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Review

DNA Methylation in Pituitary Adenomas: A Scoping Review

by
Morten Winkler Møller
1,2,3,*,
Mathias Just Nortvig
1,2,3,
Mikkel Schou Andersen
1,2,3 and
Frantz Rom Poulsen
1,2,3
1
Department of Neurosurgery, Odense University Hospital, DK-5000 Odense, Denmark
2
Department of Clinical Research, University of Southern Denmark, DK-5000 Odense, Denmark
3
BRIDGE (Brain Research-Inter Disciplinary Guided Excellence), University of Southern Denmark, DK-5000 Odense, Denmark
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(2), 531; https://doi.org/10.3390/ijms26020531
Submission received: 6 December 2024 / Revised: 4 January 2025 / Accepted: 8 January 2025 / Published: 10 January 2025
(This article belongs to the Special Issue Pituitary Tumors: An Update in the Pathogenetic Mechanisms, the Diagnostic Methods and the Therapeutic Approaches)
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Abstract

:
Pituitary adenomas are a diverse group of neoplasms with variable clinical behavior. Despite advances in genetic analysis, understanding the role of epigenetic modifications, particularly DNA methylation, remains an area under investigation. This scoping review aimed to update and synthesize the current body of literature on DNA methylation in pituitary adenomas, focusing on methodological advancements and clinical correlations. A systematic search conducted across multiple databases, including Embase, Scopus, MEDLINE, and CENTRAL, identified 107 eligible studies. Early methods, such as methylation-restricted digestion and methylation-specific PCR (MSP), have evolved into more comprehensive approaches, such as chip-based DNA methylation analysis. Key findings suggest that genes like POMC, SOCS-1, and RASSF1A show a significant association between methylation and clinical behavior. However, methylation patterns alone are insufficient to fully explain tumorigenesis. Emerging data suggest that DNA methylation might serve as a prognostic marker for invasive growth and recurrence, but further longitudinal studies are needed. This review highlights the need for future research to explore the methylome more thoroughly and to better define the clinical impact of epigenetic modifications in pituitary adenomas.

1. Introduction

In 2017, the World Health Organization (WHO) updated the classification of pituitary adenomas with the addition of transcription factors SF1, PIT1, and TPIT to better delineate adenohypophyseal cell lineages [1]. Transcription factor staining thus divides pituitary adenomas into three distinct subgroups, where SF1 is expressed in gonadotroph adenomas, PIT1 is expressed in GH- (somatotroph), PRL-, and TSH-producing adenomas, and TPIT is expressed in corticotroph adenomas [2]. Pituitary adenomas are further classified as either functioning pituitary adenomas (FPA) that secrete hormones to peripheral blood or nonfunctioning pituitary adenomas (NFPA) that do not secrete hormones [3]. The classification of pituitary adenomas based solely on transcription factors and hormone secretion does not fully explain their clinical presentation and tumor differentiation [4]. To address this, the Pituitary Society introduced a nine-tiered prognostic classification during the Pituitary Adenoma Nomenclature 3 (PANOMEN 3) clinical classification workshop. This classification combines preoperative tumor size with tumor phenotype and clinical behavior, such as invasion and mass effect (11). Research on pituitary tumorigenesis has identified a few hereditary mutations, such as MENIN [5] and AIP [6], that predispose to pituitary adenomas. Somatic mutations in GNAS (associated with somatotroph adenomas) [7] and USP8 (corticotroph adenomas) [8] have also been implicated in pituitary adenomas. Exon sequencing has otherwise revealed little additional genetic association with pituitary adenomas [9].
The limited insights from genetic studies have changed the narrative towards epigenetic alterations [10,11], such as DNA methylation [12] and histone modification [13], that may contribute to tumorigenesis, invasive growth, and disease progression. Advances in transcriptomic analysis have strengthened the classification of pituitary adenomas [14], and DNA methylation has shown potential for further improvement in their subgrouping [15].
DNA methylation is a key regulator of gene expression [16], and is mediated by DNA methyltransferases (DNMTs) that transfer a methyl group to the C5 position of cytosine nucleotides. Such 5-methylcytosines that are positioned upstream to a guanine nucleotide in DNA sequences are referred to as CpG sites [17]. The accumulation of CpG sites often results in transcriptional silencing, particularly when clustered as CpG islands [18]. As shown in Figure 1, DNA methylation inhibits the binding of transcription factors causing no or lower generation of mRNA, leading to a lower expression of the gene products generated from the specific methylated DNA strand. These products are either secreted to the extracellular space or maintained within the intracellular space regulating signaling pathways.
The role of DNA methylation in pituitary adenomas has been investigated for several decades, aiming to address the diagnostic challenges that arise due to the diverse clinical and immunohistochemical profiles of these tumors [19,20]. DNA methylation has been correlated with specific preoperative conditions, like invasive behavior [21] and postoperative recurrence [22]. The current knowledge about the value of DNA methylation and its association with clinical outcomes in pituitary adenomas is limited, as the molecular signatures are not incorporated in the guidelines when risk assessing pituitary adenomas [23]. Epigenetic mechanisms, including DNA methylation, are increasingly seen as fundamental to understanding tumor behavior and heterogeneity [24,25], including pituitary adenomas [26]. The latest systematic review on DNA methylation in pituitary adenomas was conducted in 2013 [16], underscoring the need for an updated synthesis.
The aim of this scoping review was to update and summarize the literature on advancements in DNA methylation in pituitary adenomas, highlighting trends and knowledge gaps for future research directions.

1.1. Three Main Methods to Perform DNA Methylation

To date, three methods have primarily been used to assess DNA methylation in pituitary adenomas, each offering unique insights into methylation patterns. Alongside Whole Genome Bisulfite Sequencing (WGBS), which profiles over 28 million CpG sites [27] and has never have been performed on pituitary adenomas, early studies relied on a labor-intensive digestion-based approach. This has evolved into methylation-specific PCR (MSP) that targets specific methylated regions and, most recently, a high-throughput chip-based methylation analysis covering over 200,000 sites for more comprehensive, genome-wide analysis.

1.1.1. Methylation-Restricted Digestion

This earliest approach, methylation-restricted digestion, used restriction enzymes to fragment DNA at specific methylation-sensitive sites [19]. Following digestion, the DNA could undergo PCR amplification for targeted regions [28] or be separated using agarose gel electrophoresis. DNA fragments were then transferred to a nitrocellulose membrane and probed for specific methylated regions through hybridization. Although this technique was pivotal in early methylation studies, it was limited by the specificity of restriction enzymes and the available knowledge of methylation sites.

1.1.2. Methylation-Specific Polymerase Chain Reaction (MSP)

This approach uses bisulfite conversion, which selectively converts unmethylated cytosine residues to uracil while methylated cytosines remain unchanged. This conversion allows the differentiation of methylated from unmethylated DNA, as unmethylated cytosines are ultimately converted to thymine during PCR [29]. Commercial kits streamline the bisulfite conversion process, making MSP a widely accessible technique. The bisulfite-treated DNA is then amplified using two sets of primers that target either methylated or unmethylated CpG sites [30]. MSP is highly specific, but is constrained to predefined regions, limiting its use for broader genomic exploration.

1.1.3. Chip-Based DNA Methylation Analysis

Chip-based DNA methylation analysis, or DNA methylation arrays, is a high-throughput method that enables the genome-wide profiling of methylation across thousands of CpG sites [31]. After bisulfite conversion, the DNA is fragmented and hybridized to microarray probes specific to CpG sites throughout the genome. The probes selectively bind based on methylation status, with fluorescent labels indicating methylation at each site. Data from the array are processed to generate comprehensive methylation profiles, revealing differentially methylated positions (DMPs) and regions (DMRs) linked to specific phenotypes or disease states. This method provides a global perspective on DNA methylation, enhancing our understanding of its role in tumor behavior and progression.

2. Results

2.1. Literature Screening and Eligibility

A comprehensive systematic literature search across Embase, Scopus, MEDLINE, and CENTRAL yielded 1140 abstracts. An additional two studies were identified through forward and backward citation searches. After duplicate removal, 439 abstracts were excluded, with 434 duplicates identified through Covidence systematic review web-based collaboration software platform (Veritas Health Innovation, Melbourne, Australia), and 5 were removed manually. Of the remaining 703 studies, 508 were excluded based on their title and abstract review. This left 195 studies eligible for full-text review, all of which were successfully retrieved. Three older studies that were inaccessible through standard databases were obtained with the support of a healthcare librarian at the University of Southern Denmark.
Following full-text screening, 88 studies were excluded due to irrelevant comparators (e.g., studies focusing on histone modifications without DNA methylation, immunohistochemical staining for MGMT, or transcription analysis without methylation data); inappropriate study design (e.g., conference abstracts, studies on chemotherapeutic effects, or narrative reviews not identified in the abstract stage); or unsuitable study population (e.g., pediatric studies or studies on intracranial tumors other than pituitary adenomas).
In total, 107 studies met the eligibility criteria and were included in this scoping review (see Figure 2).

2.2. Study Characteristics

The studies included in this review are detailed in Supplementary Tables S1–S3 and Table 1. While 8 studies used methylation-restricted digestion (Supplementary Table S1), 59 used methylation-specific PCR (MSP) (Supplementary Table S2), and 31 used chip-based microarray analyses (Table 1). Each study examined DNA methylation in pituitary adenoma tissue, exploring its association with various genes, pathways, and gene expression patterns relevant to tumor behavior. Additional papers using alternative approaches (nine studies) are summarized in Supplementary Table S4.

2.2.1. Methylation-Restricted Digestion

Due to their labor-intensive nature, methylation-restricted digestion studies typically involved small sample sizes, with a maximum of 30 samples [32]. Genes analyzed through this method included GH [19,33], p16 [28,34], the RB1 promoter region [32], the POMC promoter region [30], 1A DMR [35], and the MGMT promoter region [36]. These studies commonly demonstrated correlations between methylation status and gene expression. The earliest investigation of DNA methylation in pituitary adenomas, conducted in 1988, examined the methylation of the GH gene in relation to hypersecretion in GH-positive adenomas, and suggested that hypomethylation might be linked to overexpression [33]. Although sample sizes gradually increased, later studies—including analyses of MGMT promoter methylation—did not establish this marker as predictive of temozolomide response in aggressive adenomas. Additional findings from studies using methylation-restricted digestion are presented in Supplementary Table S1.

