M5 C regulator-mediated methylation modification patterns and tumor microenvironment infiltration characteristics in acute myeloid leukemia.

Abstract

Recently, many studies have been conducted to examine immune response modification at epigenetic level, but the candidate effect of RNA 5-methylcytosine (m5 C) modification on tumor microenvironment (TME) of acute myeloid leukemia (AML) is still unknown at present. We assessed the patterns of m5 C modification among 417 AML cases by using nine m5 C regulators. Thereafter, we associated those identified modification patterns with TME cell infiltration features. Additionally, stepwise regression and LASSO Cox regression analyses were conducted for quantifying patterns of m5 C modification among AML cases to establish the m5 C-score. Meanwhile, we validated the expression of genes in the m5C-score model by qRT-PCR. Finally, the present work analyzed the association between m5 C-score and AML clinical characteristics and prognostic outcomes. In total, three different patterns of m5 C modification (m5 C-clusters) were identified, and highly differentiated TME cell infiltration features were also identified. On this basis, evaluating patterns of m5 C modification in single cancer samples was important for evaluating the immune/stromal activities in TME and for predicting prognosis. In addition, the m5 C-score was established, which showed a close relation with the overall survival (OS) of test and training set samples. Moreover, multivariate Cox analysis suggested that our constructed m5 C-score served as the independent predicting factor for the prognosis of AML (hazard ratio = 1.57, 95% confidence interval = 1.38-1.79, p < 1e-5 ). This study shows that m5 C modification may be one of the key roles in the formation of diversity and complexity of TME. Meanwhile, assessing the patterns of m5 C modification among individual cancer samples is of great importance, which provides insights into cell infiltration features within TME, thereby helping to develop relevant immunotherapy and predict patient prognostic outcomes.