Identification of Prognostic Model Based on Immune-Related LncRNAs in Stage I-III Non-Small Cell Lung Cancer

• Published in: Frontiers in oncology 2021-10, Vol.11, p.706616-706616
• Main Authors: Li, Qiaxuan, Yao, Lintong, Lin, Zenan, Li, Fasheng, Xie, Daipeng, Li, Congsen, Zhan, Weijie, Lin, Weihuan, Huang, Luyu, Wu, Shaowei, Zhou, Haiyu
• Format: Article
• Language: English
• Subjects: immune; long non-coding RNA; non-small cell lung cancer; Oncology; prognostic model; tumor microenvironment
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2021.706616

Long non-coding RNAs (lncRNAs) participate in the regulation of immune response and carcinogenesis, shaping tumor immune microenvironment, which could be utilized in the construction of prognostic signatures for non-small cell lung cancer (NSCLC) as supplements. Data of patients with stage I-III NSCLC was downloaded from online databases. The least absolute shrinkage and selection operator was used to construct a lncRNA-based prognostic model. Differences in tumor immune microenvironments and pathways were explored for high-risk and low-risk groups, stratified by the model. We explored the potential association between the model and immunotherapy by the tumor immune dysfunction and exclusion algorithm. Our study extracted 15 immune-related lncRNAs to construct a prognostic model. Survival analysis suggested better survival probability in low-risk group in training and validation cohorts. The combination of tumor, node, and metastasis staging systems with immune-related lncRNA signatures presented higher prognostic efficacy than tumor, node, and metastasis staging systems. Single sample gene set enrichment analysis showed higher infiltration abundance in the low-risk group, including B cells (p