2.2.2. Methylation-Specific PCR

The advent of MSP allowed larger sample sizes and more precise assessments of methylation across specific genomic regions. The increased efficiency of MSP, aided by commercially available kits, enabled the detailed exploration of methylation patterns and correlations with gene expression data and clinical behavior. Strong correlations between methylation, gene expression, and clinical outcomes were found for genes involved in cell cycle regulation, apoptosis, and various signaling pathways such as CDKN2A [37,38], DAP kinase gene [25,39], GADD45g [40], RASSF1A [41], RASSF3 [42], Gal-3 [43], SOCS-1 [44], FGFR2-IIIb [45], GSTP1 [46,47], NNAT [48], EFEMP1 [49], sFRP4 [50,51,52], p21 [53], p27 [53], WIF1 [52], GIPR [54,55], DNMT1 [56], DNMT3A [56], DNMT3B [56], LAMA2 [57], TERT [58,59,60], GNAS [61,62], and the POMC promoter [63].
Genes and pathways with a probable, but not statistically significant, association with clinical behavior included the E-cadherin gene [64], C22orf3 [65], MEG3 [26,66], p15INK4b [67], RB1 [25,38,67,68], CDH13 [69], FGFR2 [70], CDKN2C [38], RIZ1 [71], SSTR5 [72], Pttg1 [73], autosomal and X-linked genes [74], and the CHST7 promoter [75].
Genes such as p16 [76,77,78], CDH1 [69,79], p18INK4C [80], NDRG2 [81], GADD45b [82], MEN1 [83], the hTERT promoter [84], and the STAT3 promoter [85] showed no association with clinical differences between pituitary adenomas and normal tissue. Likewise, the MGMT promoter methylation [25,86,87,88,89,90,91,92] did not appear to be a reliable prognostic marker for responses to temozolomide in aggressive adenomas. Additional genes including p16INK4a (CDKN2A) [25,67,68], CDK4 [67], p14ARF [25,38], p73 [25], THBS1 [25], caspase 8 [25], and TIMP-3 [25] were investigated, but no definitive conclusions were drawn regarding the impact of methylation on expression or clinical outcomes.
While the list of genes investigated is extensive, no single gene or pathway has been identified as the sole driver of tumor regrowth, hypersecretion, or invasiveness. More recent studies using MSP emphasize a shift towards examining broader molecular patterns, potentially contributing to tumor differentiation rather than single-pathway mechanisms [93]. Detailed findings are provided in Supplementary Materials (Table S2).

2.2.3. Chip-Based DNA Methylation Analysis

The first genome-wide, methylation-based analyses of pituitary adenomas revealed multiple candidate genes and signaling pathways that enabled differentiation between pituitary adenomas and other sellar tumors, as well as between adenoma subtypes that had traditionally been distinguished using immunohistochemistry [14,15,94,95,96,97,98,99,100,101,102,103,104]. Genome-wide methylation patterns also highlight differences between clinically invasive and non-invasive tumors, thus identifying several genes that may contribute to invasiveness without a definitive characterization [11,105,106,107,108].
Several studies identified specific genes and pathways associated with invasive tumor behavior; for example, ITPKB and CNKSR1 [109]. Additional genes—PHYHD1, LTBR, MYBPHL, C22orf42, PRR5, ANKDD1A, RAB13, CAMKV, KIFC3, WNT4, and STAT6—were implicated in the invasive behavior of non-functioning pituitary adenomas (NFPAs) [21]. TERT was examined, but no specific pathogenic role was identified in pituitary tumors [110]. Genes such as STAT5A, RHOD, GALNT9, RASSF1, CDKN1A, TP73, STAT3, HMGA2, FAM163A, HIF3A, and PRSS8 were found to be hypermethylated in NFPAs [111]. Six genes (FAM90A1, ETS2, STAT6, MYT1L, ING2, and KCNK1) were identified as potential biomarkers for NFPA regrowth [112]. A study correlating methylation and gene expression identified several hub proteins associated with pituitary adenomas, including DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, and ZFHX3 [24]. Myc-associated protein X binding sites were globally hypomethylated in growth hormone-secreting pituitary adenomas (GHPAs) compared to NFPAs, suggesting increased transcription factor binding accessibility as a potential therapeutic target for GHPAs [113]. Additionally, one study found no differences between GHPAs and NFPAs, but did find genes C7orf50, GNG7, and BAHCC1 to be associated with postoperative progression [114]. Tumors from the posterior pituitary lobe showed only minor methylation differences [115].
A recent study proposed that DNA methylation patterns in circulating cell-free DNA may distinguish pituitary adenomas from other sellar pathologies, enabling non-invasive lesion characterization based on blood samples [116].
Comprehensive findings from these studies are summarized in Table 1.
Table 1. Chip-based methylation analysis.
Table 1. Chip-based methylation analysis.
Author/YearAimSample SizeKey Findings
Duong et al. [94]/2012Methylation profile of 27,578 CpG sites spanning more than 14,000 genes in each of the major pituitary adenoma subtypes7 GH-secreting tumors, 6 corticotrophinomas, 6 prolactinomas (PRL), and 13 non-functioning (NF) adenomasFirst and unbiased survey of the pituitary tumor epigenome across different adenoma subtypes
Ling et al. [95]/2014DNA methylation alterations between invasive and noninvasive PAs subtypes24 patients with surgically resected PAsDNA methylation analysis of key candidate genes may potentially complement histopathological classification systems for PA subtypes
Gu et al. [105]/2016DNA methylation differences between invasive and non-invasive non-functioning PAs12 adenomas were included in the discovery cohort; 7 adenomas were included in an independent cohortEpigenetic modification of key gene substrates might partially account for the invasion of non-functioning PAs
Kober et al. [109]/2018The role of aberrant methylation at particular loci for gene expression in PAs31 gonadotroph NFPAs, 2 NFPAs that were positive for gonadotropins (FSH, LH, a-subunit) and TSH, and 1 null-cell adenomaInvasive NFPAs showed invasiveness-related aberrant epigenetic upregulation of ITPKB and downregulation of CNKSR1
Boresowicz [110]/2018Incidence of TERT abnormalities and to assess their role in telomere lengthening in PAsTissue samples from 101 patientsTelomerase abnormalities do not play any special role in pathogenesis of pituitary tumors
Johann et al. [96]/2018Characterize molecular alterations of sellar region ATRTs in adults as compared to pituitary adenomas47 pituitary adenomas were evaluatedSellar region ATRTs in adults form a clinically distinct entity with a different mutational spectrum
Salomon et al. [97]/2018DNA methylation data were generated from the three major subtypes of pituitary adenomas48 patientsDNA methylation alterations play a major role in the disease etiology
Kober et al. [111]/2019DNA methylation in the misregulation of gene expression in gonadotroph NFPAs32 patientsGenes with aberrant methylation in pituitary tumors—STAT5A, RHOD, GALNT9, RASSF1, CDKN1A, TP73, STAT3 and HMGA2. FAM163A, HIF3A, and PRSS8—were hypermethylated in NFPAs
Cheng et al. [21]/2019Integrated analyses of paired whole-genome DNA methylation and gene expression in PARetrospectively enrolled 68 patientsMethylation and expression levels of PHYHD1, LTBR, MYBPHL, C22orf42, PRR5, ANKDD1A, RAB13, CAMKV, KIFC3, WNT4, and STAT6 play a pivotal role in the invasive behavior of NFPA
Neou et al. [98]/2019A molecularly unbiased classification, further deciphering the pathways responsible for tumorigenesis from a single set of PitNETsThe methylome of 86 PitNETs of all typesIdentified three groups associated with tumor type and secretion; in particular, POU1F1/PIT1-lineage tumors showed global hypomethylation
Cheng et al. [112]/2020DNA methylation and expression parameters to evaluate the regrowth of NFPA71 patients diagnosed with NFPA6 of 13 genes (FAM90A1, ETS2, STAT6, MYT1L, ING2, and KCNK1) were considered potential biomarkers associated with the regrowth of NFPA
Boresowicz et al. [106]/2020CpGs located in miRNA genes that have differential methylation levels in gonadotroph PitNETs34 PitNETs and 5 samples of normal pituitaryEpigenetic regulation and changes in miRNA expression play a significant role in pathogenesis of PitNETs
Taniguchi-Ponciano et al. [14]/2020Identify the cellular pathways involved in their tumorigenesis6 non-tumoral pituitaries and 42 PAsA divergent PA origin that segregates transcriptomically into three distinct clusters depending on the specific transcription factors
Wei et al. [11]/2020Activated and inhibited pathways and related key genes in hpNFPAs versus NFPAsEight snap-frozen NFPA specimens (four hpNF-PAs and four NFPAs)The DNA methylation and gene expression patterns of two highly proliferative NFPAs occurring at young ages were noticeably distinct to those of six other NFPAs
Mosella et al. [15]/2021Identify, characterize, and validate methylation-based signatures that define PitNETs according to clinicopathological featuresDNA methylation data from PitNETs from three independent institutions and from our cohort at the Hermelin Brain Tumor Center (n = 23)Methylation signatures distinguished PitNETs by adenohypophyseal cell lineages
Schmid et al. [115]/2021Molecular differences between the three histologic types using DNA methylation analysis47 neoplasms of the posterior pituitary glandOnly subtle DNA methylation differences among tumors of the posterior pituitary
Nadaf et al. [99]/2021Use generated data to gain insights into the initiation and development of PitBs by identifying pathways differentially alteredA total of 64 tumor and normal FFPE tissuesPitB samples formed a distinct cluster separate from the various pituitary adenoma subtypes
Hagel et al. [107]/202112 double PAs (DPA) with diverse hormone profiles were investigated regarding DNA methylation profile12 cases were identified among 3654 surgical specimens of adenomaGlobal DNA methylation profiling may yield additional information in lesions that appear as null-cell adenomas immunohistochemically
Asuzu et al. [117]/2022A mechanism of tumorigenesis with therapeutic implications for CDThree tumors investigated for methylation (EPIC)There may exist histone modifications that contribute to the pathogenesis of wild-type CD that were not captured by DNA methylation approaches
Dottermusch et al. [118]/2022
(Case report)		The role of epigenomic analyses in the diagnostic workup of a challenging sellar lesion57-year-old maleExemplifies benefits and limitations of epigenomic analyses in molecular diagnostics of posterior pituitary neoplasms
Hickman et al. [119]/2022
(Case report)		A functioning corticotroph tumor with admixed adrenocortical cells, providing novel methylation profiling data33-year-old maleThe methylation profile of this tumor was unique but was placed within the T-SNE plots adenohypophyseal entities, with the closest match being corticotroph tumors
Giuffrida et al. [114]/2022Correlate the methylation status of NFPAs and GH-omas with their epidemiological and clinicopathological features21 PA samples (11 GH-omas, 10 NFPAs)C7orf50, GNG7, and BAHCC1 genes, which were found to be methylated in pituitary tumor biology
Hallén et al. [108]/2022Whether DNA methylation pattern differs in NFPAs between patients with residual adenoma with postoperative progression28 tumors from the reintervention group and 21 tumors from the radiologically stable groupMethylation patterns associated with clinically significant tumor growth requiring reintervention
Silva-Júnior et al. [100]/2022Methylome and transcriptome analysis of the three major subtypes of surgically resectable PitNETs77 patients (46 NFPT and 31 functioning pituitary tumors)Methylome and transcriptome data resulted in three clusters that were associated with each other and with 2017 and 2022 WHO classifications
Aydin et al. [24]/2022Evaluate the molecular profiling of NF-PitNETs at three biological levels34 NF-PitNET samples and 6 normal pituitary glandsProposed hub proteins, including DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3
Herrgott et al. [116]/2022Differentiate PitNETs from OPD through analysis of LB specimensPitNETs (n = 37)PitNETs release DNA methylation markers in the serum/plasma
Santana-Santos et al. [101]/2022The Northwestern Medicine (NM) classifier of CNS tumors was developed and validated3905 central nervous tumor samples. 2801 samples were used in the original classifier training, and 1104 were used for validation.Whole-genome methylation profiling of brain tumors for clinical testing has been developed and validated
Tucker et al. [113]/2023Validate the differential DNA methylation and related MAX protein expression profiles between NFPA and GHPA52 surgically resected tumors (37 NFPA, 15 GHPA)MAX transcription factor binding sites are globally hypomethylated and demonstrate increased accessibility for transcription factor binding in GHPA compared to NFPA
Galbraith et al. [102]/2023The clinical utility of DNA methylation for primary diagnosis of brain tumors1921 primary CNS tumorsDNA methylation is of limited diagnostic and prognostic value in the diagnosis of meningioma, schwannoma, and pituitary adenoma
Kober et al. [103]/2023Genome-wide DNA methylation patterns in somatotroph tumorsForty-eight tumor samplesDifferences in DNA methylation profiles between three molecular subtypes are undeniable
Feng et al. [104]/2023DNA methylation analysis in PPETS tumors and the comparison cohort15 posterior pituitary tumors (PPT)PPETS and PPT form a distinct molecular cluster

