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Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition)
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Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 1 August 2025 | Viewed by 8252

Special Issue Editors

Dr. Kazunori Kageyama

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Guest Editor
Department of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, 5 Zaifu-cho, Hirosaki, Aomori 036-8562, Japan
Interests: Cushing’s disease; hypopituitarism; pituitary tumor; proopiomelanocortin; stress
Special Issues, Collections and Topics in MDPI journals
Dr. Mitsuru Nishiyama

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Guest Editor
Health Care Center, Kochi University, 1-5-2 Akebono-cho, Kochi 780-8520, Japan
Interests: corticotropin-releasing hormone; Cushing’s disease; glucocorticoid; hypopituitarism; proopiomelanocortin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of "Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy" (https://www.mdpi.com/journal/cancers/special_issues/PTMDT).

Pituitary tumors present a variety of hormonal activities and clinical features, from overt to subtle. Functioning pituitary tumors are defined by the autonomous/dysregulated secretion of pituitary hormones. In this Special Issue, we explore recent advances in the molecular insights, diagnosis, and targeted therapy of pituitary tumors. For example, Cushing’s disease is defined by the autonomous secretion of ACTH and excess cortisol production, with their obvious manifestation of the clinical features of Cushing’s disease. Mutations in the ubiquitin-specific protease (USP) 8 or USP48 genes have been detected in Cushing’s disease. Hormones produced from pituitary tumors sometimes induce severe complications such as hypertension, hyperglycemia, osteoporosis, infections, atherosclerosis, and mental disorders. The pathophysiological characteristics of hormone production and pituitary adenoma cells should be elucidated. In addition, the usefulness and accuracy of the recent diagnostic criteria for pituitary tumors also need to be evaluated. The primary treatment for some types of pituitary tumors may be surgical excision of the adenoma from the pituitary; however, curative surgery is still challenging, and additional therapies are required to treat the resulting hypersecretion of hormones and tumor growth. This Special Issue will include original basic/translational/clinical research articles and reviews on aspects related to the pathophysiology, diagnosis, and potential treatment of pituitary tumors.

We look forward to receiving your contributions.

Sincerely,

Dr. Kazunori Kageyama
Dr. Mitsuru Nishiyama
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • acromegaly
  • adrenocorticotropic hormone
  • Cushing’s disease
  • diagnosis
  • growth hormone
  • pituitary tumor
  • proliferation
  • transcriptional factor
  • treatment

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Published Papers (7 papers)

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Research

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12 pages, 2584 KiB  
Article
Clinical, Laboratory, and Imaging Features Associated with Arginine Vasopressin Deficiency (Central Diabetes Insipidus) in Erdheim–Chester Disease (ECD)
by Sonal Vaid, Juvianee Estrada-Veras, William A. Gahl, Nicholas Patronas, Rahul H. Dave, Fady Hannah-Shmouni, Kevin O’Brien and Skand Shekhar
Cancers 2025, 17(5), 824; https://doi.org/10.3390/cancers17050824 - 27 Feb 2025
Viewed by 198
Abstract
Purpose: Erdheim–Chester disease (ECD) is an L Group Langerhans histiocytosis associated with pathogenic variants within the MAPK pathways, most commonly the BRAF gene. We analyzed prevalence, genetic, biochemical, and pituitary imaging features associated with arginine vasopressin deficiency (AVP-D), one of the most common [...] Read more.
Purpose: Erdheim–Chester disease (ECD) is an L Group Langerhans histiocytosis associated with pathogenic variants within the MAPK pathways, most commonly the BRAF gene. We analyzed prevalence, genetic, biochemical, and pituitary imaging features associated with arginine vasopressin deficiency (AVP-D), one of the most common endocrinopathies in ECD. Methods: A cross-sectional descriptive study of 61 subjects with ECD was conducted at a clinical research center from January 2011 to December 2018, with molecular genetics, baseline biochemical and pituitary endocrine function studies, and dedicated pituitary MRI (or CT) studies. AVP-D and anterior pituitary endocrinopathies (hypothyroidism, hypogonadism, adrenal insufficiency and panhypopituitarism) were assessed. Students’ t-tests, nonparametric tests, Fisher’s exact tests, and logistic regression were employed for analysis. Results: In total, 22 out of 61 subjects (36%; 19 males and 3 females) had AVP-D; 18 subjects with AVP-D were in active treatment with desmopressin. Those with versus without AVP-D were younger [mean (±SD): 50.00 (±10.45) vs. 56.72 (±10.45) years], had higher prevalence of BRAF V600E pathogenic variants [68% vs. 43%], lower IGF-1 [mean (±SD): 137.05 (±67.97) vs. 175.92 (±61.89) ng/mL], lower urine osmolality [416.00 (250.00–690.00) vs. 644.50 (538.75–757.75)) mOsm/kg], and a higher burden of central hypogonadism [81.82% vs. 36.00%], central hypothyroidism [23% vs. 2.5%], panhypopituitarism [41% vs. 0%], anterior pituitary endocrine deficits, absent posterior pituitary bright spots [63.64% vs. 20.51%], and abnormal pituitary imaging. In adjusted models, [OR (95%CI)] BRAF V600E mutation [7.38 (1.84–39.01)], central hypogonadism [6.193 (1.44–34.80)], primary hypothyroidism [13.89 (1.401–406.5)], absent posterior pituitary bright spot [12.84 (3.275–65.04)], and abnormal pituitary imaging [10.60 (2.844–48.29)] were associated with higher odds of having AVP-D. Conclusions: AVP-D is common in ECD and accompanied by a higher burden of pituitary endocrinopathies, BRAF V600E pathogenic variants, abnormal pituitary imaging, and absent posterior pituitary bright spots. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
►▼ Show Figures

