Cargando…

Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer

Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to increase the survival in GC patients, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, b...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Chengfei, Liu, Zilin, Yan, Chuanjing, Xiao, Jiangwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389051/
https://www.ncbi.nlm.nih.gov/pubmed/35991578
http://dx.doi.org/10.3389/fgene.2022.901200
_version_ 1784770353707352064
author Xu, Chengfei
Liu, Zilin
Yan, Chuanjing
Xiao, Jiangwei
author_facet Xu, Chengfei
Liu, Zilin
Yan, Chuanjing
Xiao, Jiangwei
author_sort Xu, Chengfei
collection PubMed
description Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to increase the survival in GC patients, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, but aberrant regulation of the process also leads to a variety of major human diseases. As variations of apoptotic genes may increase susceptibility to gastric cancer. Thus, it is critical to identify novel and potent tools to predict the overall survival (OS) and treatment efficacy of GC. The expression profiles and clinical characteristics of TCGA-STAD and GSE15459 cohorts were downloaded from TCGA and GEO. Apoptotic genes were extracted from the GeneCards database. Apoptosis risk scores were constructed by combining Cox regression and LASSO regression. The GSE15459 and TCGA internal validation sets were used for external validation. Moreover, we explored the relationship between the apoptosis risk score and clinical characteristics, drug sensitivity, tumor microenvironment (TME) and tumor mutational burden (TMB). Finally, we used GSVA to further explore the signaling pathways associated with apoptosis risk. By performing TCGA-STAD differential analysis, we obtained 839 differentially expressed genes, which were then analyzed by Cox regressions and LASSO regression to establish 23 genes associated with apoptosis risk scores. We used the test validation cohort from TCGA-STAD and the GSE15459 dataset for external validation. The AUC values of the ROC curve for 2-, 3-, and 5-years survival were 0.7, 0.71, and 0.71 in the internal validation cohort from TCGA-STAD and 0.77, 0.74, and 0.75 in the GSE15459 dataset, respectively. We constructed a nomogram by combining the apoptosis risk signature and some clinical characteristics from TCGA-STAD. Analysis of apoptosis risk scores and clinical characteristics demonstrated notable differences in apoptosis risk scores between survival status, sex, grade, stage, and T stage. Finally, the apoptosis risk score was correlated with TME characteristics, drug sensitivity, TMB, and TIDE scores.
format Online
Article
Text
id pubmed-9389051
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-93890512022-08-20 Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer Xu, Chengfei Liu, Zilin Yan, Chuanjing Xiao, Jiangwei Front Genet Genetics Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to increase the survival in GC patients, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, but aberrant regulation of the process also leads to a variety of major human diseases. As variations of apoptotic genes may increase susceptibility to gastric cancer. Thus, it is critical to identify novel and potent tools to predict the overall survival (OS) and treatment efficacy of GC. The expression profiles and clinical characteristics of TCGA-STAD and GSE15459 cohorts were downloaded from TCGA and GEO. Apoptotic genes were extracted from the GeneCards database. Apoptosis risk scores were constructed by combining Cox regression and LASSO regression. The GSE15459 and TCGA internal validation sets were used for external validation. Moreover, we explored the relationship between the apoptosis risk score and clinical characteristics, drug sensitivity, tumor microenvironment (TME) and tumor mutational burden (TMB). Finally, we used GSVA to further explore the signaling pathways associated with apoptosis risk. By performing TCGA-STAD differential analysis, we obtained 839 differentially expressed genes, which were then analyzed by Cox regressions and LASSO regression to establish 23 genes associated with apoptosis risk scores. We used the test validation cohort from TCGA-STAD and the GSE15459 dataset for external validation. The AUC values of the ROC curve for 2-, 3-, and 5-years survival were 0.7, 0.71, and 0.71 in the internal validation cohort from TCGA-STAD and 0.77, 0.74, and 0.75 in the GSE15459 dataset, respectively. We constructed a nomogram by combining the apoptosis risk signature and some clinical characteristics from TCGA-STAD. Analysis of apoptosis risk scores and clinical characteristics demonstrated notable differences in apoptosis risk scores between survival status, sex, grade, stage, and T stage. Finally, the apoptosis risk score was correlated with TME characteristics, drug sensitivity, TMB, and TIDE scores. Frontiers Media S.A. 2022-08-05 /pmc/articles/PMC9389051/ /pubmed/35991578 http://dx.doi.org/10.3389/fgene.2022.901200 Text en Copyright © 2022 Xu, Liu, Yan and Xiao. 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 Genetics
Xu, Chengfei
Liu, Zilin
Yan, Chuanjing
Xiao, Jiangwei
Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title_full Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title_fullStr Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title_full_unstemmed Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title_short Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
title_sort application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389051/
https://www.ncbi.nlm.nih.gov/pubmed/35991578
http://dx.doi.org/10.3389/fgene.2022.901200
work_keys_str_mv AT xuchengfei applicationofapoptosisrelatedgenesinamultiomicsrelatedprognosticmodelstudyofgastriccancer
AT liuzilin applicationofapoptosisrelatedgenesinamultiomicsrelatedprognosticmodelstudyofgastriccancer
AT yanchuanjing applicationofapoptosisrelatedgenesinamultiomicsrelatedprognosticmodelstudyofgastriccancer
AT xiaojiangwei applicationofapoptosisrelatedgenesinamultiomicsrelatedprognosticmodelstudyofgastriccancer