Abstract
BackgroundRecent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).MethodsRNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.ResultsA total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.ConclusionsWe have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.
Highlights
Lung cancer belongs to the malignant tumor group and has becoming the primary killer in tumor-related disease [1, 2]
A total of 14,144 lncRNAs sequencing data were obtained from The Cancer Genome Atlas (TCGA) database and 331 immune genes were detected from Molecular Signatures Database (MSigDB) [20]
Immune-related lncRNAs were received by building the immune lncRNAs co-expression network through the “limma package” in R studio and Cytoscape3.7.1 (Figure 2A).The co-expression network refers to the relationship between the immune genes and the lncRNAs
Summary
Lung cancer belongs to the malignant tumor group and has becoming the primary killer in tumor-related disease [1, 2]. A study shows the relationship between the lncRNA signature of tumorinfiltrating B lymphocytes and the immune therapies of bladder cancer [11]. Song et al found that a gene signature including 30 immune-related genes could predict prognosis and reveal the relationship between the tumor and the immune microenvironment [15]. Li et al found that the clinical immune signature can act as a conspicuous marker to evaluate the overall survival rate in NSCLC and patients in the early phase [16]. Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD)
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