Figure 1

Figure 1
<p>Comparisons of urine and serum osmolality between those with and without AVP-D. Urine osmolality was higher in those without AVP-D compared to those with AVP-D as denoted by double asterisks (<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 2
<p>T1-weighted MRI scans of subjects with AVP-D. (<b>A</b>) (coronal) and (<b>B</b>) (sagittal): Loss of posterior pituitary bright spot in a subject with AVP-D. (<b>C</b>,<b>D</b>): Suprasellar mass in a subject with DI. (<b>E</b>,<b>F</b>): Thickened pituitary stalk at hypothalamus in a subject with AVP-D and loss of posterior pituitary bright spot. Red arrows point to the lesion.</p> Full article ">
17 pages, 581 KiB  
Article
Associations of TRAF2 (rs867186), TAB2 (rs237025), IKBKB (rs13278372) Polymorphisms and TRAF2, TAB2, IKBKB Protein Levels with Clinical and Morphological Features of Pituitary Adenomas
by Balys Remigijus Zaliunas, Greta Gedvilaite-Vaicechauskiene, Loresa Kriauciuniene, Arimantas Tamasauskas and Rasa Liutkeviciene
Cancers 2024, 16(14), 2509; https://doi.org/10.3390/cancers16142509 - 10 Jul 2024
Cited by 1 | Viewed by 948
Abstract
Aim: The aim of this study was to determine associations of TRAF2 (rs867186), TAB2 (rs237025), IKBKB (rs13278372) gene polymorphisms and TRAF2, TAB2, IKBKB protein levels with clinical and morphological features of pituitary adenomas (PAs). Methods: This case–control study included 459 individuals [...] Read more.
Aim: The aim of this study was to determine associations of TRAF2 (rs867186), TAB2 (rs237025), IKBKB (rs13278372) gene polymorphisms and TRAF2, TAB2, IKBKB protein levels with clinical and morphological features of pituitary adenomas (PAs). Methods: This case–control study included 459 individuals divided into two groups: a control group (n = 320) and a group of individuals with PAs (n = 139). DNA from peripheral blood leukocytes was isolated using salt precipitation and column method. Real-time PCR was used for TRAF2 (rs867186), TAB2 (rs237025), and IKBKB (rs13278372) SNP genotyping, and TRAF2, TAB2, IKBKB protein concentration measurements were performed by immunoenzymatic analysis tests using a commercial ELISA kit according to the manufacturer’s recommendations. The labeling index Ki-67 was determined by immunohistochemical analysis using a monoclonal antibody (clone SP6; Spring Bioscience Corporation). Statistical data analysis was performed using the programs "IMB SPSS Statistics 29.0". Results: We found significant differences in TRAF2 (rs867186) genotypes (AA, AG, GG) between groups: 79.1%, 17.3%, 3.6% vs. 55.3%, 20.9%, 23.8% (p < 0.001). The G allele was less frequent in the PA group than in controls (12.2% vs. 34.2%, p < 0.001). The AG and GG genotypes reduced PA occurrence by 1.74-fold and 9.43-fold, respectively, compared to AA (p < 0.001). In the dominant model, GG and AG genotypes reduced PA odds by 3.07-fold, while in the recessive model, the GG genotype reduced PA odds by 8.33-fold (p < 0.001). Each G allele decreased PA odds by 2.49-fold in the additive model (p < 0.001). Microadenomas had significant genotype differences compared to controls: 81.3%, 18.8%, 0.0% vs. 55.3%, 20.9%, 23.8% (p < 0.001), with the G allele being less frequent (9.4% vs. 34.2%, p < 0.001). In macroadenomas, genotype differences were 78%, 16.5%, 5.5% vs. 55.3%, 20.9%, 23.8% (p < 0.001), and the G allele was less common (13.7% vs. 34.2%, p < 0.001). The dominant model showed that GG and AG genotypes reduced microadenoma odds by 3.5-fold (p = 0.001), and each G allele reduced microadenoma odds by 3.1-fold (p < 0.001). For macroadenomas, the GG genotype reduced odds by 6.1-fold in the codominant model (p < 0.001) and by 2.9-fold in GG and AG genotypes combined compared to AA (p < 0.001). The recessive model indicated the GG genotype reduced macroadenoma odds by 5.3-fold (p < 0.001), and each G allele reduced odds by 2.2-fold in the additive model (p < 0.001). Conclusions: The TRAF2 (rs867186) G allele and GG genotype are significantly associated with reduced odds of pituitary adenomas, including both microadenomas and macroadenomas, compared to the AA genotype. These findings suggest a protective role of the G allele against the occurrence of these tumors. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
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Figure 1

