An 11-lncRNA risk scoring model predicts prognosis of lung squamous cell carcinoma.

OBJECTIVE: 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). PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: Our results suggested that the 11-lncRNA risk scoring model may provide a new insight for predicting prognosis of LUSC patients.