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一种用于预测肺腺癌预后、免疫浸润和免疫治疗反应的新型铜死亡相关长链非编码RNA定义风险特征。

A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma.

作者信息

Ma Chao, Li Feng, Gu Zhuoyu, Yang Yang, Qi Yu

机构信息

Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

出版信息

Front Pharmacol. 2023 Aug 21;14:1146840. doi: 10.3389/fphar.2023.1146840. eCollection 2023.

Abstract

Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor immunity formation and patient-tailored treatment optimization of lung adenocarcinoma (LUAD) is still unclear. RNA sequencing and survival data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database for model training. The patients with LUAD in GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081 were used for validation. The proofed cuproptosis-related genes were extracted from the previous studies. The Pearson correlation was applied to select cuproptosis-related lncRNAs. We chose differentially expressed cuproptosis-related lncRNAs in the tumor and normal tissues and allowed them to go to a Cox regression and a LASSO regression for a lncRNA signature that predicts the LUAD prognosis. Kaplan-Meier estimator, Cox model, ROC, tAUC, PCA, nomogram predictor, decision curve analysis, and real-time PCR were further deployed to confirm the model's accuracy. We examined this model's link to other regulated cell death forms. Applying TMB, immune-related signatures, and TIDE demonstrated the immunotherapeutic capabilities of signatures. We evaluated the relationship of our signature to anticancer drug sensitivity. GSEA, immune infiltration analysis, and function experiments further investigated the functional mechanisms of the signature and the role of immune cells in the prognostic power of the signature. An eight-lncRNA signature (TSPOAP1-AS1, AC107464.3, AC006449.7, LINC00324, COLCA1, HAGLR, MIR4435-2HG, and NKILA) was built and demonstrated owning prognostic power by applied to the validation cohort. Each signature gene was confirmed differentially expressed in the real world by real-time PCR. The eight-lncRNA signature correlated with 2321/3681 (63.05%) apoptosis-related genes, 11/20 (55.00%) necroptosis-related genes, 34/50 (68.00%) pyroptosis-related genes, and 222/380 (58.42%) ferroptosis-related genes. Immunotherapy analysis suggested that our signature may have utility in predicting immunotherapy efficacy in patients with LUAD. Mast cells were identified as key players that support the predicting capacity of the eight-lncRNA signature through the immune infiltrating analysis. In this study, an eight-lncRNA signature linked to cuproptosis was identified, which may improve LUAD management strategies. This signature may possess the ability to predict the effect of LUAD immunotherapy. In addition, infiltrating mast cells may affect the signature's prognostic power.

摘要

铜死亡是一种新发现的非凋亡性细胞死亡形式,可能与肿瘤的发生发展有关。然而,铜死亡相关lncRNAs在肺腺癌(LUAD)肿瘤免疫形成和患者个体化治疗优化中的潜在作用仍不清楚。从癌症基因组图谱(TCGA)数据库下载LUAD患者的RNA测序和生存数据用于模型训练。GSE29013、GSE30219、GSE31210、GSE37745和GSE50081中的LUAD患者用于验证。从先前的研究中提取已证实的铜死亡相关基因。应用Pearson相关性来选择铜死亡相关lncRNAs。我们选择肿瘤组织和正常组织中差异表达的铜死亡相关lncRNAs,并将它们进行Cox回归和LASSO回归,以获得预测LUAD预后的lncRNA特征。进一步采用Kaplan-Meier估计、Cox模型、ROC、tAUC、PCA、列线图预测器、决策曲线分析和实时PCR来确认模型的准确性。我们研究了该模型与其他调节性细胞死亡形式的联系。应用肿瘤突变负荷(TMB)、免疫相关特征和肿瘤免疫功能障碍和排除(TIDE)来证明特征的免疫治疗能力。我们评估了我们的特征与抗癌药物敏感性的关系。基因集富集分析(GSEA)、免疫浸润分析和功能实验进一步研究了特征的功能机制以及免疫细胞在特征预后能力中的作用。构建了一个由八个lncRNA组成的特征(TSPOAP1-AS1、AC107464.3、AC006449.7、LINC00324、COLCA1、HAGLR、MIR4435-2HG和NKILA),并通过应用于验证队列证明其具有预后能力。通过实时PCR在现实世界中证实每个特征基因均差异表达。这八个lncRNA特征与2321/3681(63.05%)的凋亡相关基因、11/20(55.00%)的坏死性凋亡相关基因、34/50(68.00%)的焦亡相关基因和222/380(58.42%)的铁死亡相关基因相关。免疫治疗分析表明,我们的特征可能有助于预测LUAD患者的免疫治疗疗效。通过免疫浸润分析确定肥大细胞是支持八个lncRNA特征预测能力的关键因素。在本研究中,鉴定了一个与铜死亡相关的八个lncRNA特征,这可能会改善LUAD的管理策略。该特征可能具有预测LUAD免疫治疗效果的能力。此外,浸润的肥大细胞可能会影响特征的预后能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b26a/10475834/efd81068b993/fphar-14-1146840-g001.jpg

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