2.2.4. Additional Papers

In addition to the three main methods described above, nine other studies explored DNA methylation in pituitary adenomas using alternative approaches. The three case reports used methylation profiling to assist in diagnosing complex tumor cases, distinguishing pituitary adenomas from other intracranial pathologies based on genome-wide methylation patterns [120,121,122]. The other six studies used various mass-array platforms to assess methylation patterns across tumor subgroups and subtypes within pituitary adenomas. Though comparable to MSP, these methods offer technical distinctions that expand analytical scope.
Genes and pathways examined in these studies include the ZAR1 non-promoter region, noted for frequent methylation events in pituitary adenomas [123]. Additionally, MGMT promoter methylation was investigated, with findings indicating significant methylation in carcinomas [124]. Other notable associations included LINE-1, linked to tumor invasiveness [125], and METTL3, which appears to influence cell growth regulation in GH-secreting pituitary adenomas (GH-PAs) [126]. Two genome-wide studies explored links between methylation status, invasive behavior [127], and proliferation rate [128]. However, no specific genes or pathways were conclusively tied to these characteristics.
Comprehensive findings from these studies are summarized in Supplementary Materials (Table S3).
Table 2 summarizes the key findings from the studies, using methylation-restricted digestion, methylation-specific PCR, and chip-based DNA methylation analysis. The table summarizes the genes and pathways found to be associated with clinical behavior, those with probable associations, and those showing no association.

3. Discussion

For over three decades, DNA methylation has been a valuable tool to distinguish tumor types and subtypes, especially when specific genetic mutations are absent. Epigenetic modifications offer valuable insights into gene regulation and expression, yet defining their direct clinical impact remains a challenge. Early studies using methylation-restricted digestion revealed that the growth hormone gene is methylated in non-functioning pituitary adenomas (NFPAs), but not in functioning pituitary adenomas (FPAs). Since then, our understanding of the role of DNA methylation in pituitary tumor biology has advanced significantly. This review aimed to provide an overview of DNA methylation studies in pituitary adenomas, summarizing the genes and signaling pathways investigated to date and identifying those with potential clinical relevance.
Only a limited number of genes demonstrated a clear correlation between methylation and expression across different analytical platforms. For example, POMC promoter methylation was linked to reduced gene expression and was also associated with aggressive features in Cushing’s disease [30,63]. POMC plays a crucial role in the synthesis of the adrenocorticotropic hormone (ACTH) in pituitary corticotroph cells, and its dysregulation may impact the hypothalamic–pituitary–adrenal (HPA) axis and potentially influence the onset and severity of corticotropic tumors.
Another gene with notable findings was SOCS-1, which was associated with non-functioning pituitary adenomas across multiple modalities, including methylation-specific PCR (MSP) and EPIC array [24,44]. SOCS-1 (suppressor of cytokine signaling 1) is involved in the JAK-STAT pathway, where it acts as an inhibitor. The dysregulation of this pathway, particularly through SOCS-1, has been associated with more aggressive tumor phenotypes [129]. RASSF1A, identified both by MSP [41] and EPIC [111], is a well-known tumor suppressor gene that prevents tumorigenesis and uncontrolled cell growth through various intracellular pathways [130]. Methylation of RASSF1A would lower expression and increase the risk of tumorigenesis and uncontrolled growth. Additionally, RB1, a pivotal tumor suppressor and the first of its kind identified, was detected across multiple analysis methods, although not by EPIC [25,32,38,67,68]. The established role of RB1 in inhibiting invasive growth makes it a key gene of interest in studies aiming to uncover risk factors for aggressive pituitary adenomas [131]. Although multiple studies found a correlation between DNA methylation status for specific genes and sites, the reproducibility of these findings was only seen for the genes mentioned above.
Although the gold standard for methylation analysis remains whole-genome bisulfite sequencing (WGBS), this has yet to be performed on pituitary adenomas. Early studies relying on digestion-restricted analysis provided foundational insights and laid the groundwork for subsequent advances but are now limited by outdated technology. Methylation-specific PCR (MSP) has since offered more targeted assessments, as its primers are designed to focus on relevant gene regions. MSP can yield more precise methylation profiles of genes or pathways and is often paired with gene expression analysis to investigate the functional impact of methylation [132]. The shift toward chip-based methylation analysis appears driven by its accessibility and efficiency, which is particularly advantageous for handling large cohorts of tissue samples [100]. More recent studies have employed both chip-based methylation analysis and expression profiling to examine the correlation between DNA methylation and gene expression [14,98]. These studies provide large exploratory datasets and offer valuable insights into epigenetic regulation. However, they are limited by the scope of the chip itself, which captures less than five percent of the methylation sites across the genome [133]. Although these sites are selected to represent coding regions, the inherent selection bias of commercially available chips can limit conclusions about unmethylated regions and their effects on gene expression. For example, MGMT promoter methylation, which has been well-studied using MSP [25,86,87,88,89,90,91,92], has shown no impact on treatment outcomes and has not been consistently identified as a differentially methylated region in chip-based studies.
The clinical applications of DNA methylation in pituitary adenomas have shifted from trying to locate specific sites along the genome responsible for tumorigenesis [42,56], often without clinical follow-up, towards using DNA methylation for profiling for the better prognostication of outcomes [22]. This paper did find some selective probes associated with a higher risk of recurrence. However, these findings were not found using the same method within the same subgroup of pituitary adenomas (non-functioning) [100]. Therefore, the current research trends point towards using DNA methylation for subclassification into subgroups, rather than pinpointing specific methylation sites responsible for tumor regrowth [98]. This is further illustrated by the multiple studies showing a probable association between methylation levels and clinical correlation (Table 2).
This study did encounter some limitations. Multiple studies reported analysis of methylome and transcriptome data [98,100]. Transcriptomic analysis was not included in this paper, and methylation of specific sites did not always correlate with down-regulated expression. Therefore, the inclusion of transcriptomic data and the expression of gene products correlated with next-generation sequencing (NGS), and maybe even proteomics, could probably better illustrate the complex molecular pathways responsible for tumorigenesis, invasive growth, and differences in recurrence rate. In isolation, DNA methylation analysis seems limited when applied for selective genes, as the reproducibility within methods (MSP) for MGMT and in-between methods as previously described did not show similar results. However, DNA methylation profiling using multiple methylated sites shows promising results, segregating pituitary adenomas into distinct clusters [14]. Furthermore, a fundamental limitation of search strings in systematic reviews is the potential to miss relevant studies due to narrow scope or rigid terminology [134]. Our search entailed multiple databases to decrease the risk of missing studies [135]. Moreover, we consulted a librarian to optimize the search string to provide a very broad search. Despite optimization, no search string can guarantee full coverage of all pertinent studies.
This review also analyzed the impact of methylation on clinical outcomes, including associations between methylation status, gene expression, and tumor behavior. A variety of study outcomes have been reported, ranging from assessments of gene expression changes linked to methylation in specific genes or promoter regions to broader correlations between methylation profiles and preoperative clinical features, such as invasive growth. Although some studies provide limited postoperative follow-up, more recent investigations are beginning to report recurrence-free survival data in relation to methylation status [108]. These findings suggest that DNA methylation profiling holds promise as a prognostic marker, potentially guiding more tailored postoperative care for patients with pituitary adenomas.

4. Materials and Methods

This scoping review followed a protocol registered on medRxiv [136] without deviation and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines [137]. The initial search block was developed in Embase (Ovid, Elsevier, Amsterdam, The Netherlands) using relevant subject headings and keywords, and was then adapted for use in other bibliographic databases: Medline (Ovid, US National Library of Medicine, Bethesda, MD, USA), Cochrane Central Register of Controlled Trials (CENTRAL, Wiley, Hoboken, NJ, USA), and Scopus (Elsevier, Amsterdam, The Netherlands). Details of the search strategy are provided in the Supplementary Materials (Table S4).
The inclusion criteria focused on primary studies involving DNA methylation analysis in human pituitary adenomas. Both pre-clinical and clinical studies were included, but review articles were excluded. We included publications in any language, provided that both the title and abstract were in English. Records identified in the database searches were imported into Covidence (Veritas Health Innovation), where duplicates were removed. Covidence facilitated screening at both the title/abstract (Cohens Kappa = 0.69) and full-text (Cohens Kappa = 0.79) levels. Two reviewers (MWM and MJN) independently screened all records, with any disagreements resolved through discussion within the research team. Included records were then exported to Endnote for further data organization.
A data-charting form was developed to systematically extract relevant information from each included study. The following data items were recorded for each study:
Author(s)
Year of publication
Origin/country of origin
Aim/purpose
Population and sample size
Methodology/methods
Intervention type, comparator, and details (e.g., duration, if applicable)
Outcomes and their measurement methods (if applicable)
Key findings related to the review question(s)

5. Conclusions

This scoping review explored the role of DNA methylation pituitary adenomas through the last three decades. Epigenetic modifications undeniable contribute to the subclassification, invasive growth and recurrence probabilities of PAs. Specific pathways might be directly correlated with clinical outcomes, in which epigenetic alterations play a major role. Further research into epigenetic mechanisms could enhance the potential of epigenetics as a prognostic marker, enabling personalized follow-up strategies and supporting expanded treatment regimens based on these findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms26020531/s1.

Author Contributions

Conceptualization, M.W.M., M.J.N. and F.R.P.; methodology, M.W.M. and M.S.A.; software, M.W.M.; validation, M.W.M., M.J.N. and M.S.A.; formal analysis, M.W.M.; investigation, M.W.M.; resources, M.W.M. and F.R.P.; data curation, M.W.M.; writing—original draft preparation, M.W.M.; writing—review and editing, M.W.M., M.J.N., M.S.A. and F.R.P.; visualization, M.W.M.; supervision, F.R.P.; project administration, M.W.M.; funding acquisition, M.W.M. and F.R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Novo Nordisk Fundation, grant number NNF21OC0071244.

