Comprehensive Analysis of the Function, Immune Profiles, and Clinical Implication of m1A Regulators in Lung Adenocarcinoma - Bao, Guangyao; Li, Tian; Guan, Xiaojiao; Yao, Yao; Liang, Jie; Xiang, Yifang; Zhong, Xinwen (Frontiers in Oncology, 2022)
Background: Previous studies have demonstrated that transcriptional RNA methyladenosine modification acted as a critical role in cancer occurrence and progression. However, clinical implications of N1-methyladenosine (m1A) regulators and their effect on tumor immunity in lung adenocarcinoma (LUAD) remains largely unknown. Methods: Here, we thoroughly analyzed the characteristics of somatic mutation, copy number variation (CNV), DNA methylation, and expression levels of m1A regulators. We classified 955 lung adenocarcinoma patients into different m1A modification patterns based on an unsupervised consensus clustering algorithm. We then calculated the differences in gene expression, prognosis outcomes, and immune profiles among different m1A clusters. Subsequently, we screened differently expressed genes (DEGs) related to prognosis among different m1A clusters. We identified m1A related gene clusters according to the prognosis-related different expressed genes. We further constructed a scoring standard named the m1A score and comprehensively analyzed the survival outcomes, genetic and clinical-pathological features, immune microenvironment, and treatment responses of immunotherapy, chemotherapy, and targeted therapy sensitivity between high and low m1A score groups. Results: In this study, we totally identified three different m1A modification patterns, named cluster A, B, and C. Cluster A processed the poorest clinical outcomes, the lowest immune cell infiltration rate, and the highest tumor purity score. Then, three m1A gene clusters (gene cluster A, B, C) were speculated. Subsequently, we divided the lung adenocarcinoma patients into high and low m1A score groups with the combined analysis of m1A modification patterns and m1A gene clusters. The low m1A score group was accompanied by higher mortality, higher tumor mutation burden (TMB) and genome mutation frequency, lower programmed cell death-Ligand 1 (PD-L1) expression and tumor immune dysfunction and exclusion (TIDE) expression. Moreover, m1A score was positively correlated with almost all immune cells. Finally, common chemotherapy and targeted therapy drugs showed sensitivity differences between high and low m1A score groups. Conclusions: Collectively, we explored the potential value of m1A regulators in the prognosis and treatment of lung adenocarcinoma in multiple dimensions and provided some preliminary basis for the follow-up study of m1A regulators in lung adenocarcinoma.