Abstract
BACKGROUND: As the most abundant modification in mRNA, the N(6)-methyladenosine (m(6)A) RNA modification is involved in the occurrence and development of various tumors. However, the underlying functions of this alteration in the immune microenvironment of lung adenocarcinoma (LUAD) remain unknown. METHODS: We identified m(6)A-mediated immune genes by performing a correlation analysis. Next, a m(6)A-mediated immune model was constructed using multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The potential of this model to predict the immune landscapes, drug sensitivities, and immunotherapy responses of different LUAD risk groups was studied. RESULTS: A m(6)A-mediated immune model containing 13 m(6)A-mediated immune genes was established and found to be an independent predictor of survival time. The prognosis of low-risk patients was significantly better than that of high-risk patients. These two risk groups displayed different immune environments, genomic backgrounds, chemotherapy responses and immunotherapy response tendencies. The low- and high-risk groups strongly corresponded to the immune-hot and immune-cold phenotypes, respectively. The low-risk group was more enriched in immune-related biological processes, and the high-risk group was more enriched in proliferation-related biological processes. Furthermore, low-risk patients responded better to immunotherapy based on the results obtained from the tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping algorithm using five external independent immunotherapy cohorts. CONCLUSIONS: Our results suggest that the m(6)A modification participates in regulating the tumor microenvironment. The m(6)A-mediated immune model may be useful to predict the immunotherapy responses and outcomes of patients with LUAD.