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

Deciphering the prognostic role of endoplasmic reticulum stress in lung adenocarcinoma: integrating prognostic prediction and immunotherapy strategies

Endoplasmic reticulum stress (ERS) is a critical factor influencing lung adenocarcinoma (LUAD) progression and patient outcomes. In this study, we analyzed gene expression data from LUAD samples sourced from The Cancer Genomic Atlas and Gene Expression Omnibus databases. Utilizing advanced statistical methods including LASSO and Cox regression, we developed a ERS-associated signature (ERAS) based on ten ERS-related genes. This model stratified patients into high- and low-risk groups, with the high-risk group exhibiting decreased survival rates, elevated tumor mutational burden, and heightened chemotherapy sensitivity. Additionally, we observed lower immune and ESTIMATE scores in the high-ERAS group, indicating a potentially compromised immune response. Experimental validation through quantitative real-time polymerase chain reaction confirmed the utility of our model. Furthermore, we constructed a nomogram to predict 1-, 3-, and 5-year survival rates, providing clinicians with a valuable tool for personalized patient management. In conclusion, our study demonstrates the efficacy of the ERAS in identifying high-ERAS LUAD patients, offering promising implications for improved prognostication and treatment strategies.

The endoplasmic reticulum stress-related genes and molecular typing predicts prognosis and reveals characterization of tumor immune microenvironment in lung squamous cell carcinoma

BACKGROUND

Endoplasmic reticulum stress (ERS) acts critical roles on cell growth, proliferation, and metastasis in various cancers. However, the relationship between ERs and lung squamous cell carcinoma (LUSC) prognoses still remains unclear.

METHODS

The consensus clustering analysis of ERS-related genes and the differential expression analysis between clusters were investigated in LUSC based on TCGA database. Furthermore, ERS-related prognostic risk models were constructed by LASSO regression and Cox regression analyses. Then, the predictive effect of the risk model was evaluated by Kaplan-Meier, Cox regression, and ROC Curve analyses, as well as validated in the GEO cohort. According to the optimal threshold, patients with LUSC were divided into high- and low- risk groups, and somatic mutations, immune cell infiltration, chemotherapy response and immunotherapy effect were systematically analyzed.

RESULTS

Two ERS-related clusters were identified in patients with LUSC that had distinct patterns of immune cell infiltration. A 5-genes ERS-related prognostic risk model and nomogram were constructed and validated. Kaplan-Meier curves and Cox regression analysis showed that ERS risk score was an independent prognostic factor (p < 0.001, HR = 1.317, 95% CI = 1.159-1.496). Patients with low-risk scores presented significantly lower TIDE scores and significantly lower IC50 values for common chemotherapy drugs such as cisplatin and gemcitabine.

CONCLUSION

ERS-related risk signature has certain prognostic value and may be a potential therapeutic target and prognostic biomarker for LUSC patients.

Integrated multi-omic analysis and experiment reveals the role of endoplasmic reticulum stress in lung adenocarcinoma

BACKGROUND

Lung cancer is a highly prevalent malignancy worldwide and is associated with high mortality rates. While the involvement of endoplasmic reticulum (ER) stress in the development of lung adenocarcinoma (LUAD) has been established, the underlying mechanism remains unclear.

METHODS

In this study, we utilized data from The Cancer Genome Atlas (TCGA) to identify differentially expressed endoplasmic reticulum stress-related genes (ERSRGs) between LUAD and normal tissues. We performed various bioinformatics analyses to investigate the biological functions of these ERSRGs. Using LASSO analysis and multivariate stepwise regression, we constructed a novel prognostic model based on the ERSRGs. We further validated the performance of the model using two independent datasets from the Gene Expression Omnibus (GEO). Additionally, we conducted functional enrichment analysis, immune checkpoint analysis, and immune infiltration analysis and drug sensitivity analysis of LUAD patients to explore the potential biological function of the model. Furthermore, we conducted a battery of experiments to verify the expression of ERSRGs in a real-world cohort.

RESULTS

We identified 106 ERSRGs associated with LUAD, which allowed us to classify LUAD patients into two subtypes based on gene expression differences. Using six prognostic genes (NUPR1, RHBDD2, VCP, BAK1, EIF2AK3, MBTPS2), we constructed a prognostic model that exhibited excellent predictive performance in the training dataset and was successfully validated in two independent external datasets. The risk score derived from this model emerged as an independent prognostic factor for LUAD. Confirmation of the linkage between this risk model and immune infiltration was affirmed through the utilization of Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The q-PCR results verified significant differences in the expression of prognostic genes between cancer and paracancer tissues. Notably, the protein expression of NUPR1, as determined by immunohistochemistry (IHC), exhibited an opposite pattern compared to the mRNA expression patterns.

CONCLUSION

This study establishes a novel prognostic model for LUAD based on six ER stress-related genes, facilitating the prediction of LUAD prognosis. Additionally, NUPR1 was identified as a potential regulator of stress in LUAD.