Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), 54 Youdian Road, Hangzhou, China.
The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
BMC Med Genomics. 2022 Jan 29;15(1):15. doi: 10.1186/s12920-022-01164-5.
Determining the prognosis of lung adenocarcinoma (LUAD) is challenging. The present study aimed to identify prognostic ferroptosis-related long noncoding RNAs (FRLs) and construct a prognostic model. Moreover, differential analysis of immune and N6-methyladenosine (m6A)-related genes was systematically conducted.
A total of 504 patients selected from a dataset from The Cancer Genome Atlas were included. The patients with LUAD were randomly divided into a training group and a test group at a ratio of 1:1. Pearson correlation analysis and univariate Cox regression analysis were used to identify the prognostic FRLs. Then, a prognostic model was constructed from the optimized subset of prognostic FRLs based on the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, the receiver operating characteristic (ROC) curve and survival analysis were used to evaluate the performance of the model. The risk score based on the prognostic model was analyzed using Cox regression analysis. Moreover, gene set enrichment analysis and differential analysis of immune- and m6A-related genes were conducted.
After univariate Cox regression analysis and LASSO algorithm analysis, a total of 19 prognostic FRLs were selected to construct the final model to obtain the risk score. The area under the ROC curve of the prognostic model for 1-year, 3-year, and 5-year overall survival (OS) was 0.763, 0.745, and 0.778 in the training set and 0.716, 0.724, and 0.736 in the validation set, respectively. Moreover, the OS of the high-risk group was significantly worse than that of the low-risk group in the training group (P < 0.001) and in the test group (P < 0.001). After univariate and multivariate Cox regression analysis, the risk score [hazard ratio (HR) = 1.734; P < 0.001] and stage (HR = 1.557; P < 0.001) were both considered significant prognostic factors for LUAD. A nomogram was constructed based on clinical features and risk score. The expression of 34 checkpoint genes and 13 m6A-related genes varied significantly between the two risk groups.
This study constructed a prognostic model to effectively predict the OS of patients with LUAD, and these OS-related FRLs might serve as potential therapeutic targets of LUAD.
确定肺腺癌（LUAD）的预后具有挑战性。本研究旨在确定与铁死亡相关的长链非编码 RNA（FRL）的预后，并构建预后模型。此外，还对免疫和 N6-甲基腺苷（m6A）相关基因的差异分析进行了系统研究。
从癌症基因组图谱（TCGA）的数据集共纳入 504 例 LUAD 患者。将 LUAD 患者随机分为训练组和测试组，比例为 1:1。采用 Pearson 相关性分析和单因素 Cox 回归分析筛选与预后相关的 FRL。然后，基于最小绝对收缩和选择算子（LASSO）算法，从优化的预后 FRL 子集中构建预后模型。随后，采用接收者操作特征（ROC）曲线和生存分析评估模型的性能。基于预后模型的风险评分采用 Cox 回归分析进行分析。此外，进行了基因集富集分析和免疫及 m6A 相关基因的差异分析。
经过单因素 Cox 回归分析和 LASSO 算法分析，筛选出 19 个与预后相关的 FRL 构建最终模型，获得风险评分。该预后模型在训练集的 1 年、3 年和 5 年总生存（OS）的 ROC 曲线下面积分别为 0.763、0.745 和 0.778，在验证集分别为 0.716、0.724 和 0.736。此外，在训练组（P<0.001）和测试组（P<0.001）中，高风险组的 OS 明显差于低风险组。经过单因素和多因素 Cox 回归分析，风险评分（HR=1.734；P<0.001）和分期（HR=1.557；P<0.001）均被认为是 LUAD 的显著预后因素。基于临床特征和风险评分构建了一个列线图。两组间 34 个检查点基因和 13 个 m6A 相关基因的表达差异显著。
本研究构建了一个预测 LUAD 患者 OS 的预后模型，这些与 OS 相关的 FRL 可能成为 LUAD 的潜在治疗靶点。