Acknowledgments

We thank Andreas Lundh from Cochrane Denmark and the Center for Evidence-Based Medicine Odense for commenting on the protocol. The search strategy was developed in collaboration with an information specialist. We thank professional healthcare librarian Mette Brandt Eriksen, University of Southern Denmark. Finally, we thank Claire Gudex, University of Southern Denmark, for proofreading this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mete, O.; Lopes, M.B. Overview of the 2017 WHO Classification of Pituitary Tumors. Endocr. Pathol. 2017, 28, 228–243. [Google Scholar] [CrossRef] [PubMed]
  2. Burcea, I.F.; Năstase, V.N.; Poiană, C. Pituitary transcription factors in the immunohistochemical and molecular diagnosis of pituitary tumours—A systematic review. Endokrynol. Pol. 2021, 72, 53–63. [Google Scholar] [CrossRef] [PubMed]
  3. Manojlovic-Gacic, E.; Engstrom, B.E.; Casar-Borota, O. Histopathological classification of non-functioning pituitary neuroendocrine tumors. Pituitary 2018, 21, 119–129. [Google Scholar] [CrossRef] [PubMed]
  4. Trouillas, J.; Jaffrain-Rea, M.-L.; Vasiljevic, A.; Raverot, G.; Roncaroli, F.; Villa, C. How to Classify Pituitary Neuroendocrine Tumors (PitNET)s in 2020. Cancers 2020, 12, 514. [Google Scholar] [CrossRef]
  5. Mayr, B.; Apenberg, S.; Rothämel, T.; von zur Mühlen, A.; Brabant, G. Menin mutations in patients with multiple endocrine neoplasia type 1. Eur. J. Endocrinol. 1997, 137, 684–687. [Google Scholar] [CrossRef]
  6. Beckers, A.; Aaltonen, L.A.; Daly, A.F.; Karhu, A. Familial isolated pituitary adenomas (FIPA) and the pituitary adenoma predisposition due to mutations in the aryl hydrocarbon receptor interacting protein (AIP) gene. Endocr. Rev. 2013, 34, 239–277. [Google Scholar] [CrossRef]
  7. Mantovani, G.; Lania, A.G.; Spada, A. GNAS imprinting and pituitary tumors. Mol. Cell Endocrinol. 2010, 326, 15–18. [Google Scholar] [CrossRef]
  8. Reincke, M.; Sbiera, S.; Hayakawa, A.; Theodoropoulou, M.; Osswald, A.; Beuschlein, F.; Meitinger, T.; Mizuno-Yamasaki, E.; Kawaguchi, K.; Saeki, Y.; et al. Mutations in the deubiquitinase gene USP8 cause Cushing’s disease. Nat. Genet. 2015, 47, 31–38. [Google Scholar] [CrossRef]
  9. Bi, W.L.; Horowitz, P.; Greenwald, N.F.; Abedalthagafi, M.; Agarwalla, P.K.; Gibson, W.J.; Mei, Y.; Schumacher, S.E.; Ben-David, U.; Chevalier, A.; et al. Landscape of Genomic Alterations in Pituitary Adenomas. Clin. Cancer Res. 2017, 23, 1841–1851. [Google Scholar] [CrossRef]
  10. Hauser, B.M.; Lau, A.; Gupta, S.; Bi, W.L.; Dunn, I.F. The Epigenomics of Pituitary Adenoma. Front. Endocrinol. 2019, 10, 290. [Google Scholar] [CrossRef]
  11. Wei, Z.; Zhou, C.; Li, M.; Huang, R.; Deng, H.; Shen, S.; Wang, R. Integrated multi-omics profiling of nonfunctioning pituitary adenomas. Pituitary 2021, 24, 312–325. [Google Scholar] [CrossRef] [PubMed]
  12. Capper, D.; Jones, D.T.W.; Sill, M.; Hovestadt, V.; Schrimpf, D.; Sturm, D.; Koelsche, C.; Sahm, F.; Chavez, L.; Reuss, D.E.; et al. DNA methylation-based classification of central nervous system tumours. Nature 2018, 555, 469–474. [Google Scholar] [CrossRef] [PubMed]
  13. Zhao, W.; Jiang, X.; Weisenthal, K.; Ma, J.; Botticelli, E.M.; Zhou, Y.; Hedley-Whyte, E.T.; Wang, B.; Swearingen, B.; Soberman, R.J.; et al. High Histone Deacetylase 2/3 Expression in Non-Functioning Pituitary Tumors. Front. Oncol. 2022, 12, 875122. [Google Scholar] [CrossRef]
  14. Taniguchi-Ponciano, K.; Andonegui-Elguera, S.; Peña-Martínez, E.; Silva-Román, G.; Vela-Patiño, S.; Gomez-Apo, E.; Chavez-Macias, L.; Vargas-Ortega, G.; Espinosa-de-los-Monteros, L.; Gonzalez-Virla, B.; et al. Transcriptome and methylome analysis reveals three cellular origins of pituitary tumors. Sci. Rep. 2020, 10, 19373. [Google Scholar] [CrossRef]
  15. Mosella, M.S.; Sabedot, T.S.; Silva, T.C.; Malta, T.M.; Dezem, F.S.; Asmaro, K.P.; Wells, M.; Mukherjee, A.; Poisson, L.M.; Snyder, J.; et al. DNA methylation-based signatures classify sporadic pituitary tumors according to clinicopathological features. Neuro Oncol. 2021, 23, 1292–1303. [Google Scholar] [CrossRef]
  16. Pease, M.; Ling, C.; Mack, W.J.; Wang, K.; Zada, G. The role of epigenetic modification in tumorigenesis and progression of pituitary adenomas: A systematic review of the literature. PLoS ONE 2013, 8, e82619. [Google Scholar] [CrossRef]
  17. Moore, L.D.; Le, T.; Fan, G. DNA methylation and its basic function. Neuropsychopharmacology 2013, 38, 23–38. [Google Scholar] [CrossRef]
  18. Tycko, B. Epigenetic gene silencing in cancer. J. Clin. Investig. 2000, 105, 401–407. [Google Scholar] [CrossRef]
  19. Huttner, A.; Adams, E.F.; Buchfelder, M.; Fahlbusch, R. Growth hormone gene structure in human pituitary somatotrophinomas: Promoter region sequence and methylation studies. J. Mol. Endocrinol. 1994, 12, 167–172. [Google Scholar] [CrossRef]
  20. Asa, S.L.; Mete, O.; Perry, A.; Osamura, R.Y. Overview of the 2022 WHO Classification of Pituitary Tumors. Endocr. Pathol. 2022, 33, 6–26. [Google Scholar] [CrossRef]
  21. Cheng, S.; Xie, W.; Miao, Y.; Guo, J.; Wang, J.; Li, C.; Zhang, Y. Identification of key genes in invasive clinically non-functioning pituitary adenoma by integrating analysis of DNA methylation and mRNA expression profiles. J. Transl. Med. 2019, 17, 407. [Google Scholar] [CrossRef] [PubMed]
  22. Hallen, T.; Johannsson, G.; Dahlen, R.; Andersson-Assarsson, J.C.; Glad, C.A.; Orndal, C.; Engvall, A.; Caren, H.; Skoglund, T.; Olsson, D.S. Genome-Wide DNA Methylation Differences in Patients With Non-Functioning Pituitary Adenomas With or Without Postsurgical Intervention. J. Endocr. Soc. 2021, 5 (Suppl. S1), A643. [Google Scholar] [CrossRef]
  23. Ho, K.K.Y.; Fleseriu, M.; Wass, J.; Katznelson, L.; Raverot, G.; Little, A.S.; Castano, J.P.; Reincke, M.; Lopes, M.B.; Kaiser, U.B.; et al. A proposed clinical classification for pituitary neoplasms to guide therapy and prognosis. Lancet Diabetes Endocrinol. 2024, 12, 209–214. [Google Scholar] [CrossRef] [PubMed]
  24. Aydin, B.; Beklen, H.; Arga, K.Y.; Bayrakli, F.; Turanli, B. Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors. J. Endocrinol. Investig. 2023, 46, 727–747. [Google Scholar] [CrossRef]
  25. Bello, M.J.; De Campos, J.M.; Isla, A.; Casartelli, C.; Rey, J.A. Promoter CpG methylation of multiple genes in pituitary adenomas: Frequent involvement of caspase-8. Oncol. Rep. 2006, 15, 443–448. [Google Scholar] [CrossRef]
  26. Gejman, R.; Batista, D.L.; Zhong, Y.; Zhou, Y.; Zhang, X.; Swearingen, B.; Stratakis, C.A.; Hedley-Whyte, E.T.; Klibanski, A. Selective loss of MEG3 expression and intergenic differentially methylated region hypermethylation in the MEG3/DLK1 locus in human clinically nonfunctioning pituitary adenomas. J. Clin. Endocrinol. Metab. 2008, 93, 4119–4125. [Google Scholar] [CrossRef]
  27. Stirzaker, C.; Taberlay, P.C.; Statham, A.L.; Clark, S.J. Mining cancer methylomes: Prospects and challenges. Trends Genet. 2014, 30, 75–84. [Google Scholar] [CrossRef]
  28. Ruebel, K.H.; Jin, L.; Zhang, S.; Scheithauer, B.W.; Lloyd, R.V. Inactivation of the p16 gene in human pituitary nonfunctioning tumors by hypermethylation is more common in null cell adenomas. Endocr. Pathol. 2001, 12, 281–289. [Google Scholar] [CrossRef]
  29. Miyata, K.; Naito, M.; Miyata, T.; Mokuda, S.; Asahara, H. Bisulfite Sequencing for DNA Methylation Analysis of Primary Muscle Stem Cells. Methods Mol. Biol. 2017, 1668, 3–13. [Google Scholar] [CrossRef]
  30. Newell-Price, J.; King, P.; Clark, A.J. The CpG island promoter of the human proopiomelanocortin gene is methylated in nonexpressing normal tissue and tumors and represses expression. Mol. Endocrinol. (Baltim. Md.) 2001, 15, 338–348. [Google Scholar] [CrossRef]
  31. Sahoo, K.; Sundararajan, V. Methods in DNA methylation array dataset analysis: A review. Comput. Struct. Biotechnol. J. 2024, 23, 2304–2325. [Google Scholar] [CrossRef] [PubMed]
  32. Simpson, D.J.; Hibberts, N.A.; McNicol, A.M.; Clayton, R.N.; Farrell, W.E. Loss of pRb expression in pituitary adenomas is associated with methylation of the RB1 CpG island. Cancer Res. 2000, 60, 1211–1216. [Google Scholar] [PubMed]
  33. Hoi, S.U.; Kelley, P.; Lee, W.H. Abnormalities of the human growth hormone gene and protooncogenes in some human pituitary adenomas. Mol. Endocrinol. 1988, 2, 85–89. [Google Scholar] [CrossRef]
  34. Woloschak, M.; Yu, A.; Post, K.D. Frequent inactivation of the p16 gene in human pituitary tumors by gene methylation. Mol. Carcinog. 1997, 19, 221–224. [Google Scholar] [CrossRef]
  35. Picard, C.; Silvy, M.; Gerard, C.; Buffat, C.; Lavaque, E.; Figarella-Branger, D.; Dufour, H.; Gabert, J.; Beckers, A.; Brue, T.; et al. Gsα overexpression and loss of Gsα imprinting in human somatotroph adenomas: Association with tumor size and response to pharmacologic treatment. Int. J. Cancer 2007, 121, 1245–1252. [Google Scholar] [CrossRef]
