Abstract
Background: Acute myelocytic leukemia (AML) is one of the hematopoietic cancers with an unfavorable prognosis. However, the prognostic value of N 6-methyladenosine-associated long non-coding RNAs (lncRNAs) in AML remains elusive. Materials and Methods: The transcriptomic data of m6A-related lncRNAs were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. AML samples were classified into various subgroups according to the expression of m6A-related lncRNAs. The differences in terms of biological function, tumor immune microenvironment, copy number variation (CNV), and drug sensitivity in AML between distinct subgroups were investigated. Moreover, an m6A-related lncRNA prognostic model was established to evaluate the prognosis of AML patients. Results: Nine prognosis-related m6A-associated lncRNAs were selected to construct a prognosis model. The accuracy of the model was further determined by the Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curve. Then, AML samples were classified into high- and low-risk groups according to the median value of risk scores. Gene set enrichment analysis (GSEA) demonstrated that samples with higher risks were featured with aberrant immune-related biological processes and signaling pathways. Notably, the high-risk group was significantly correlated with an increased ImmuneScore and StromalScore, and distinct immune cell infiltration. In addition, we discovered that the high-risk group harbored higher IC50 values of multiple chemotherapeutics and small-molecule anticancer drugs, especially TW.37 and MG.132. In addition, a nomogram was depicted to assess the overall survival (OS) of AML patients. The model based on the median value of risk scores revealed reliable accuracy in predicting the prognosis and survival status. Conclusion: The present research has originated a prognostic risk model for AML according to the expression of prognostic m6A-related lncRNAs. Notably, the signature might also serve as a novel biomarker that could guide clinical applications, for example, selecting AML patients who could benefit from immunotherapy.