m7G-related miRNA signature for prediction of prognosis in lung adenocarcinoma

Abstract

Abstract Background N7-methylguanosine (m7G), one of the most conserved nucleotide modifications, presents in mRNA caps and internal sites of tRNAs and rRNAs. Previous data have demonstrated that abnormal m7G is associated with tumorigenesis. Notably, the expression of m7G’s regulators remains unknown in human cancers, especially in lung adenocarcinoma (LUAD). This study aimed to construct a prognostic signature based on m7G-related miRNAs in LUAD and to explore the potential association of the regulators with tumor immune microenvironment (TIME). Methods We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed functional enrichment analysis and defined the immune landscape as well as drug response profile in the high-risk and low-risk subgroups. Results This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of 1-year, 3-year and 5-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. Conclusion We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into molecular mechanisms of m7G in LUAD.