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Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma

BACKGROUND: Endoplasmic reticulum (ER) stress had a crucial impact on cell survival, proliferation, and metastasis in various cancers. However, the role of ER stress in lung adenocarcinoma remains unclear. METHOD: Gene expression and clinical data of lung adenocarcinoma (LUAD) samples were extracted...

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Autores principales: Shu, Long, Liu, Shuang, Tao, Yongguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469654/
https://www.ncbi.nlm.nih.gov/pubmed/36110953
http://dx.doi.org/10.3389/fonc.2022.902353
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author Shu, Long
Liu, Shuang
Tao, Yongguang
author_facet Shu, Long
Liu, Shuang
Tao, Yongguang
author_sort Shu, Long
collection PubMed
description BACKGROUND: Endoplasmic reticulum (ER) stress had a crucial impact on cell survival, proliferation, and metastasis in various cancers. However, the role of ER stress in lung adenocarcinoma remains unclear. METHOD: Gene expression and clinical data of lung adenocarcinoma (LUAD) samples were extracted from The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. ER stress score (ERSS) was constructed based on hub genes selected from 799 ER stress-related genes by least absolute shrinkage and selection operator (LASSO) regression. A Cox regression model, integrating ERSS and the TNM stage, was developed to predict overall survival (OS) in TCGA cohort and was validated in GEO cohorts. Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), and gene mutation analyses were performed to further understand the molecular features of ERSS. The tumor immune infiltration was evaluated by ESTIMATE, CIBERSORT, and xCell algorithms. The receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the risk model. p< 0.05 was considered statistically significant. RESULTS: One hundred fifty-seven differentially expressed genes (DEGs) were identified between tumor and para-carcinoma tissues, and 45 of them significantly correlated with OS. Next, we identified 18 hub genes and constructed ERSS by LASSO regression. Multivariate analysis demonstrated that higher ERSS (p< 0.0001, hazard ratio (HR) = 3.8, 95%CI: 2.8–5.2) and TNM stage (p< 0.0001, HR = 1.55, 95%CI: 1.34–1.8) were independent predictors for worse OS. The prediction model integrating ERSS and TNM stage performed well in TCGA cohort (area under the curve (AUC) at five years = 0.748) and three GEO cohorts (AUC at 5 years = 0.658, 0.717, and 0.739). Pathway enrichment analysis showed that ERSS significantly correlated with unfolded protein response. Meanwhile, pathways associated with the cell cycle, growth, and metabolism were significantly enriched in the high ERSS group. Patients with SMARCA4, TP53, and EGFR mutations showed significantly higher ERSS (p = 4e−04, 0.0027, and 0.035, respectively). Tissues with high ERSS exhibited significantly higher infiltration of M1 macrophages, activated dendritic cells, and lower infiltration of CD8+ T cells and B cells, which indicate an activated tumor antigen-presenting but suppressive immune response status. CONCLUSION: We developed and validated an ER stress-related risk model that exhibited great predictive value for OS in patients with LUAD. Our work also expanded the understanding of the role of ER stress in LUAD.
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spelling pubmed-94696542022-09-14 Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma Shu, Long Liu, Shuang Tao, Yongguang Front Oncol Oncology BACKGROUND: Endoplasmic reticulum (ER) stress had a crucial impact on cell survival, proliferation, and metastasis in various cancers. However, the role of ER stress in lung adenocarcinoma remains unclear. METHOD: Gene expression and clinical data of lung adenocarcinoma (LUAD) samples were extracted from The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. ER stress score (ERSS) was constructed based on hub genes selected from 799 ER stress-related genes by least absolute shrinkage and selection operator (LASSO) regression. A Cox regression model, integrating ERSS and the TNM stage, was developed to predict overall survival (OS) in TCGA cohort and was validated in GEO cohorts. Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), and gene mutation analyses were performed to further understand the molecular features of ERSS. The tumor immune infiltration was evaluated by ESTIMATE, CIBERSORT, and xCell algorithms. The receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the risk model. p< 0.05 was considered statistically significant. RESULTS: One hundred fifty-seven differentially expressed genes (DEGs) were identified between tumor and para-carcinoma tissues, and 45 of them significantly correlated with OS. Next, we identified 18 hub genes and constructed ERSS by LASSO regression. Multivariate analysis demonstrated that higher ERSS (p< 0.0001, hazard ratio (HR) = 3.8, 95%CI: 2.8–5.2) and TNM stage (p< 0.0001, HR = 1.55, 95%CI: 1.34–1.8) were independent predictors for worse OS. The prediction model integrating ERSS and TNM stage performed well in TCGA cohort (area under the curve (AUC) at five years = 0.748) and three GEO cohorts (AUC at 5 years = 0.658, 0.717, and 0.739). Pathway enrichment analysis showed that ERSS significantly correlated with unfolded protein response. Meanwhile, pathways associated with the cell cycle, growth, and metabolism were significantly enriched in the high ERSS group. Patients with SMARCA4, TP53, and EGFR mutations showed significantly higher ERSS (p = 4e−04, 0.0027, and 0.035, respectively). Tissues with high ERSS exhibited significantly higher infiltration of M1 macrophages, activated dendritic cells, and lower infiltration of CD8+ T cells and B cells, which indicate an activated tumor antigen-presenting but suppressive immune response status. CONCLUSION: We developed and validated an ER stress-related risk model that exhibited great predictive value for OS in patients with LUAD. Our work also expanded the understanding of the role of ER stress in LUAD. Frontiers Media S.A. 2022-08-30 /pmc/articles/PMC9469654/ /pubmed/36110953 http://dx.doi.org/10.3389/fonc.2022.902353 Text en Copyright © 2022 Shu, Liu and Tao 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 Oncology
Shu, Long
Liu, Shuang
Tao, Yongguang
Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title_full Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title_fullStr Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title_full_unstemmed Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title_short Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
title_sort development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469654/
https://www.ncbi.nlm.nih.gov/pubmed/36110953
http://dx.doi.org/10.3389/fonc.2022.902353
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