Hu Junxi, Tian Shuyu, Liu Qingwen, Hou Jiaqi, Wu Jun, Wang Xiaolin, Shu Yusheng
Clinical Medical College, Yangzhou University, Yangzhou, China.
Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou, China.
Front Immunol. 2025 Feb 10;16:1477437. doi: 10.3389/fimmu.2025.1477437. eCollection 2025.
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.
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.
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. cell experiment results showed that AL162632.3 was markedly upregulated, while AC026355.2 tended to be downregulated across these cell lines. Ultimately, suppressing lnc-AL162632.3 markedly reduced the growth, mobility, and invasiveness of lung cancer cells.
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.
谷胱甘肽（GSH）代谢维持肿瘤的氧化还原平衡并影响耐药性，而长链非编码RNA（lncRNA）影响肺腺癌（LUAD）的进展。本研究利用与GSH相关的lncRNA建立了一个预后模型，以预测LUAD的预后并评估肿瘤免疫情况。
本研究分析了来自癌症基因组图谱（TCGA）的生存数据，并使用Pearson相关性分析确定了与GSH代谢相关的lncRNA。采用Cox回归和最小绝对收缩与选择算子（LASSO）方法构建预后模型，并通过Kaplan-Meier分析、受试者工作特征（ROC）曲线和主成分分析（PCA）进行验证。功能分析揭示了免疫浸润和药物敏感性差异。定量PCR和实验研究证实了lnc-AL162632.3在LUAD中的作用。
我们的模型共纳入9个lncRNA，分别为AL162632.3、AL360270.1、LINC00707、DEPDC1-AS1、GSEC、LINC01711、AL078590.2、AC026355.2和AL096701.4。该模型有效地预测了患者的生存情况，纳入其他临床风险因素的列线图充分满足了临床需求。根据模型评分对患者进行分层，结果显示不同组之间在免疫细胞组成、功能和突变方面存在显著差异。此外，顺铂、多西他赛和紫杉醇等关键肺癌药物的半数抑制浓度（IC50）值也存在差异。细胞实验结果表明，在这些细胞系中，AL162632.3明显上调，而AC026355.2则趋于下调。最终，抑制lnc-AL162632.3显著降低了肺癌细胞的生长、迁移和侵袭能力。
本研究确定了与GSH代谢相关的lncRNA是LUAD的关键预后因素，并建立了一个风险分层模型。高危患者显示出肿瘤突变负荷（TMB）和干性增加，强调了个性化免疫治疗改善生存结局的潜力。