  36. Raverot, G.; Sturm, N.; De Fraipont, F.; Muller, M.; Salenave, S.; Caron, P.; Chabre, O.; Chanson, P.; Cortet-Rudelli, C.; Assaker, R.; et al. Temozolomide treatment in aggressive pituitary tumors and pituitary carcinomas: A French multicenter experience. J. Clin. Endocrinol. Metab. 2010, 95, 4592–4599. [Google Scholar] [CrossRef]
  37. Simpson, D.J.; Bicknell, J.E.; McNicol, A.M.; Clayton, R.N.; Farrell, W.E. Hypermethylation of the p16/CDKN2A/MTSI gene and loss of protein expression is associated with nonfunctional pituitary adenomas but not somatotrophinomas. Genes. Chromosomes Cancer 1999, 24, 328–336. [Google Scholar] [CrossRef]
  38. Kirsch, M.; Morz, M.; Pinzer, T.; Schackert, H.K.; Schackert, G. Frequent loss of the CDKN2C (p18INK4c) gene product in pituitary adenomas. Genes. Chromosomes Cancer 2009, 48, 143–154. [Google Scholar] [CrossRef]
  39. Simpson, D.J.; Clayton, R.N.; Farrell, W.E. Preferential loss of Death Associated Protein kinase expression in invasive pituitary tumours is associated with either CPG island methylation or homozygous deletion. Oncogene 2002, 21, 1217–1224. [Google Scholar] [CrossRef]
  40. Bahar, A.; Bicknell, J.E.; Simpson, D.J.; Clayton, R.N.; Farrell, W.E. Loss of expression of the growth inhibitory gene GADD45γ, in human pituitary adenomas, is associated with CpG island methylation. Oncogene 2004, 23, 936–944. [Google Scholar] [CrossRef]
  41. Qian, Z.R.; Sano, T.; Yoshimoto, K.; Yamada, S.; Ishizuka, A.; Mizusawa, N.; Horiguchi, H.; Hirokawa, M.; Asa, S.L. Inactivation of RASSF1A tumor suppressor gene by aberrant promoter hypermethylation in human pituitary adenomas. Lab. Investig. 2005, 85, 464–473. [Google Scholar] [CrossRef] [PubMed]
  42. Peng, H.; Liu, H.; Zhao, S.; Wu, J.; Fan, J.; Liao, J. Silencing of RASSF3 by DNA Hypermethylation Is Associated with Tumorigenesis in Somatotroph Adenomas. PLoS ONE 2013, 8, e59024. [Google Scholar] [CrossRef] [PubMed]
  43. Ruebel, K.H.; Jin, L.; Qian, X.; Scheithauer, B.W.; Kovacs, K.; Nakamura, N.; Zhang, H.; Raz, A.; Lloyd, R.V. Effects of DNA methylation on galectin-3 expression in pituitary tumors. Cancer Res. 2005, 65, 1136–1140. [Google Scholar] [CrossRef] [PubMed]
  44. Buslei, R.; Kreutzer, J.; Hofmann, B.; Schmidt, V.; Siebzehnrul, F.; Hahnen, E.; Eyupoglu, I.Y.; Fahlbusch, R.; Blumcke, I. Abundant hypermethylation of SOCS-1 in clinically silent pituitary adenomas. Acta Neuropathol. 2006, 111, 264–271. [Google Scholar] [CrossRef] [PubMed]
  45. Zhu, X.; Asa, S.L.; Ezzat, S. Fibroblast growth factor 2 and estrogen control the balance of histone 3 modifications targeting MAGE-A3 in pituitary neoplasia. Clin. Cancer Res. 2008, 14, 1984–1996. [Google Scholar] [CrossRef]
  46. Yuan, Y.; Qian, Z.R.; Sano, T.; Asa, S.L.; Yamada, S.; Kagawa, N.; Kudo, E. Reduction of GSTP1 expression by DNA methylation correlates with clinicopathological features in pituitary adenomas. Mod. Pathol. 2008, 21, 856–865. [Google Scholar] [CrossRef]
  47. Ferrau, F.; Romeo, P.D.; Puglisi, S.; Ragonese, M.; Spagnolo, F.; Salpietro, C.; Ientile, R.; Curro, M.; Visalli, G.; Alibrandi, A.; et al. GSTP1 gene methylation and AHR rs2066853 variant predict resistance to first generation somatostatin analogs in patients with acromegaly. J. Endocrinol. Investig. 2019, 42, 825–831. [Google Scholar] [CrossRef]
  48. Revill, K.; Dudley, K.J.; Clayton, R.N.; McNicol, A.M.; Farrell, W.E. Loss of neuronatin expression is associated with promoter hypermethylation in pituitary adenoma. Endocr.-Relat. Cancer 2009, 16, 537–548. [Google Scholar] [CrossRef]
  49. Duong, C.V.; Yacqub-Usman, K.; Emes, R.D.; Clayton, R.N.; Farrell, W.E. The EFEMP1 gene: A frequent target for epigenetic silencing in multiple human pituitary adenoma subtypes. Neuroendocrinology 2013, 98, 200–211. [Google Scholar] [CrossRef]
  50. Wu, Y.; Bai, J.; Li, Z.; Wang, F.; Cao, L.; Liu, C.; Yu, S.; Yu, G.; Zhang, Y. Low expression of secreted frizzled-related protein 4 in aggressive pituitary adenoma. Pituitary 2015, 18, 335–342. [Google Scholar] [CrossRef]
  51. Wu, Y.; Liu, C.; Yu, S.; Gao, H.; Li, Z.; Li, C.; Zhang, Y. Assessment of sFRP4 as a bio-marker for predicting aggressiveness and recurrence of growth hormone-secreting pituitary adenomas. Oncol. Rep. 2016, 35, 2991–2999. [Google Scholar] [CrossRef] [PubMed]
  52. Song, W.; Qian, L.; Jing, G.; Jie, F.; Xiaosong, S.; Chunhui, L.; Yangfang, L.; Guilin, L.; Gao, H.; Yazhuo, Z. Aberrant expression of the sFRP and WIF1 genes in invasive non-functioning pituitary adenomas. Mol. Cell. Endocrinol. 2018, 474, 168–175. [Google Scholar] [CrossRef] [PubMed]
  53. Dong, W.; Li, J.; Liu, Q.; Liu, C.; Li, C.; Song, G.; Zhu, H.; Gao, H.; Zhang, Y. P21Waf1/Cip1 and p27Kip1 are correlated with the development and invasion of prolactinoma. J. Neuro-Oncol. 2018, 136, 485–494. [Google Scholar] [CrossRef] [PubMed]
  54. Hage, M.; Chaligne, R.; Viengchareun, S.; Villa, C.; Salenave, S.; Bouligand, J.; Letouze, E.; Tosca, L.; Rouquette, A.; Tachdjian, G.; et al. Hypermethylator Phenotype and Ectopic GIP Receptor in GNAS Mutation-Negative Somatotropinomas. J. Clin. Endocrinol. Metab. 2019, 104, 1777–1787. [Google Scholar] [CrossRef] [PubMed]
  55. Dalle Nogare, M.; D’Annunzio, S.; Vazza, G.; Regazzo, D.; Picello, L.; Denaro, L.; Voltan, G.; Scaroni, C.; Ceccato, F.; Occhi, G. The Methylation Analysis of the Glucose-Dependent Insulinotropic Polypeptide Receptor (GIPR) Locus in GH-Secreting Pituitary Adenomas. Int. J. Mol. Sci. 2023, 24, 9264. [Google Scholar] [CrossRef]
  56. Ma, H.S.; Wang, E.L.; Xu, W.F.; Yamada, S.; Yoshimoto, K.; Qian, Z.R.; Shi, L.; Liu, L.L.; Li, X.H. Overexpression of dna (Cytosine-5)-methyltransferase 1 (dnmt1) and dna (cytosine-5)-methyltransferase 3a (dnmt3a) is associated with aggressive behavior and hypermethylation of tumor suppressor genes in human pituitary adenomas. Med. Sci. Monit. 2018, 24, 4841–4850. [Google Scholar] [CrossRef]
  57. Wang, R.Q.; Lan, Y.L.; Lou, J.C.; Lyu, Y.Z.; Hao, Y.C.; Su, Q.F.; Ma, B.B.; Yuan, Z.B.; Yu, Z.K.; Zhang, H.Q.; et al. Expression and methylation status of LAMA2 are associated with the invasiveness of nonfunctioning PitNET. Ther. Adv. Endocrinol. Metab. 2019, 10, 1–11. [Google Scholar] [CrossRef]
  58. Miyake, Y.; Adachi, J.I.; Suzuki, T.; Mishima, K.; Araki, R.; Mizuno, R.; Nishikawa, R. TERT promoter methylation is significantly associated with TERT upregulation and disease progression in pituitary adenomas. J. Neuro-Oncol. 2019, 141, 131–138. [Google Scholar] [CrossRef]
  59. Alzoubi, H.; Minasi, S.; Gianno, F.; Antonelli, M.; Belardinilli, F.; Giangaspero, F.; Jaffrain-Rea, M.-L.; Buttarelli, F.R. Alternative Lengthening of Telomeres (ALT) and Telomerase Reverse Transcriptase Promoter Methylation in Recurrent Adult and Primary Pediatric Pituitary Neuroendocrine Tumors. Endocr. Pathol. 2022, 33, 494–505. [Google Scholar] [CrossRef]
  60. Endo, M.; Adachi, J.I.; Murakami, C.; Inomoto, C.; Komatsu, M.; Hanakita, S.; Oyama, K.I.; Matsuno, A.; Nishikawa, R.; Oya, S. A case of aggressive pituitary neuroendocrine tumour with extremely rapid progression: Possible diagnostic value of TERT promoter methylation. Br. J. Neurosurg. 2022, 1–7. [Google Scholar] [CrossRef]
  61. Valimaki, N.; Schalin-Jantti, C.; Karppinen, A.; Paetau, A.; Kivipelto, L.; Aaltonen, L.A.; Karhu, A. Genetic and epigenetic characterization of growth hormone-secreting pituitary tumors. Mol. Cancer Res. 2019, 17, 2432–2443. [Google Scholar] [CrossRef] [PubMed]
  62. Romanet, P.; Galluso, J.; Kamenicky, P.; Hage, M.; Theodoropoulou, M.; Roche, C.; Graillon, T.; Etchevers, H.C.; De Murat, D.; Mougel, G.; et al. Somatotroph Tumors and the Epigenetic Status of the GNAS Locus. Int. J. Mol. Sci. 2021, 22, 7570. [Google Scholar] [CrossRef] [PubMed]
  63. Araki, T.; Tone, Y.; Kameda, H.; Ben-Shlomo, A.; Yamada, S.; Takeshita, A.; Yamamoto, M.; Kawakami, Y.; Tone, M.; Melmed, S. Two Distinctive POMC Promoters Modify Gene Expression in Cushing Disease. J. Clin. Endocrinol. Metab. 2021, 106, E3346–E3363. [Google Scholar] [CrossRef]
  64. Xu, B.; Sano, T.; Yoshimoto, K.; Yamada, S. Downregulation of E-cadherin and its undercoat proteins in pituitary growth hormone cell adenomas with prominent fibrous bodies. Endocr. Pathol. 2002, 13, 341–351. [Google Scholar] [CrossRef]
  65. Bahar, A.; Simpson, D.J.; Cutty, S.J.; Bicknell, J.E.; Hoban, P.R.; Holley, S.; Mourtada-Maarabouni, M.; Williams, G.T.; Clayton, R.N.; Farrell, W.E. Isolation and characterization of a novel pituitary tumor apoptosis gene. Mol. Endocrinol. 2004, 18, 1827–1839. [Google Scholar] [CrossRef]
  66. Zhao, J.; Dahle, D.; Zhou, Y.; Zhang, X.; Klibanski, A. Hypermethylation of the promoter region is associated with the loss of MEG3 gene expression in human pituitary tumors. J. Clin. Endocrinol. Metab. 2005, 90, 2179–2186. [Google Scholar] [CrossRef]
  67. Ogino, A.; Yoshino, A.; Katayama, Y.; Watanabe, T.; Ota, T.; Komine, C.; Yokoyama, T.; Fukushima, T. The p15(INK4b)/p16(INK4a)/RB1 pathway is frequently deregulated in human pituitary adenomas. J. Neuropathol. Exp. Neurol. 2005, 64, 398–403. [Google Scholar] [CrossRef]
