Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis.


Journal

BMC pulmonary medicine
ISSN: 1471-2466
Titre abrégé: BMC Pulm Med
Pays: England
ID NLM: 100968563

Informations de publication

Date de publication:
15 May 2023
Historique:
received: 31 08 2022
accepted: 18 04 2023
medline: 17 5 2023
pubmed: 16 5 2023
entrez: 15 5 2023
Statut: epublish

Résumé

Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments. The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes. A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis. A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Sections du résumé

BACKGROUND BACKGROUND
Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.
METHODS METHODS
The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.
RESULTS RESULTS
A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.
CONCLUSION CONCLUSIONS
A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

Identifiants

pubmed: 37189138
doi: 10.1186/s12890-023-02443-2
pii: 10.1186/s12890-023-02443-2
pmc: PMC10186720
doi:

Substances chimiques

PCSK9 protein, human EC 3.4.21.-
Proprotein Convertase 9 EC 3.4.21.-
RNA, Messenger 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

172

Informations de copyright

© 2023. The Author(s).

Références

Cell Physiol Biochem. 2017;42(4):1684-1700
pubmed: 28743125
Cancer Cell. 2014 May 12;25(5):563-73
pubmed: 24823636
Int J Mol Sci. 2019 Oct 07;20(19):
pubmed: 31591367
Nat Cell Biol. 2011 Mar;13(3):184-90
pubmed: 21364565
J Cancer. 2017 Jun 1;8(8):1466-1476
pubmed: 28638462
Lung Cancer. 2014 Apr;84(1):13-22
pubmed: 24524818
Cancer Res. 2013 Aug 1;73(15):4732-43
pubmed: 23752693
Curr Opin Oncol. 2021 Jan;33(1):40-46
pubmed: 33165004
J Clin Med. 2020 Nov 03;9(11):
pubmed: 33153004
Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61
pubmed: 23180760
Cancer Biomark. 2021;30(1):85-94
pubmed: 32986659
Clin Cancer Res. 2015 Aug 1;21(15):3561-8
pubmed: 25695692
J Indian Inst Sci. 2020;100(4):701-716
pubmed: 33100615
Oncotarget. 2014 Dec 15;5(23):12331-45
pubmed: 25514597
Proc Natl Acad Sci U S A. 2002 Sep 17;99(19):12293-7
pubmed: 12218188
Oncoimmunology. 2021 Aug 10;10(1):1962591
pubmed: 34408924
Lung Cancer. 2022 Sep;171:42-46
pubmed: 35907387
Nucleic Acids Res. 2023 Jan 6;51(D1):D587-D592
pubmed: 36300620
J Immunol Res. 2022 May 25;2022:5366185
pubmed: 35664356
Lung Cancer. 2019 Feb;128:91-100
pubmed: 30642458
CA Cancer J Clin. 2022 Jan;72(1):7-33
pubmed: 35020204
Int J Mol Sci. 2020 Sep 13;21(18):
pubmed: 32933189
EBioMedicine. 2020 Sep;59:102959
pubmed: 32853987
Respirology. 2016 Jul;21(5):821-33
pubmed: 27101251
PLoS One. 2012;7(12):e51845
pubmed: 23272178
Crit Rev Oncol Hematol. 2021 Jul;163:103374
pubmed: 34087341
Immune Netw. 2022 Feb 14;22(1):e2
pubmed: 35291660
Am J Pathol. 2013 May;182(5):1843-53
pubmed: 23499372
Annu Rev Biochem. 2005;74:739-89
pubmed: 15952902
J Cell Mol Med. 2021 Apr;25(8):3870-3884
pubmed: 33611848
Clin Cancer Res. 2020 May 1;26(9):2231-2243
pubmed: 31953311
Pharm Stat. 2011 Mar-Apr;10(2):128-34
pubmed: 22328315
Cell. 2015 Jun 18;161(7):1527-38
pubmed: 26073941
Oncol Lett. 2019 Nov;18(5):4605-4612
pubmed: 31611968
Genome Med. 2020 Feb 26;12(1):21
pubmed: 32102694
Intern Med J. 2016 Aug;46(8):946-54
pubmed: 27177600
Ann Oncol. 2019 Jan 1;30(1):44-56
pubmed: 30395155
J Neurooncol. 2008 Jun;88(2):121-33
pubmed: 18317690
Nat Rev Cancer. 2014 Sep;14(9):581-97
pubmed: 25145482
Naunyn Schmiedebergs Arch Pharmacol. 2022 Jun;395(6):643-658
pubmed: 35307759
Proc Natl Acad Sci U S A. 2010 Aug 31;107(35):15553-8
pubmed: 20702765
Science. 2015 Apr 3;348(6230):69-74
pubmed: 25838375
Protein Sci. 2019 Nov;28(11):1947-1951
pubmed: 31441146
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
pubmed: 10592173
Matrix Biol. 2018 Nov;73:105-121
pubmed: 29499357
J Cancer. 2021 Jul 25;12(19):5723-5731
pubmed: 34475986
Physiol Rev. 2007 Oct;87(4):1377-408
pubmed: 17928587

Auteurs

Yang Wang (Y)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.
Clinical Trial Center, Peking University Cancer Hospital & Institute, Beijing, China.

Jun Nie (J)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Ling Dai (L)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Weiheng Hu (W)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Sen Han (S)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Jie Zhang (J)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Xiaoling Chen (X)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Xiangjuan Ma (X)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Guangming Tian (G)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Di Wu (D)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Ziran Zhang (Z)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Jieran Long (J)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China.

Jian Fang (J)

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Oncology, Peking University Cancer Hospital & Institute, 52# Fucheng Road, Haidian District, Beijing, 100142, China. jianfang@bjcancer.org.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH