An m1A/m6A/m7G/m5C regulator-mediated methylation modification pattern and Landscape of immune microenvironment infiltration characterization in Lower-Grade Glioma cohorts from three continents based on machine learning

Background: Although many studies have highlighted RNA modification processes such as N1-methyladenosine (m1A), N6-methyladenosine (m6A), N7-methylguanosine (m7G), and 5-methylcytosine methylation (m5C)’s role in the prognosis of patients suffering from different cancers, their prospective involvement in lower-grade gliomas (LGG) has not yet been outlined. Methods: This work aims to assess the 64 genes related to m1A/m6A/m7G/m5C modification. Based on the expression of methylation-related regulators (MRRs), unsupervised clustering was conducted to identify molecular subtypes. The m1A/m6A/m7G/m5C modification patterns, tumor microenvironment (TME) cell infiltration features, and correlation with immune infiltration markers were assessed. Additionally, the first stage of MMR screening was conducted using univariate Cox analysis, and the prognostic model for the m1A/m6A/m7G/m5C risk score was constructed using different machine learning algorithms analysis. Results: The m1A/m6A/m7G/m5C risk model, including five genes illustrated better prognostic ability for LGG in both the training and validation datasets, wherein the patients were classified into the low and high-risk groups. The LGG patients who were categorized into the high-risk groups displayed poor prognoses. In addition, the role played by five genes at the protein expression level was confirmed using immunohistochemical sections in the HPA database. Finally, functional analysis revealed the richness of pathways and biological processes related to MRR regulation and immune function. Conclusion: An m1A/m6A/m7G/m5C-related risk model was developed and validated in this study to offer valuable new insights into the role played by m1A/m6A/m7G/m5C modification patterns in predicting the prognosis of LGG patients from three continents and developing better and improved treatment strategies for LGG.