Related Experiment Videos
Prognostic model of endoplasmic reticulum stress-related lncRNAs in lung adenocarcinoma: Construction and validation
Haiyang Li1, Zhenshan Zhao2, Jing Li1
1Department of Medical Oncology, Kailuan General Hospital, Tangshan, China.
Background:
Lung adenocarcinoma (LUAD) ranks among the deadliest malignancies worldwide. The endoplasmic reticulum (ER) stress response plays a critical role in the pathogenesis of various cancers, and long non-coding RNAs (lncRNAs) are known for their regulatory roles in gene expression and disease progression.
Objectives:
To construct and validate a prognostic model based on ER stress-related lncRNAs in LUAD.
Material And Methods:
The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used. Utilizing the Molecular Signatures Database (MSigDB), we identified ER stress-related mRNAs and lncRNAs. Weighted gene co-expression network analysis (WGCNA) was employed to identify genes associated with LUAD prognosis. An lncRNA-based prognostic risk scoring model was constructed using univariate and least absolute shrinkage and selection operator (LASSO) regression analyses and independently validated. Consensus clustering analysis was applied to define risk subgroups, optimizing the risk scoring system. The model's performance was evaluated using receiver operating characteristic (ROC) curves and nomograms. Differentially expressed gene (DEG) and enrichment analyses were performed to investigate the biological relevance of the risk score. Additionally, the relationships between risk scores, immune infiltration, and the tumor microenvironment (TME) were explored.
Results:
Using WGCNA, we successfully identified genes strongly associated with ER stress in LUAD prognosis. A prognostic model comprising 13 signature genes was developed, demonstrating robust discrimination between highand low-risk patients, with the high-risk group exhibiting reduced overall survival (OS). The model's predictive accuracy was confirmed through Kaplan-Meier and ROC analyses. Correlation analysis between risk scores and immune infiltration indicated that the model reflects the immune landscape of LUAD. Subgroup analysis using consensus clustering (C1 and C2) revealed more pronounced differences in immune cell dynamics than the binary risk score classification alone.
Conclusions:
This study introduces a novel prognostic model based on the co-expression of ER stress-related lncRNAs in LUAD.
