Comprehensive analysis of mA subtype classification for immune microenvironment of pituitary adenomas.

BACKGROUND

N6-methyladenosine (mA) RNA methylation and tumor immune microenvironment (IME) have an essential role in tumor development. However, their relationships in pituitary adenomas (PAs) remains unclear.

METHODS

PA datasets from the Gene Expression Omnibus (GEO) and European Bioinformatics Institute (EMBL-EBI) were used. We utilized hierarchical clustering algorithms based on the mA regulator gene set to identify mA subtypes. ESTIMATE and CIBERSORT algorithms were applied to explore the compositions of stromal and immune cells. A nomogram model was constructed for the prediction of m6A subtypes in PAs. Immunohistochemistry and multiplex immunofluorescence staining were used to analyze the expression level of m6A regulator YTHDF2 in relation to M2 macrophages and immune checkpoints in PAs.

RESULTS

We concluded the IME landscape of mA subtype classification and characterized two emerging mA subtypes. Different IME between these two mA subtypes were identified. Simultaneously, a polygenic nomogram model was constructed for predicting mA subtype classification, with excellent predictive performance (training set, AUC = 0.984; validation set, AUC = 0.986). YTHDF2 was highly expressed in PAs and accompanied by upregulated M2 macrophages and expression of PD-L1.

CONCLUSIONS

We proposed two novel mA subtypes in PAs for the first time and constructed a reliable and clinically accessible nomogram model for them. Meanwhile, YTHDF2 was first identified as a promising biomarker for immunotherapy and potential molecular target in PAs.