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A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma

INTERNATIONAL JOURNAL OF GENERAL MEDICINE(2021)

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摘要
Purpose: Long noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD. Patients and Methods: We retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas. Results: We identified three LUAD subtypes (clusters 1-3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyteassociated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso-Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases. Conclusion: The LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.
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关键词
bioinformatics,expression,prognosis,regression
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