  68. Yoshino, A.; Katayama, Y.; Ogino, A.; Watanabe, T.; Yachi, K.; Ohta, T.; Komine, C.; Yokoyama, T.; Fukushima, T. Promoter hypermethylation profile of cell cycle regulator genes in pituitary adenomas. J. Neuro-Oncol. 2007, 83, 153–162. [Google Scholar] [CrossRef]
  69. Qian, Z.R.; Sano, T.; Yoshimoto, K.; Asa, S.L.; Yamada, S.; Mizusawa, N.; Kudo, E. Tumor-specific downregulation and methylation of the CDH13 (H-cadherin) and CDH1 (E-cadherin) genes correlate with aggressiveness of human pituitary adenomas. Mod. Pathol. Off. J. United States Can. Acad. Pathol. Inc. 2007, 20, 1269–1277. [Google Scholar] [CrossRef]
  70. Zhu, X.; Lee, K.; Asa, S.L.; Ezzat, S. Epigenetic silencing through DNA and histone methylation of fibroblast growth factor receptor 2 in neoplastic pituitary cells. Am. J. Pathol. 2007, 170, 1618–1628. [Google Scholar] [CrossRef]
  71. Xue, Y.; Chen, R.; Du, W.; Yang, F.; Wei, X. RIZ1 and histone methylation status in pituitary adenomas. Tumour Biol. 2017, 39, 1010428317711794. [Google Scholar] [CrossRef] [PubMed]
  72. Pedraza-Arevalo, S.; Ibanez-Costa, A.; Blazquez-Encinas, R.; Branco, M.R.; Vazquez-Borrego, M.C.; Herrera-Martinez, A.D.; Venegas-Moreno, E.; Serrano-Blanch, R.; Arjona-Sanchez, A.; Galvez-Moreno, M.A.; et al. Epigenetic and post-transcriptional regulation of somatostatin receptor subtype 5 (SST5) in pituitary and pancreatic neuroendocrine tumors. Mol. Oncol. 2022, 16, 764–779. [Google Scholar] [CrossRef] [PubMed]
  73. Szabo, B.; Meszaros, K.; Krokker, L.; Liko, I.; Saskoi, E.; Nemeth, K.; Szabo, P.T.; Szucs, N.; Czirjak, S.; Szaloki, G.; et al. Aspirin Mediates Its Antitumoral Effect Through Inhibiting PTTG1 in Pituitary Adenoma. J. Clin. Endocrinol. Metab. 2022, 107, 3066–3079. [Google Scholar] [CrossRef] [PubMed]
  74. Guaraldi, F.; Morandi, L.; Zoli, M.; Mazzatenta, D.; Righi, A.; Evangelisti, S.; Ambrosi, F.; Tonon, C.; Giannini, C.; Lloyd, R.V.; et al. Epigenomic and somatic mutations of pituitary tumors with clinical and pathological correlations in 111 patients. Clin. Endocrinol. (Oxf.) 2022, 97, 763–772. [Google Scholar] [CrossRef]
  75. Dong, W.; Shi, W.; Liu, Y.; Li, J.; Zhang, Y.; Dong, G.; Dong, X.; Gao, H. CHST7 Methylation Status Related to the Proliferation and Differentiation of Pituitary Adenomas. Cells 2022, 11, 2400. [Google Scholar] [CrossRef]
  76. Jaffrain-Rea, M.L.; Ferretti, E.; Toniato, E.; Cannita, K.; Santoro, A.; Di Stefano, D.; Ricevuto, E.; Maroder, M.; Tamburrano, G.; Cantore, G.; et al. p16 (INK4a, MTS-1) gene polymorphism and methylation status in human pituitary tumours. Clin. Endocrinol. 1999, 51, 317–325. [Google Scholar] [CrossRef]
  77. Seemann, N.; Kuhn, D.; Wrocklage, C.; Keyvani, K.; Hackl, W.; Buchfelder, M.; Fahlbusch, R.; Paulus, W. CDKN2A/p 16 inactivation is related to pituitary adenoma type and size. J. Pathol. 2001, 193, 491–497. [Google Scholar] [CrossRef]
  78. Simpson, D.J.; McNicol, A.M.; Murray, D.C.; Bahar, A.; Turner, H.E.; Wass, J.A.H.; Esiri, M.M.; Clayton, R.N.; Farrell, W.E. Molecular Pathology Shows p16 Methylation in Nonadenomatous Pituitaries from Patients with Cushing’s Disease. Clin. Cancer Res. 2004, 10, 1780–1788. [Google Scholar] [CrossRef]
  79. Evang, J.A.; Berg, J.P.; Casar-Borota, O.; Lekva, T.; Kringen, M.K.; Ramm-Pettersen, J.; Bollerslev, J. Reduced levels of E-cadherin correlate with progression of corticotroph pituitary tumours. Clin. Endocrinol. 2011, 75, 811–818. [Google Scholar] [CrossRef]
  80. Hossain Md, G.; Iwata, T.; Mizusawa, N.; Qian, Z.R.; Shima, S.W.N.; Okutsu, T.; Yamada, S.; Sano, T.; Yoshimoto, K. Expression of p18INK4C is down-regulated in human pituitary adenomas. Endocr. Pathol. 2009, 20, 114–121. [Google Scholar] [CrossRef]
  81. Vaitkiene, P.; Valiulyte, I.; Glebauskiene, B.; Liutkeviciene, R. N-myc downstream-regulated gene 2 (NDRG2) promoter methylation and expression in pituitary adenoma. Diagn. Pathol. 2017, 12, 33. [Google Scholar] [CrossRef] [PubMed]
  82. Michaelis, K.A.; Knox, A.J.; Xu, M.; Kiseljak-Vassiliades, K.; Edwards, M.G.; Geraci, M.; Kleinschmidt-DeMasters, B.K.; Lillehei, K.O.; Wierman, M.E. Identification of growth arrest and DNA-damage- inducible gene β (GADD45β) as a novel tumor suppressor in pituitary gonadotrope tumors. Endocrinology 2011, 152, 3603–3613. [Google Scholar] [CrossRef] [PubMed]
  83. Raef, H.; Zou, M.; Baitei, E.Y.; Al-Rijjal, R.A.; Kaya, N.; Al-Hamed, M.; Monies, D.; Abu-Dheim, N.N.; Al-Hindi, H.; Al-Ghamdi, M.H.; et al. A novel deletion of the MEN1 gene in a large family of multiple endocrine neoplasia type 1 (MEN1) with aggressive phenotype. Clin. Endocrinol. 2011, 75, 791–800. [Google Scholar] [CrossRef] [PubMed]
  84. Kochling, M.; Ewelt, C.; Furtjes, G.; Peetz-Dienhart, S.; Koos, B.; Hasselblatt, M.; Paulus, W.; Stummer, W.; Brokinkel, B. hTERT promoter methylation in pituitary adenomas. Brain Tumor Pathol. 2016, 33, 27–34. [Google Scholar] [CrossRef]
  85. Valiulyte, I.; Steponaitis, G.; Skiriute, D.; Tamasauskas, A.; Vaitkiene, P. Signal transducer and activator of transcription 3 (STAT3) promoter methylation and expression in pituitary adenoma. BMC Med. Genet. 2017, 18, 72. [Google Scholar] [CrossRef]
  86. McCormack, A.I.; McDonald, K.L.; Gill, A.J.; Clark, S.J.; Burt, M.G.; Campbell, K.A.; Braund, W.J.; Little, N.S.; Cook, R.J.; Grossman, A.B.; et al. Low O6-methylguanine-DNA methyltransferase (MGMT) expression and response to temozolomide in aggressive pituitary tumours. Clin. Endocrinol. 2009, 71, 226–233. [Google Scholar] [CrossRef]
  87. Losa, M.; Mazza, E.; Terreni, M.R.; McCormack, A.; Gill, A.J.; Motta, M.; Cangi, M.G.; Talarico, A.; Mortini, P.; Reni, M. Salvage therapy with temozolomide in patients with aggressive or metastatic pituitary adenomas: Experience in six cases. Eur. J. Endocrinol. 2010, 163, 843–851. [Google Scholar] [CrossRef]
  88. Bush, Z.M.; Longtine, J.A.; Cunningham, T.; Schiff, D.; Jane Jr, J.A.; Vance, M.L.; Thorner, M.O.; Laws Jr, E.R.; Lopes, M.B.S. Temozolomide treatment for aggressive pituitary tumors: Correlation of clinical outcome with O6-methylguanine methyltransferase (MGMT) promoter methylation and expression. J. Clin. Endocrinol. Metab. 2010, 95, E280–E290. [Google Scholar] [CrossRef]
  89. Arya, S.; Majaid, M.A.; Shwetha, S.D.; Sravani, K.; Arivazhagan, A.; Sampath, S.; Santosh, V. Implications of MGMT methylation status in pituitary adenoma. Pathol. Res. Pract. 2014, 210, 407–411. [Google Scholar] [CrossRef]
  90. Ceccato, F.; Lombardi, G.; Manara, R.; Emanuelli, E.; Denaro, L.; Milanese, L.; Gardiman, M.P.; Bertorelle, R.; Scanarini, M.; D’Avella, D.; et al. Temozolomide and pasireotide treatment for aggressive pituitary adenoma: Expertise at a tertiary care center. J. Neuro-Oncol. 2015, 122, 189–196. [Google Scholar] [CrossRef]
  91. Jiang, X.B.; Hu, B.; He, D.S.; Mao, Z.G.; Wang, X.; Song, B.B.; Zhu, Y.H.; Wang, H.J. Expression profiling of O6 methylguanine-DNA-methyl transferase in prolactinomas: A correlative study of promoter methylation and pathological features in 136 cases. BMC Cancer 2015, 15, 644. [Google Scholar] [CrossRef] [PubMed]
  92. Micko, A.S.G.; Hoftberger, R.; Wohrer, A.; Millesi, M.; Knosp, E.; Wolfsberger, S. MGMT assessment in pituitary adenomas: Comparison of different immunohistochemistry fixation chemicals. Pituitary 2018, 21, 266–273. [Google Scholar] [CrossRef] [PubMed]
  93. Wang, Z.; Chang, M.; Zhou, G.; Liu, P.; Lou, J.; Zhang, Y.; Guo, X.; Bao, X.; Lian, W.; Wang, Y.; et al. Multi-Omics Investigations Revealed Underlying Molecular Mechanisms Associated With Tumor Stiffness and Identified Sunitinib as a Potential Therapy for Reducing Stiffness in Pituitary Adenomas. Front. Cell Dev. Biol. 2022, 10, 820562. [Google Scholar] [CrossRef] [PubMed]
  94. Duong, C.V.; Emes, R.D.; Wessely, F.; Yacqub-Usman, K.; Clayton, R.N.; Farrell, W.E. Quantitative, genome-wide analysis of the DNA methylome in sporadic pituitary adenomas. Endocr.-Relat. Cancer 2012, 19, 805–816. [Google Scholar] [CrossRef]
  95. Ling, C.; Pease, M.; Shi, L.; Punj, V.; Shiroishi, M.S.; Commins, D.; Weisenberger, D.J.; Wang, K.; Zada, G. A pilot genome-scale profiling of DNA methylation in sporadic pituitary macroadenomas: Association with Tumor invasion and histopathological subtype. PLoS ONE 2014, 9, e96178. [Google Scholar] [CrossRef]
  96. Johann, P.D.; Bens, S.; Oyen, F.; Wagener, R.; Giannini, C.; Perry, A.; Raisanen, J.M.; Reis, G.F.; Nobusawa, S.; Arita, K.; et al. Sellar Region Atypical Teratoid/Rhabdoid Tumors (ATRT) in Adults Display DNA Methylation Profiles of the ATRT-MYC Subgroup. Am. J. Surg. Pathol. 2018, 42, 506–511. [Google Scholar] [CrossRef]
  97. Salomon, M.P.; Wang, X.; Marzese, D.M.; Hsu, S.C.; Nelson, N.; Zhang, X.; Matsuba, C.; Takasumi, Y.; Ballesteros-Merino, C.; Fox, B.A.; et al. The Epigenomic Landscape of Pituitary Adenomas Reveals Specific Alterations and Differentiates Among Acromegaly, Cushing’s Disease and Endocrine-Inactive Subtypes. Clin. Cancer Res. 2018, 24, 4126–4136. [Google Scholar] [CrossRef]
