Abstract
Lung squamous cell carcinoma (LUSC) is the most common type of lung cancer accounting for 40% to 51%. Long noncoding RNAs (lncRNAs) have been reported to play a significant role in the invasion, migration, and proliferation of lung cancer tissue cells. However, systematic identification of lncRNA signatures and evaluation of the prognostic value for LUSC are still an urgent problem. In this work, LUSC RNA-seq data were collected from TCGA database, and the limma R package was used to screen differentially expressed lncRNAs (DElncRNAs). In total, 216 DElncRNAs were identified between the LUSC and normal samples. lncRNAs associated with prognosis were calculated using univariate Cox regression analysis. The overall survival (OS) prognostic model containing 10 lncRNAs and the disease-free survival (DFS) prognostic model consisting of 11 lncRNAs were constructed using a machine learning-based algorithm, systematic LASSO-Cox regression analysis. We found that the survival rate of samples in the high-risk group was lower than that in the low-risk group. Results of ROC curves showed that both the OS and DFS risk score had better prognostic effects than the clinical characteristics, including age, stage, gender, and TNM. Two lncRNAs (LINC00519 and FAM83A-AS1) that were commonly identified as prognostic factors in both models could be further investigated for their clinical significance and therapeutic value. In conclusion, we constructed lncRNA prognostic models with considerable prognostic effect for both OS and DFS of LUSC.
Highlights
Lung cancer is one of the most common types of cancer
Univariate Cox regression analysis was applied for the clinical data and the Long noncoding RNAs (lncRNAs) expression data using the survival R package. lncRNAs related to overall survival (OS) and disease-free survival (DFS) were separately screened with the P value of 0.05 as the threshold. en, a machine learning-based algorithm, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, was used to screen a panel of lncRNAs that were significantly related to OS and DFS
The lung squamous cell carcinoma (LUSC) samples were randomly divided into a training set and a test set at a ratio of 1 : 1, and 10-fold cross-validation was performed to tune lncRNAs related to OS and DFS in the training set
Summary
Lung cancer is one of the most common types of cancer. In 2018, lung cancer accounted for 11.6% of global cancer [1], and more than 1,600,000 new cases are diagnosed yearly [2]. Despite advances in the treatment methods of LUSC, the mortality is still high, and the 5-year overall survival (OS). Rate of LUSC patients with clinical I and II stages is about 40%. The 5-year OS rate for the stage III–IV LUSC patients is less than 5% [6, 7]. The basic methods for assessing the diagnosis and prognosis of LUSC are based on disease stage and histological grade
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