Zhang Liren, Yang Lei, Chen Xiaobo, Huang Qiubo, Ouyang Zhiqiang, Wang Ran, Xiang Bingquan, Lu Hong, Ren Wenjun, Wang Ping
Department of Thoracic Surgery, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
Department of Traditional Chinese Medicine Rehabilitation Medicine, The Affiliated Calmette Hospital of Kunming Medical University, The First People's Hospital of Kunming, Kunming, China.
Transl Cancer Res. 2025 Feb 28;14(2):761-777. doi: 10.21037/tcr-24-1085. Epub 2025 Feb 24.
BACKGROUND: Lung adenocarcinoma (LUAD) is a common type of lung cancer and one of the leading causes of cancer death worldwide. Long non-coding RNAs (lncRNAs) play a crucial role in tumors. The purpose of this study was to explore the expression of lncRNAs associated with RNA methylation modification and their prognostic value in LUAD. METHODS: The RNA sequencing and clinical data were downloaded from The Cancer Genome Atlas dataset, and the messenger RNA and lncRNAs were annotated by Ensemble. The lncRNAs related to RNA methylation regulators (RMlncRNAs) were filtered by Pearson correlation analysis between differentially expressed lncRNAs and RNA methylation regulators. Univariate Cox regression analysis, multivariate Cox regression analysis, and least absolute shrinkage and selection operator regression analysis were used to construct a prognostic model. The receiver operating characteristic curve (ROC) was plotted to validate the predictive value of the prognostic model. Then, tumor mutational burden (TMB) and microsatellite instability were used to compare the immunotherapy response. Finally, to perform a drug sensitivity analysis, the half-maximal inhibitory concentration (IC) of targeted drugs was calculated using pRRophetic package. RESULTS: In total, 18 RMlncRNAs associated with the prognosis of LUAD patients were identified. Then, six feature lncRNAs (, , , , , and ) were used to construct a prognostic model. The ROC curves for training, testing, and validation sets showed that the prognosis model was effective. The subindex based on the prognostic model had a high correlation with TMB. The high-risk group might be subject to greater immune resistance according to the comparison of Tumor Immune Dysfunction and Exclusion scores. Finally, the IC of 11 drugs had differences between high- and low-risk group, and only three of the drug's target genes (, , and ) were differentially expressed. CONCLUSIONS: In conclusion, a prognostic model based on six feature lncRNAs (, , , , , and ) was constructed by bioinformatics analysis, which might provide a new insight into the evaluation and treatment of LUAD.
背景：肺腺癌（LUAD）是一种常见的肺癌类型，也是全球癌症死亡的主要原因之一。长链非编码RNA（lncRNAs）在肿瘤中起着至关重要的作用。本研究的目的是探讨与RNA甲基化修饰相关的lncRNAs在LUAD中的表达及其预后价值。 方法：从癌症基因组图谱数据集中下载RNA测序和临床数据，并通过Ensemble注释信使RNA和lncRNAs。通过差异表达lncRNAs与RNA甲基化调节因子之间的Pearson相关分析筛选与RNA甲基化调节因子相关的lncRNAs（RMlncRNAs）。采用单因素Cox回归分析、多因素Cox回归分析和最小绝对收缩和选择算子回归分析构建预后模型。绘制受试者工作特征曲线（ROC）以验证预后模型的预测价值。然后，使用肿瘤突变负荷（TMB）和微卫星不稳定性来比较免疫治疗反应。最后，为了进行药物敏感性分析，使用pRRophetic软件包计算靶向药物的半数最大抑制浓度（IC）。 结果：共鉴定出18种与LUAD患者预后相关的RMlncRNAs。然后，使用6种特征性lncRNAs（， ， ， ， ，和 ）构建预后模型。训练集、测试集和验证集的ROC曲线表明预后模型有效。基于预后模型的亚指标与TMB高度相关。根据肿瘤免疫功能障碍和排除评分的比较，高危组可能具有更强的免疫抗性。最后，11种药物的IC在高危组和低危组之间存在差异，且只有3种药物的靶基因（ ， ，和 ）存在差异表达。 结论：总之，通过生物信息学分析构建了基于6种特征性lncRNAs（ ， ， ， ， ，和 ）的预后模型，这可能为LUAD的评估和治疗提供新的见解。
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