Department of Respiration, Zhejiang Jinhua Guangfu Hospital, Jinhua, China.
Eur Rev Med Pharmacol Sci. 2020 May;24(10):5456-5464. doi: 10.26355/eurrev_202005_21330.
The study aims to construct a multi-gene risk scoring model that can be used to predict the prognosis of patients with lung squamous cell carcinoma (LUSC).
RNA-seq data from 494 LUSC tumor samples and 49 peripheral lung tissue samples were obtained from TCGA_LUSC database. Differential analysis was conducted using edgeR to screen differentially expressed lncRNAs (DElncRNAs) between LUSC and normal samples. Univariate Cox regression analysis was used to screen lncRNAs that were significantly correlated with LUSC prognosis. LASSO regression model was built to reduce complexity. The LUSC prognostic model based on lncRNAs was established by multivariate Cox regression analysis, which was evaluated by ROC curves and survival analysis. ROC and Kaplan-Meier survival curves of each lncRNA in the model were plotted and compared.
2085 DElncRNAs were identified. Combined with univariate Cox regression analysis, 342 prognosis-related genes were screened. After LASSO regression analysis, 11 lncRNAs tightly related to LUSC prognosis were identified and a risk scoring model was constructed. ROC curve analysis proved the good performance of the model. The Kaplan-Meier survival curve showed that the mortality in high-risk group was significantly higher. The survival analysis results of each lncRNA were also consistent with the prediction in Cox regression.
Our results suggested that the 11-lncRNA risk scoring model may provide a new insight for predicting prognosis of LUSC patients.
本研究旨在构建一个多基因风险评分模型，用于预测肺鳞状细胞癌（LUSC）患者的预后。
从 TCGA_LUSC 数据库中获取 494 例 LUSC 肿瘤样本和 49 例外周肺组织样本的 RNA-seq 数据。使用 edgeR 进行差异分析，筛选 LUSC 与正常样本之间差异表达的长非编码 RNA（DElncRNAs）。采用单因素 Cox 回归分析筛选与 LUSC 预后显著相关的 lncRNAs。构建 LASSO 回归模型以降低复杂性。通过多因素 Cox 回归分析建立基于 lncRNAs 的 LUSC 预后模型，通过 ROC 曲线和生存分析进行评估。绘制并比较模型中每个 lncRNA 的 ROC 和 Kaplan-Meier 生存曲线。
鉴定出 2085 个 DElncRNAs。结合单因素 Cox 回归分析，筛选出 342 个与预后相关的基因。经过 LASSO 回归分析，确定了 11 个与 LUSC 预后密切相关的 lncRNAs，并构建了风险评分模型。ROC 曲线分析证明了该模型的良好性能。Kaplan-Meier 生存曲线显示，高危组的死亡率明显更高。每个 lncRNA 的生存分析结果也与 Cox 回归的预测一致。
我们的结果表明，该 11-lncRNA 风险评分模型可能为预测 LUSC 患者的预后提供新的思路。
