ArticleFrontiers in immunology2025
A prognostic signature of Glutathione metabolism-associated long non-coding RNAs for lung adenocarcinoma with immune microenvironment insights.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A Glutathione Metabolism-Related Transcriptomic Signature for Prognostic Assessment and Biological Characterization of Lung Adenocarcinoma.Cancer informatics · 2026Article
- Immune-related long noncoding RNAs in predicting the prognosis and immune landscape of intrahepatic cholangiocarcinoma: a bioinformatics analysis with experimental verification.Translational cancer research · 2025Article
- LncRNA DEPDC1-AS1 drives the progression of endometrial carcinoma by regulating miR-508-3p.Discover oncology · 2025Article
- Multi-omics analysis reveals glutathione metabolism-related immune suppression and constructs a prognostic model in lung adenocarcinoma.Frontiers in immunology · 2025Article
- Metabolic reprogramming-driven stratification and therapeutic targeting in lung adenocarcinoma: implications for prognosis and personalized treatment.Frontiers in oncology · 2025Article
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7 authors.
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Abstract
Background: Glutathione (GSH) metabolism supports tumor redox balance and drug resistance, while long non-coding RNAs (lncRNAs) influence lung adenocarcinoma (LUAD) progression. This study developed a prognostic model using GSH-related lncRNAs to predict LUAD outcomes and assess tumor immunity. Methods: This study analyzed survival data from The Cancer Genome Atlas (TCGA) and identified GSH metabolism-related lncRNAs using Pearson correlation. A prognostic model was built with Cox and Least Absolute Shrinkage and Selection Operator (LASSO) methods and validated by Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, and Principal Component Analysis (PCA). Functional analysis revealed immune infiltration and drug sensitivity differences. Quantitative PCR and experimental studies confirmed the role of lnc-AL162632.3 in LUAD. Results: Our model included a total of nine lncRNAs, namely AL162632.3, AL360270.1, LINC00707, DEPDC1-AS1, GSEC, LINC01711, AL078590.2, AC026355.2, and AL096701.4. The model effectively forecasted patient survival, and the nomogram, incorporating additional clinical risk factors, satisfied clinical needs adequately. Patient stratification based on model scores revealed significant disparities in immune cell composition, functionality, and mutations between groups. Additionally, variations were noted in the IC50 values for key lung cancer medications such as Cisplatin, Docetaxel, and Paclitaxel. Conclusion: This study identified GSH metabolism-related lncRNAs as key prognostic factors in LUAD and developed a model for risk stratification. High-risk patients showed increased tumor mutation burden (TMB) and stemness, emphasizing the potential of personalized immunotherapy to improve survival outcomes.
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