Identification of Prognostic Model Based on Immune-Related LncRNAs in Stage I-III Non-Small Cell Lung Cancer - Li, Qiaxuan; Yao, Lintong; Lin, Zenan; Li, Fasheng; Xie, Daipeng; Li, Congsen; Zhan, Weijie; Lin, Weihuan; Huang, Luyu; Wu, Shaowei; Zhou, Haiyu (Frontiers in Oncology, 2021)
Background: Long non-coding RNAs (lncRNAs) participate in regulation of immune response and carcinogenesis, shaping tumor immune microenvironment, which could be utilized in construction of prognostic signature for non-small cell lung cancer (NSCLC) as supplements. Methods: Data of patients with stage I-III NSCLC is downloaded from online databases. The Least Absolute Shrinkage and Selection Operator was performed to construct a lncRNA based prognostic model. Differences in tumor immune microenvironment and pathway were explored between high-risk and low- risk group stratified by the model. We explored the potential association between the model and immunotherapy by the Tumor Immune Dysfunction and Exclusion algorithm. Results: Our study extracted 15 immune-related lncRNAs to construct a prognostic model. Survival analysis suggested better survival probability in low score group training and validation cohorts. Combination of Tumor, Node, and Metastasis staging system with immune-related lncRNAs signature presented higher prognostic efficacy than Tumor, Node, and Metastasis staging system. Single sample gene set enrichment analysis showed higher infiltration abundance in low-risk group, including B cells (p<0.001), activated CD8+ T cells (p<0.01), CD4+ T cells (p<0.001), activated dendritic cells (p<0.01) and CD56+ nature killer cells (p<0.01). Low-risk patients have higher immune score and estimate score significantly by ESTIMATE algorithm. The predicted proportion of responder to immunotherapy was higher in low-risk group. Pathways critical in the model were enriched in immune response, cytoskeleton. Conclusions: Our immune related lncRNA model could describe immune contexture of tumor microenvironment and facilitate clinical therapeutic strategies by improve prognostic efficacy of traditional tumor staging.