01-15 /pmc/articles/PMC7805270/ /pubmed/33510848 http://dx.doi.org/10.4251/wjgo.v13.i1.37 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Observational Study
Zhao, Dong-Yan
Sun, Xi-Zhen
Yao, Shu-Kun
Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title_full Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title_fullStr Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title_full_unstemmed Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title_short Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
title_sort mining the cancer genome atlas database for tumor mutation burden and its clinical implications in gastric cancer
topic Observational Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805270/
https://www.ncbi.nlm.nih.gov/pubmed/33510848
http://dx.doi.org/10.4251/wjgo.v13.i1.37
work_keys_str_mv AT zhaodongyan miningthecancergenomeatlasdatabasefortumormutationburdenanditsclinicalimplicationsingastriccancer
AT sunxizhen miningthecancergenomeatlasdatabasefortumormutationburdenanditsclinicalimplicationsingastriccancer
AT yaoshukun miningthecancergenomeatlasdatabasefortumormutationburdenanditsclinicalimplicationsingastriccancer