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. 2023 May 15;23(1):172.
doi: 10.1186/s12890-023-02443-2.

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

Yang Wang  1   2 , Jun Nie  1 , Ling Dai  1 , Weiheng Hu  1 , Sen Han  1 , Jie Zhang  1 , Xiaoling Chen  1 , Xiangjuan Ma  1 , Guangming Tian  1 , Di Wu  1 , Ziran Zhang  1 , Jieran Long  1 , Jian Fang  3
Affiliations

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

Yang Wang et al. BMC Pulm Med. .

Abstract

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: 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: 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: 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.

Keywords: Bioinformatics; Endoplasmic reticulum stress; Lung adenocarcinoma; Prognosis; Risk model.

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Conflict of interest statement

The author has no conflicting interests to disclose with the content of this article.

Figures

Fig. 1
Fig. 1
Identification of ERS-DEGs. A The volcano map of DEGs between normal and LUAD patients. B The heat map of DEGs between normal and LUAD patients. C The Venn diagram of ERSGs and DEGs
Fig. 2
Fig. 2
Identification of model genes by univariate Cox regression and multivariate Cox analysis. A The forest map of univariate Cox analysis. B The forest map of multivariate Cox analysis
Fig. 3
Fig. 3
The validation of the risk model in training set. survival differences in high-risk and low-risk groups of training set. A The risk profile of training set. B The KM survival curve in high-risk and low-risk groups of training set. C The ROC curve of training set. D Heat map of correlation between iskscore and each clinical characteristic of training set
Fig. 4
Fig. 4
The survival differences in high-risk and low-risk groups of test set. A Kaplan–Meier Curve for Survival. B Risk Curve. C Correlation heatmap of different modules and clinical parameters. D the ROC curve
Fig. 5
Fig. 5
The survival differences in high-risk and low-risk groups of external validation set. A Kaplan–Meier Curve for Survival. B Risk Curve. C Correlation heatmap of different modules and clinical parameters. D the ROC curve
Fig. 6
Fig. 6
The box plots of risk scores in A Age. B gender. C M stage. D N stage. E T stage. F tumor stages
Fig. 7
Fig. 7
Independent prognostic analysis of the risk model in validation set. A The forest plot of Univariate Cox analysis. B The forest plot of Multivariate Cox analysis. C Nomogram graph predicting survival. D Calibration curve for the nomogram
Fig. 8
Fig. 8
The enrichment Analysis of 70 DEGs between high and low risk groups. A Overview of enrichment results. B The bubble diagram of GO-BP enriched by DEGs. C The bubble diagram of GO-CC enriched by DEGs. D The bubble diagram of GO-MF enriched by DEGs E The bubble diagram of REACTOME enriched by DEGs
Fig. 9
Fig. 9
Comparison of ERS and vascular-related genes. A The box line plot of genes related to endoplasmic reticulum stress state in high and low risk groups. B The box plot of vascular-related genes in high and low risk groups
Fig. 10
Fig. 10
Comparison of TMB and immunotherapy response between high and low risk groups. A The Box plot of TMB values in the high and low risk groups. B The Box plot of neoantigen in the high and low risk groups. C The Box plot of TIDE score, PD-L1 protein (CD274), and T cell exclusion score in the high and low risk groups
Fig. 11
Fig. 11
The box plot of the 10 drugs with significant differences in the high and low risk groups
Fig. 12
Fig. 12
The expression of HSPD1, COL1A1, PCSK9, MAOB, GRIA1, and CAV1 genes in normal cell HBE135-E6E7 and A549, NCI-H1975, and NCI-1395 cell lines
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