A novel algorithm for lung adenocarcinoma based on N6 methyladenosine-related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti-tumor therapies

Abstract

Background: Lung cancer is a highly heterogeneous malignant tumor with high incidence and mortality. Recently, increasing evidence has demonstrated that N6-methyladenosine (m6A) methylation and the tumor microenvironment (TME) play important roles in the occurrence and development of lung adenocarcinoma (LUAD). Methods: In this study, we constructed a novel and reliable algorithm based on m6A-related immune lncRNAs (mrilncRNAs), consisting of molecular subtypes and a prognostic signature. Results: According to the analyses of molecular subtypes, patients in cluster 1 were in a more advanced stage, showed poor prognosis, were sensitive to immunotherapy (anti-programmed cell death 1 Ligand 1 (PD-L1) and anti-lymphocyte activating 3 (LAG-3)), and had a highest tumor mutational burden (TMB), while anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy seemed to be a good choice for patients in cluster 3. Subsequently, the results of the risk assessment model indicated that the low-risk patients exhibited a survival advantage, had an earlier stage, and showed a higher response to common anti-cancer drugs, including chemotherapy (Docetaxel, Paclitaxel), molecular targeted therapy (Erlotinib), and immunotherapy (anti-CTLA-4 therapy), while Gefitinib could be a good choice for patients with high-risk scores. Conclusion: In conclusion, the constructed algorithm exhibits promising practical prospects, and allows the selection of suitable and sensitive anti-cancer drugs, which could provide theoretical support to predict the survival outcomes of patients with LUAD.