Necroptosis-related lncRNAs: biomarkers for predicting prognosis and immune response in lung adenocarcinoma.

IF 4 2区医学 Q2 ONCOLOGY

Translational lung cancer research Pub Date : 2024-10-31 Epub Date: 2024-10-28 DOI:10.21037/tlcr-24-627

Chunxuan Lin, Kunpeng Lin, Xiaochun Lin, Hai Yuan, Yingying Zhang, Zhijun Xie, Yong Dai, Luhao Liu, Yoshihisa Shimada, Taichiro Goto, Katsuhiro Okuda, Taisheng Liu, Chenggong Wei

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引用次数: 0

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the most prevalent types of lung cancer (LC), accounting for 50% of all LC cases. Despite therapeutic advancements, patients suffer from adverse drug reactions. Furthermore, the prognosis of LC patients remains poor. Necroptosis is a novel mode of cell death and is critically involved in regulating immunotherapy in patients. However, the correlation between the necroptosis-related long non-coding RNA (lncRNA) (necro-related lnc) signature (NecroLncSig) and the response of patients with LUAD to immunotherapy is unclear. This study developed a model using lncRNAs to predict the prognosis of patients with LUAD.

Methods: We obtained the transcriptomic and clinical data of LUAD patients from The Cancer Genome Atlas (TCGA) database. Next, we conducted a co-expression analysis to identify the necro-related lnc. In addition, we constructed the NecroLncSig using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then we evaluated and validated the NecroLncSig using a Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, principal component analysis (PCA), Gene Ontology (GO) enrichment analysis, a nomogram, and calibration curves. Finally, we used the NecroLncSig to predict the responses of patients to immunotherapy.

Results: We constructed the NecroLncSig based on seven necro-related lnc. The patients were classified into a high-risk group (HRG) and a low-risk group (LRG). The overall survival (OS) of patients in the HRG was significantly poorer in the training, testing, and entire sets (P<0.05) than that of the patients in the LRG. Univariate and multivariate Cox regression analyses demonstrated that the risk score could predict the OS of patients in an independent manner (P<0.001). Time-dependent ROC analysis demonstrated that the area under the curve values of the NecroLncSig for 1-, 2-, and 3-year OS were 0.689, 0.700, and 0.685, respectively, for the entire set. Furthermore, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm showed that the response of patients in the HRG to immunotherapy was better than that of patients in the LRG.

Conclusions: Necro-related lnc can affect disease progression and patient prognosis. In addition, these lncRNAs can be used to design therapeutic strategies, such as immunotherapy, to treat patients with LUAD.

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坏死相关 lncRNA：预测肺腺癌预后和免疫反应的生物标记物

背景：肺腺癌（LUAD）是最常见的肺癌类型之一，占所有肺癌病例的 50%。尽管治疗手段不断进步，但患者仍会出现药物不良反应。此外，肺癌患者的预后仍然很差。坏死是一种新的细胞死亡模式，在调节患者的免疫疗法中起着至关重要的作用。然而，坏死相关长非编码RNA（lncRNA）（Necro-related lnc）特征（NecroLncSig）与LUAD患者对免疫疗法的反应之间的相关性尚不清楚。本研究利用lncRNAs建立了一个预测LUAD患者预后的模型：我们从癌症基因组图谱（TCGA）数据库中获得了LUAD患者的转录组和临床数据。接下来，我们进行了共表达分析，以确定与坏死相关的lnc。此外，我们还利用单变量和最小绝对收缩与选择算子（LASSO）Cox回归分析构建了NecroLncSig。然后，我们使用 Kaplan-Meier (KM) 生存分析、接收器操作特征曲线 (ROC)、主成分分析 (PCA)、基因本体 (GO) 富集分析、提名图和校准曲线对 NecroLncSig 进行了评估和验证。最后，我们利用 NecroLncSig 预测了患者对免疫疗法的反应：结果：我们根据七个与坏死相关的 lnc 构建了 NecroLncSig。结果：我们根据七种坏死相关 lnc 构建了 NecroLncSig，并将患者分为高危组（HRG）和低危组（LRG）。在训练集、测试集和整个集中，HRG 患者的总生存率（OS）明显较低（PConclusions：与坏死相关的 lnc 可影响疾病进展和患者预后。此外，这些lncRNA可用于设计治疗策略，如免疫疗法，以治疗LUAD患者。

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

Translational lung cancer research Medicine-Oncology

CiteScore

7.20

自引率

2.50%

发文量

137

期刊介绍： Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.