ArticleNeuro-oncology2025
DNA methylation profiling of pituitary neuroendocrine tumors identifies distinct clinical and pathological subtypes based on epigenetic differentiation.
Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DNA methylation as a predictor of pituitary neuroendocrine tumour behaviour: A systematic review.Journal of neuroendocrinology · 2026Pooled it
- Sellar region neurocytomas exhibit a CIMP and neuroendocrine-like epigenetic signature distinct from other intra-axial neurocytomas.Acta neuropathologica · 2026Article
- Multi-omics integration unravels four molecular subgroups of corticotroph pituitary neuroendocrine tumours with distinct clinicopathological features.Nature communications · 2026Article
- Neuroendocrine tumours through an epigenetic lens: Emerging insights for diagnosis and treatment.Journal of neuroendocrinology · 2026Review
- Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.Endocrine pathology · 2026Article
- The role of methylation in pituitary neuroendocrine tumors current insights and emerging perspectives.Molecular biology reports · 2026Review
- DNA methylation profiling predicts postsurgical regrowth in SF1-lineage nonfunctioning pituitary neuroendocrine tumors.Neuro-oncology · 2026Article
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13 authors.
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Abstract
backgroundPituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories.
methodsIn this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes.
resultsCNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation.
conclusionOur findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.
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