  98. Neou, M.; Villa, C.; Armignacco, R.; Jouinot, A.; Raffin-Sanson, M.L.; Septier, A.; Letourneur, F.; Diry, S.; Diedisheim, M.; Izac, B.; et al. Pangenomic Classification of Pituitary Neuroendocrine Tumors. Cancer Cell 2020, 37, 123–134.e125. [Google Scholar] [CrossRef]
  99. Nadaf, J.; de Kock, L.; Chong, A.S.; Korbonits, M.; Thorner, P.; Benlimame, N.; Fu, L.; Peet, A.; Warner, J.; Ploner, O.; et al. Molecular characterization of DICER1-mutated pituitary blastoma. Acta Neuropathol. 2021, 141, 929–944. [Google Scholar] [CrossRef]
  100. Silva-Júnior, R.; Bueno, A.C.; Martins, C.S.; Coelli-Lacchini, F.; Ozaki, J.G.O.; Almeida, E.S.D.C.; Marrero-Gutierrez, J.; Santos, A.C.D.; Garcia-Peral, C.; Machado, H.R.; et al. Integrating Methylome and Transcriptome Signatures Expands the Molecular Classification of the Pituitary Tumors. J. Clin. Endocrinol. Metab. 2023, 108, 1452–1463. [Google Scholar] [CrossRef]
  101. Santana-Santos, L.; Kam, K.L.; Dittmann, D.; De Vito, S.; McCord, M.; Jamshidi, P.; Fowler, H.; Wang, X.; Aalsburg, A.M.; Brat, D.J.; et al. Validation of Whole Genome Methylation Profiling Classifier for Central Nervous System Tumors. J. Mol. Diagn. 2022, 24, 924–934. [Google Scholar] [CrossRef] [PubMed]
  102. Galbraith, K.; Vasudevaraja, V.; Serrano, J.; Shen, G.; Tran, I.; Abdallat, N.; Wen, M.; Patel, S.; Movahed-Ezazi, M.; Faustin, A.; et al. Clinical utility of whole-genome DNA methylation profiling as a primary molecular diagnostic assay for central nervous system tumors—A prospective study and guidelines for clinical testing. Neuro-Oncol. Adv. 2023, 5, vdad076. [Google Scholar] [CrossRef] [PubMed]
  103. Kober, P.; Rymuza, J.; Baluszek, S.; Maksymowicz, M.; Nyc, A.; Mossakowska, B.J.; Zielinski, G.; Kunicki, J.; Bujko, M. DNA Methylation Pattern in Somatotroph Pituitary Neuroendocrine Tumors. Neuroendocrinology 2024, 114, 51–63. [Google Scholar] [CrossRef] [PubMed]
  104. Feng, J.; Duan, Z.; Yao, K.; Gui, Q.; Liu, X.; Wang, X.; Du, Z.; Shao, L.; Zhang, B.; Cai, S.; et al. Primary papillary epithelial tumor of the sella and posterior pituitary tumor show similar (epi)genetic features and constitute a single neuro-oncological entity. Neuro-Oncol. 2023, 25, 1487–1497. [Google Scholar] [CrossRef]
  105. Gu, Y.; Zhou, X.; Hu, F.; Yu, Y.; Xie, T.; Huang, Y.; Zhao, X.; Zhang, X. Differential DNA methylome profiling of nonfunctioning pituitary adenomas suggesting tumour invasion is correlated with cell adhesion. J. Neuro-Oncol. 2016, 129, 23–31. [Google Scholar] [CrossRef]
  106. Boresowicz, J.; Kober, P.; Rusetska, N.; Maksymowicz, M.; Paziewska, A.; Dabrowska, M.; Zeber-Lubecka, N.; Kunicki, J.; Bonicki, W.; Ostrowski, J.; et al. DNA Methylation Influences miRNA Expression in Gonadotroph Pituitary Tumors. Life 2020, 10, 59. [Google Scholar] [CrossRef]
  107. Hagel, C.; Schuller, U.; Flitsch, J.; Knappe, U.J.; Kellner, U.; Bergmann, M.; Buslei, R.; Buchfelder, M.; Rudiger, T.; Herms, J.; et al. Double adenomas of the pituitary reveal distinct lineage markers, copy number alterations, and epigenetic profiles. Pituitary 2021, 24, 904–913. [Google Scholar] [CrossRef]
  108. Hallén, T.; Johannsson, G.; Dahlén, R.; Glad, C.A.M.; Örndal, C.; Engvall, A.; Carén, H.; Skoglund, T.; Olsson, D.S. Genome-wide DNA Methylation Differences in Nonfunctioning Pituitary Adenomas With and Without Postsurgical Progression. J. Clin. Endocrinol. Metab. 2022, 107, 2318–2328. [Google Scholar] [CrossRef]
  109. Kober, P.; Boresowicz, J.; Rusetska, N.; Maksymowicz, M.; Goryca, K.; Kunicki, J.; Bonicki, W.; Siedlecki, J.A.; Bujko, M. DNA methylation profiling in nonfunctioning pituitary adenomas. Mol. Cell. Endocrinol. 2018, 473, 194–204. [Google Scholar] [CrossRef]
  110. Boresowicz, J.; Kober, P.; Rusetska, N.; Maksymowicz, M.; Goryca, K.; Kunicki, J.; Bonicki, W.; Bujko, M. Telomere Length and TERT abnormalities in pituitary adenomas. Neuroendocrinol. Lett. 2018, 39, 49–55. [Google Scholar]
  111. Kober, P.; Boresowicz, J.; Rusetska, N.; Maksymowicz, M.; Paziewska, A.; Dabrowska, M.; Kunicki, J.; Bonicki, W.; Ostrowski, J.; Siedlecki, J.A.; et al. The role of aberrant DNA methylation in misregulation of gene expression in gonadotroph nonfunctioning pituitary tumors. Cancers 2019, 11, 1650. [Google Scholar] [CrossRef] [PubMed]
  112. Cheng, S.; Li, C.; Xie, W.; Miao, Y.; Guo, J.; Wang, J.; Zhang, Y. Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes involved in the regrowth of clinically non-functioning pituitary adenoma. Aging 2020, 12, 2408–2427. [Google Scholar] [CrossRef] [PubMed]
  113. Tucker, D.W.; Pangal, D.J.; Du, R.; Gogia, A.S.; Tafreshi, A.; Ruzevick, J.; Hurth, K.T.; Triche, T.; Micko, A.; Carpten, J.D.; et al. Validation of Myc-Associated Protein X (MAX) regulation in growth hormone secreting and nonfunctional pituitary adenoma. PLoS ONE 2023, 18, e0284949. [Google Scholar] [CrossRef] [PubMed]
  114. Giuffrida, G.; D’Argenio, V.; Ferrau, F.; Lasorsa, V.A.; Polito, F.; Aliquo, F.; Ragonese, M.; Cotta, O.R.; Alessi, Y.; Oteri, R.; et al. Methylome Analysis in Nonfunctioning and GH-Secreting Pituitary Adenomas. Front. Endocrinol. 2022, 13, 841118. [Google Scholar] [CrossRef]
  115. Schmid, S.; Solomon, D.A.; Perez, E.; Thieme, A.; Kleinschmidt-DeMasters, B.K.; Giannini, C.; Reinhardt, A.; Asa, S.L.; Mete, O.; Stichel, D.; et al. Genetic and epigenetic characterization of posterior pituitary tumors. Acta Neuropathol. 2021, 142, 1025–1043. [Google Scholar] [CrossRef]
  116. Herrgott, G.A.; Asmaro, K.P.; Wells, M.; Sabedot, T.S.; Malta, T.M.; Mosella, M.S.; Nelson, K.; Scarpace, L.; Barnholtz-Sloan, J.S.; Sloan, A.E.; et al. Detection of tumor-specific DNA methylation markers in the blood of patients with pituitary neuroendocrine tumors. Neuro Oncol. 2022, 24, 1126–1139. [Google Scholar] [CrossRef]
  117. Asuzu, D.T.; Alvarez, R.; Fletcher, P.A.; Mandal, D.; Johnson, K.; Wu, W.; Elkahloun, A.; Clavijo, P.; Allen, C.; Maric, D.; et al. Pituitary adenomas evade apoptosis via noxa deregulation in Cushing’s disease. Cell Rep. 2022, 40, 111223. [Google Scholar] [CrossRef]
  118. Dottermusch, M.; Rotermund, R.; Ricklefs, F.L.; Wefers, A.K.; Saeger, W.; Flitsch, J.; Glatzel, M.; Matschke, J. The Diagnostic Impact of Epigenomics in Pituicyte-derived Tumors: Report of an Unusual Sellar Lesion with Extensive Hemorrhage and Necrotic Debris. Endocr. Pathol. 2022, 33, 411–413. [Google Scholar] [CrossRef]
  119. Hickman, R.A.; Gionco, J.T.; Faust, P.L.; Miller, M.L.; Bruce, J.; Page-Wilson, G.; Rosenblum, M.K.; Asa, S.L. Pituitary corticotroph tumour with adrenocortical cells: A distinct clinicopathologic entity with unique morphology and methylation profile. Neuropathol. Appl. Neurobiol. 2022, 48, e12754. [Google Scholar] [CrossRef]
  120. Batisse, M.; Raverot, G.; Maqdasy, S.; Durando, X.; Sturm, N.; Montoriol, P.F.; Kemeny, J.L.; Chazal, J.; Trouillas, J.; Tauveron, I. Aggressive silent GH pituitary tumor resistant to multiple treatments, including temozolomide. Cancer Investig. 2013, 31, 190–196. [Google Scholar] [CrossRef]
  121. Ronsley, R.; Boue, D.R.; Venkata, L.P.R.; Scott, S.; Shaikhouni, A.; Jones, J.; Schieffer, K.M.; Cottrell, C.E.; Mardis, E.R.; Olshefski, R.; et al. An unusual case of atypical teratoid/rhabdoid tumor, initially diagnosed as atypical pituitary adenoma in a 13-year-old male patient. Neuro-Oncol. Adv. 2022, 4, vdac121. [Google Scholar] [CrossRef] [PubMed]
  122. Barrantes-Freer, A.; Braune, M.; Sandner, B.; Dottermusch, M.; Lindner, D. Comparative epigenomics indicate a common origin of ectopic and intrasellar corticotroph pituitary neuroendocrine tumors/adenomas: A case report. Virchows Arch. 2024, 1–6. [Google Scholar] [CrossRef] [PubMed]
  123. Watanabe, T.; Yachi, K.; Ohta, T.; Fukushima, T.; Yoshino, A.; Katayama, Y.; Shinojima, Y.; Terui, T.; Nagase, H. Aberrant hypermethylation of non-promoter zygote arrest 1 (Zar1) in human brain tumors. Neurol. Med.-Chir. 2010, 50, 1062–1069. [Google Scholar] [CrossRef] [PubMed]
  124. Salehi, F.; Scheithauer, B.W.; Kros, J.M.; Lau, Q.; Fealey, M.; Erickson, D.; Kovacs, K.; Horvath, E.; Lloyd, R.V. MGMT promoter methylation and immunoexpression in aggressive pituitary adenomas and carcinomas. Neuropathol. Appl. Neurobiol. 2011, 37 (Suppl. S1), 17–18. [Google Scholar] [CrossRef]
  125. Rusetska, N.; Kober, P.; Krol, S.K.; Boresowicz, J.; Maksymowicz, M.; Kunicki, J.; Bonicki, W.; Bujko, M. Invasive and noninvasive nonfunctioning gonadotroph pituitary tumors differ in dna methylation level of line-1 repetitive elements. J. Clin. Med. 2021, 10, 560. [Google Scholar] [CrossRef]
  126. Chang, M.; Wang, Z.; Gao, J.; Yang, C.; Feng, M.; Niu, Y.; Tong, W.M.; Bao, X.; Wang, R. METTL3-mediated RNA m6A Hypermethylation Promotes Tumorigenesis and GH Secretion of Pituitary Somatotroph Adenomas. J. Clin. Endocrinol. Metab. 2022, 107, 136–149. [Google Scholar] [CrossRef]
