The multi-omics analysis identifies a novel endoplasmic reticulum stress and immune related genes signature in lung adenocarcinoma

Abstract

Background: Lung adenocarcinoma (LUAD), a prevalent and aggressive malignancy, necessitates improved prognostic tools and therapeutic insights. While endoplasmic reticulum stress (ERS) and tumor-immune interactions are recognized as key cancer hallmarks, their combined prognostic potential in LUAD remains insufficiently explored.

Methods: Utilizing transcriptomic and clinical data from the TCGA-LUAD cohort, we developed an ERS-immune prognostic signature through Least Absolute Shrinkage and Selection Operator algorithm. A clinical nomogram integrating risk scores with established prognostic factors was established. Tumor microenvironment characteristics were evaluated using the CIBERSORT and ESTIMATE algorithm. The changes following the CR2 gene knockdown in NSCLC cells were evaluated through CCK-8 assay and Transwell assays.

Results: The 10-gene signature effectively stratified patients into distinct risk groups with significant survival differences. The nomogram demonstrated enhanced predictive accuracy compared to traditional staging systems. High-risk patients exhibited immunosuppressive features. CR2 knockdown significantly reduced cellular proliferation and inhibited metastatic capacity.

Conclusion: This integrated ERS-immune signature provides clinically relevant prognostic stratification and reveals potential therapeutic vulnerabilities in LUAD, offering a framework for personalized treatment strategies.

Keywords: Endoplasmic reticulum stress; Immune microenvironment; LASSO; Lung adenocarcinoma; Prognostic.

Fig. 1

Co-expression network structure. (A) Connectivity parameter optimization for scale-free architecture validation through soft threshold power evaluation. (B) Hierarchical clustering dendrogram with color-coded modules reflecting topological dissimilarity. (C) Module-trait association matrix with color gradients (red: high; blue: low correlation coefficients) and statistical significance indicated parenthetically

Fig. 2

Functional enrichment analysis. (A) Venn diagram showing the overlap between all mRNA (List 1) and endoplasmic reticulum stress and immune genes (List 2). (B) Volcano plot displaying the DEGs. (C) Forest plot showing the hazard ratios (HR), 95% confidence intervals, and P-values for genes associated with prognosis. (D) Circular visualization of GO and KEGG pathway enrichment analysis

Fig. 3

Construction of the sisk score model. (A) Coefficient profile plot of selected genes. (B) Lasso Cox regression with 10-fold cross-validation for parameter tuning. (C, D) Risk score distribution and survival status of patients in TCGA. (E) Kaplan-Meier survival analysis comparing high- and low-risk groups. (F) ROC curve evaluating the prognostic accuracy in TCGA

Fig. 4

Nomogram construction. (A) Forest plot showing univariate Cox analysis of clinical features (age, gender, clinical stage, T stage, N stage, M stage) and risk score. (B) Forest plot showing multivariate Cox analysis of clinical features (clinical stage, T stage, N stage, M stage) and risk score. (C) Nomogram based on clinical features and risk score. (D) Calibration curves for the TCGA, GSE27094, and GSE31210 datasets

Fig. 5

Immune infiltration analysis between risk groups. (A) Distribution of 28 immune cell subsets between high and low-risk groups. (B) Boxplot showing the expression of immune checkpoint molecules, inflammatory factors, and the cytolytic molecule gene family in the high-risk and low-risk groups. (C) Boxplot displaying the expression levels of immune scores. (D) Negative correlation between ESTIMATE scores and risk score. *p < 0.05, ** p < 0.01, *** p < 0.001

Fig. 6

Correlation of prognostic model with tumor mutation burden. (A, B) Waterfall plots showing the distribution differences of the top 20 mutated genes in the low-risk group (A) and high-risk group (B). (C) Boxplot showing the distribution of TMB in the high-risk and low-risk groups. (D) Scatter plot showing the correlation between risk score and TMB. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 7

Association of drug sensitivity, immune response, and risk score. (A) Correlation heatmap of gene expression and therapeutic drug responses (chemotherapy and targeted therapies). (B) TIDE score comparison between high- and low-risk groups. (C) Immunotherapy response rates stratified by risk group. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 8

Functional characterization of CR2 in LUAD cells. (A, B) CR2 knockdown significantly reduced cell viability assessed by CCK-8 assay in A549 and H1299 cells. (C, D) CR2 silencing markedly impaired cell migration capacity in Transwell assays (uncoated membrane) using A549 and H1299 cells. (E, F) CR2 depletion potently suppressed cell invasion capacity in Transwell assays (Matrigel-coated membrane) using A549 and H1299 cells. Data represent mean ± SD; ****P < 0.0001