m ⁵C regulator‐mediated methylation modification patterns and tumor microenvironment infiltration characteristics in acute myeloid leukemia

Abstract

Background

Recently, many studies have been conducted to examine immune response modification at epigenetic level, but the candidate effect of RNA 5‐methylcytosine (m ⁵C) modification on tumor microenvironment (TME) of acute myeloid leukemia (AML) is still unknown at present.

Methods

We assessed the patterns of m ⁵C modification among 417 AML cases by using nine m ⁵C 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 m ⁵C modification among AML cases to establish the m ⁵C‐score. Meanwhile, we validated the expression of genes in the m5C‐score model by qRT‐PCR. Finally, the present work analyzed the association between m ⁵C‐score and AML clinical characteristics and prognostic outcomes.

Results

In total, three different patterns of m ⁵C modification (m ⁵C‐clusters) were identified, and highly differentiated TME cell infiltration features were also identified. On this basis, evaluating patterns of m ⁵C modification in single cancer samples was important for evaluating the immune/stromal activities in TME and for predicting prognosis. In addition, the m ⁵C‐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 m ⁵C‐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).

Conclusions

This study shows that m ⁵C modification may be one of the key roles in the formation of diversity and complexity of TME. Meanwhile, assessing the patterns of m ⁵C 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.