  127. Garcia-Martinez, A.; Sottile, J.; Sanchez-Tejada, L.; Fajardo, C.; Camara, R.; Lamas, C.; Barbera, V.M.; Pico, A. DNA Methylation of Tumor Suppressor Genes in Pituitary Neuroendocrine Tumors. J. Clin. Endocrinol. Metab. 2019, 104, 1272–1282. [Google Scholar] [CrossRef]
  128. Szabo, B.; Nemeth, K.; Meszaros, K.; Szucs, N.; Czirjak, S.; Reiniger, L.; Rajnai, H.; Krencz, I.; Karaszi, K.; Krokker, L.; et al. Demethylation Status of Somatic DNA Extracted From Pituitary Neuroendocrine Tumors Indicates Proliferative Behavior. J. Clin. Endocrinol. Metab. 2020, 105, 2015–2026. [Google Scholar] [CrossRef]
  129. Ilangumaran, S.; Gui, Y.; Shukla, A.; Ramanathan, S. SOCS1 expression in cancer cells: Potential roles in promoting antitumor immunity. Front. Immunol. 2024, 15, 1362224. [Google Scholar] [CrossRef]
  130. Dubois, F.; Bergot, E.; Zalcman, G.; Levallet, G. RASSF1A, puppeteer of cellular homeostasis, fights tumorigenesis, and metastasis-an updated review. Cell Death Dis. 2019, 10, 928. [Google Scholar] [CrossRef]
  131. Chinnam, M.; Goodrich, D.W. RB1, development, and cancer. Curr. Top. Dev. Biol. 2011, 94, 129–169. [Google Scholar] [CrossRef] [PubMed]
  132. Chen, Y.; Cai, F.; Cao, J.; Gao, F.; Lv, Y.; Tang, Y.; Zhang, A.; Yan, W.; Wang, Y.; Hu, X.; et al. Analysis of Related Factors of Tumor Recurrence or Progression After Transnasal Sphenoidal Surgical Treatment of Large and Giant Pituitary Adenomas and Establish a Nomogram to Predict Tumor Prognosis. Front. Endocrinol. 2021, 12, 793337. [Google Scholar] [CrossRef] [PubMed]
  133. Noguera-Castells, A.; García-Prieto, C.A.; Álvarez-Errico, D.; Esteller, M. Validation of the new EPIC DNA methylation microarray (900K EPIC v2) for high-throughput profiling of the human DNA methylome. Epigenetics 2023, 18, 2185742. [Google Scholar] [CrossRef] [PubMed]
  134. Livoreil, B.; Glanville, J.; Haddaway, N.R.; Bayliss, H.; Bethel, A.; de Lachapelle, F.F.; Robalino, S.; Savilaakso, S.; Zhou, W.; Petrokofsky, G.; et al. Systematic searching for environmental evidence using multiple tools and sources. Environ. Evid. 2017, 6, 23. [Google Scholar] [CrossRef]
  135. Bramer, W.M.; Rethlefsen, M.L.; Kleijnen, J.; Franco, O.H. Optimal database combinations for literature searches in systematic reviews: A prospective exploratory study. Syst. Rev. 2017, 6, 245. [Google Scholar] [CrossRef]
  136. Møller, M.W.; Nortvig, M.J.; Andersen, M.S.; Poulsen, F.R. DNA methylation in pituitary adenomas: Scoping review protocol. medRxiv 2024. [Google Scholar] [CrossRef]
  137. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
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Figure 1. Schematic representation of hypomethylation (left) and hypermethylation (right) and their effects on gene expression and protein synthesis. 1. Hypomethylation maintains open DNA strands, allowing transcription factors to bind and facilitate gene transcription. In contrast, hypermethylation results in closed DNA conformation, blocking transcription factor binding and suppressing transcription. 2. In hypomethylated regions, mRNA is efficiently transcribed and translated into proteins in ribosomes. Hypermethylation, however, reduces mRNA availability or alters splicing, leading to limited or absent translation. 3. As a result, gene products are fully expressed in hypomethylated states, enabling secretion or intracellular signaling, whereas hypermethylation produces fewer or no gene products, impairing these processes. Created in BioRender. Møller, M.—2025. https://BioRender.com/w17i636 (accessed on 4 January 2025).
Figure 1. Schematic representation of hypomethylation (left) and hypermethylation (right) and their effects on gene expression and protein synthesis. 1. Hypomethylation maintains open DNA strands, allowing transcription factors to bind and facilitate gene transcription. In contrast, hypermethylation results in closed DNA conformation, blocking transcription factor binding and suppressing transcription. 2. In hypomethylated regions, mRNA is efficiently transcribed and translated into proteins in ribosomes. Hypermethylation, however, reduces mRNA availability or alters splicing, leading to limited or absent translation. 3. As a result, gene products are fully expressed in hypomethylated states, enabling secretion or intracellular signaling, whereas hypermethylation produces fewer or no gene products, impairing these processes. Created in BioRender. Møller, M.—2025. https://BioRender.com/w17i636 (accessed on 4 January 2025).
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Figure 2. PRISMA flow diagram for identification, screening and included studies on pituitary adenomas and DNA methylation. Numbers in blue font represent the total number of studies at each stage of the process, as indicated within the respective boxes.
Figure 2. PRISMA flow diagram for identification, screening and included studies on pituitary adenomas and DNA methylation. Numbers in blue font represent the total number of studies at each stage of the process, as indicated within the respective boxes.
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Table 2. Summary of findings regarding associations between methylation levels and gene expression and clinical outcomes.
Table 2. Summary of findings regarding associations between methylation levels and gene expression and clinical outcomes.
Methylation AssociationMethodGenes/PathwaysClinical OutcomesKey Findings
Correlation with methylation and expression/clinical behaviorMethylation-restricted
digestion		GH-gene, p16, RB1 promoter, POMC promoter, 1A DMRPOMC promoter methylation repressed expression.
Partial methylation of 1A DMR induced higher expression		No clinical function of these findings
Methylation-specific PCRCDKN2A, DAP kinase, GADD45g, RASSF1A, RASSF3, Gal-3, SOCS-1, FGFR2-IIIb, GSTP1, NNAT, EFEMP1, sFRP4, p21, p27, WIF1, GIPR, DNMT1, DNMT3A, DNMT3B, LAMA2, TERT, GNAS, POMC promoterRASSF1A, RASSF3—somatotroph tumorigensis,
SOCS-1—NFPAs,
FGFR2-IIIb—oncogenic signals, GSTP1—invasiveness and response to SSA,
sFRP4—aggresiveness,
p21 and p27—invasiveness, DNMT1, DNMT3A, and DNMT3B—oncogenic factors, TERT—disease progression, POMC promoter		Related primarily to invasive behavior
Chip-basedSTAT5A, RHOD, GALNT9, RASSF1, CDKN1A, TP73, STAT3, HMGA2, FAM163A, HIF3A, PRSS8Hypermethylated in NFPADifferent significant sites for NFPA, minimal overlap between studies
PHYHD1, LTBR, MYBPHL, C22orf42, PRR5, ANKDD1A, RAB13, CAMKV, KIFC3, WNT4, and STAT6Invasive behavior of NFPA
FAM90A1, ETS2, STAT6, MYT1L, ING2, and KCNK1Regrowth of NFPA
C7orf50, GNG7, and BAHCC1Methylated in pituitary adenomas
DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3Hub proteins for NFPA
Myc-associated protein X transcription factor binding sitesGlobally hypomethylated in NFPA
Other methodsMETTL3Regulation of cell growth or hormone secretion of GH-PA
Probable association (no significant findings)Methylation-specific PCRE-cadherin, C22orf3, MEG3, p15INK4b, RB1, CDH13, FGFR2, CDKN2C, RIZ1, SSTR5, Pttg1, autosomal and X-linked genes, CHST7 promoterE-cadherin—aggressive behavior,
MEG3—pathogenesis of NFPAs,
CDH13—pathogenesis,
Autosomal and X-linked genes—aggressiveness and response to treatment,
CHST7—promoter for lineage		Related to pathogenesis
No association with clinical differencesMethylation-restricted
digestion		MGMT promoterPoor predictor of outcome of treatment with TemozolomideNo significant association between methylation status of MGMT and clinical outcomes
Methylation-specific PCRp16, CDH1, p18INK4C, NDRG2, MGMT, GADD45b, CDH1, MEN1, hTERT promoter, STAT3 promoter p16—aggressiveness,
MGMT—expression,
NDRG2—invasiveness,
STAT3 promoter—prognostic marker		Genes otherwise proven related to aggressive pathologies showed no association with aggressive behavior in pituitary adenomas
Chip-basedTERT, Myc-associated protein X transcription factor binding sitesTERT—pathogenesis,
Myc-associated protein X transcription factor binding sites
Other methodsZAR1, MGMT promoter, LINE-1MGMT promoter—immunoreactivity,
LINE-1—aggresiveness
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Møller, M.W.; Nortvig, M.J.; Andersen, M.S.; Poulsen, F.R. DNA Methylation in Pituitary Adenomas: A Scoping Review. Int. J. Mol. Sci. 2025, 26, 531. https://doi.org/10.3390/ijms26020531

AMA Style

Møller MW, Nortvig MJ, Andersen MS, Poulsen FR. DNA Methylation in Pituitary Adenomas: A Scoping Review. International Journal of Molecular Sciences. 2025; 26(2):531. https://doi.org/10.3390/ijms26020531

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Møller, Morten Winkler, Mathias Just Nortvig, Mikkel Schou Andersen, and Frantz Rom Poulsen. 2025. "DNA Methylation in Pituitary Adenomas: A Scoping Review" International Journal of Molecular Sciences 26, no. 2: 531. https://doi.org/10.3390/ijms26020531

APA Style

Møller, M. W., Nortvig, M. J., Andersen, M. S., & Poulsen, F. R. (2025). DNA Methylation in Pituitary Adenomas: A Scoping Review. International Journal of Molecular Sciences, 26(2), 531. https://doi.org/10.3390/ijms26020531

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