International Journal of General Medicine (Nov 2021)
A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma
Abstract
Na Li,1 Mu Su,2 Louyin Zhu,2 Li Wang,2 Yonggang Peng,2 Bo Dong,1 Liya Ma,1 Yongyu Liu3 1Department of Central Laboratory, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People’s Republic of China; 2Berry Oncology Corporation, Beijing, People’s Republic of China; 3Department of Thoracic Surgery, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, Shenyang, 110044, Liaoning, People’s Republic of ChinaCorrespondence: Yongyu LiuDepartment of Thoracic Surgery, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, No. 11 Beihai Street, Dadong District, Shenyang, 110044, Liaoning, People’s Republic of ChinaTel/Fax +86 024 8832 0630Email [email protected]: 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-lymphocyte-associated 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.Keywords: bioinformatics, expression, prognosis, regression
