N6-甲基腺苷（m6A）RNA甲基化和肿瘤免疫微环境（IME）对肿瘤的发展起着重要作用。然而，它们在垂体腺瘤（PAs）中的关系仍不清楚。采用了来自基因表达欧洲生物信息学研究所（EMBL-EBI）和基因表达欧洲生信网（GEO）的PA数据集。利用基于m6A调控基因集的分层聚类算法，识别m6A亚型。我们应用ESTIMATE和CIBERSORT算法探究间质和免疫细胞的组成。建立了一个用于PAs中预测m6A亚型的分数模型。通过免疫组化和多重免疫荧光染色来分析m6A调控因子YTHDF2的表达水平与PAs中M2巨噬细胞和免疫检查点的关系。我们总结了m6A亚型分类与IME景观，并表征了两种新兴的m6A亚型。鉴定了这两种m6A亚型之间不同的IME。同时，我们建立了一个多基因分数模型，用于预测m6A亚型的分类，并具有优异的预测性能（训练集，AUC = 0.984；验证集，AUC = 0.986）。YTHDF2在PAs中高表达，并伴随着上调的M2巨噬细胞和PD-L1的表达。我们首次提出了PAs中的两种新m6A亚型，并为它们建立了可靠且临床易于使用的分数模型。同时，YTHDF2首次被确定为免疫治疗和PAs中潜在分子靶点的有希望的生物标志物。版权所有©2023 Elsevier B.V.发表。

N6-methyladenosine (m6A) RNA methylation and tumor immune microenvironment (IME) have an essential role in tumor development. However, their relationships in pituitary adenomas (PAs) remains unclear.PA datasets from the Gene Expression Omnibus (GEO) and European Bioinformatics Institute (EMBL-EBI) were used. We utilized hierarchical clustering algorithms based on the m6A regulator gene set to identify m6A 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.We concluded the IME landscape of m6A subtype classification and characterized two emerging m6A subtypes. Different IME between these two m6A subtypes were identified. Simultaneously, a polygenic nomogram model was constructed for predicting m6A 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.We proposed two novel m6A 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.Copyright © 2023. Published by Elsevier B.V.