A Glutathione Metabolism-Related Transcriptomic Signature for Prognostic Assessment and Biological Characterization of Lung Adenocarcinoma.

IF 2.1 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2026-08-04 eCollection Date: 2026-01-01 DOI:10.1177/11769351261476162
Zhengzuo Sheng, Siyu Chen, Su Chen
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引用次数: 0

Abstract

Background: Glutathione metabolism plays an important role in redox homeostasis, oxidative stress responses, and metabolic adaptation in cancer. However, its prognostic significance in lung adenocarcinoma (LUAD) remains incompletely understood. This study aimed to develop a glutathione metabolism-related prognostic signature and investigate its associations with immune characteristics, genomic alterations, and biological pathways in LUAD.

Methods: Transcriptomic, clinical, and somatic mutation data from TCGA-LUAD were analyzed, and GSE50081 was used as an independent validation cohort. Glutathione metabolism-related genes were identified through differential expression and Cox regression analyses, followed by least absolute shrinkage and selection operator (LASSO) Cox regression to construct a prognostic signature. Survival analysis, time-dependent receiver operating characteristic (ROC) analysis, Cox regression, immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), mutation profiling, tumor mutation burden (TMB) analysis, and drug-sensitivity prediction were subsequently performed.

Results: A glutathione metabolism-related signature stratified patients into high- and low-risk groups with significantly different overall survival in both the TCGA training cohort and the GSE50081 validation cohort. The risk score remained an independent prognostic factor in multivariable Cox regression analysis. High-risk tumors exhibited reduced B-cell and dendritic-cell infiltration, enrichment of cell-cycle- and metabolism-related pathways, higher frequencies of TP53 and KEAP1 mutations, and elevated tumor mutation burden. Computational drug-sensitivity analysis identified differences in predicted responses to several therapeutic agents between risk groups.

Conclusions: The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD. These findings provide additional insights into glutathione metabolism-related heterogeneity in LUAD and warrant further biological and clinical validation.

肺腺癌预后评估和生物学特性的谷胱甘肽代谢相关转录组特征。
背景:谷胱甘肽代谢在癌症的氧化还原稳态、氧化应激反应和代谢适应中起重要作用。然而,其在肺腺癌(LUAD)中的预后意义仍不完全清楚。本研究旨在开发谷胱甘肽代谢相关的预后特征,并研究其与LUAD的免疫特性、基因组改变和生物学途径的关系。方法:分析TCGA-LUAD的转录组学、临床和体细胞突变数据,并以GSE50081作为独立验证队列。通过差异表达和Cox回归分析鉴定谷胱甘肽代谢相关基因,然后通过最小绝对收缩和选择算子(LASSO) Cox回归构建预后特征。随后进行生存分析、时间依赖性受试者工作特征(ROC)分析、Cox回归、免疫浸润分析、基因集富集分析(GSEA)、突变谱分析、肿瘤突变负担(TMB)分析和药物敏感性预测。结果:在TCGA训练队列和GSE50081验证队列中,谷胱甘肽代谢相关特征将患者分层为高风险和低风险组,总生存率显著不同。在多变量Cox回归分析中,风险评分仍然是一个独立的预后因素。高危肿瘤表现为b细胞和树突状细胞浸润减少,细胞周期和代谢相关通路富集,TP53和KEAP1突变频率较高,肿瘤突变负担升高。计算药物敏感性分析确定了不同风险组对几种治疗药物的预测反应的差异。结论:提出的谷胱甘肽代谢相关特征在训练和验证队列中都显示了预后价值,并且与LUAD的免疫特征、途径富集模式、基因组改变和肿瘤突变负担相关。这些发现为LUAD中谷胱甘肽代谢相关异质性提供了额外的见解,并需要进一步的生物学和临床验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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