Figure 1
<p>Serum <span class="html-italic">IKBKB</span>, <span class="html-italic">TRAF2</span>, and <span class="html-italic">TAB2</span> levels between groups (Mann–Whitney U test was used). (<b>A</b>) Serum <span class="html-italic">IKBKB</span> levels (ng/mL) in PA and control groups; (<b>B</b>) serum <span class="html-italic">TRAF2</span> levels (ng/mL) in PA and control groups; (<b>C</b>) serum <span class="html-italic">TAB2</span> levels (pg/mL) in PA and reference groups; (<b>D</b>) serum <span class="html-italic">IKBKB</span> levels (ng/mL) between microadenoma and macroadenoma groups; (<b>E</b>) serum <span class="html-italic">TRAF2</span> levels (ng/mL) between microadenoma and macroadenoma groups; (<b>F</b>) serum <span class="html-italic">TAB2</span> levels (pg/mL) between microadenoma and macroadenoma groups.</p> Full article ">
14 pages, 2467 KiB  
Article
Genome-Wide DNA Methylation Profiling as a Prognostic Marker in Pituitary Adenomas—A Pilot Study
by Morten Winkler Møller, Marianne Skovsager Andersen, Bo Halle, Christian Bonde Pedersen, Henning Bünsow Boldt, Qihua Tan, Philipp Sebastian Jurmeister, Grayson A. Herrgott, Ana Valeria Castro, Jeanette K. Petersen and Frantz Rom Poulsen
Cancers 2024, 16(12), 2210; https://doi.org/10.3390/cancers16122210 - 13 Jun 2024
Viewed by 1188
Abstract
Background: The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been [...] Read more.
Background: The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been proposed, but no study has linked this epigenetic variance with regrowth potential and the clinical heterogeneity of nonfunctioning pituitary adenomas. This study aimed to investigate whether DNA methylation profiling can be useful as a clinical prognostic marker. Methods: A DNA methylation analysis by Illumina’s MethylationEPIC array was performed on 54 pituitary macroadenomas from patients who underwent transsphenoidal surgery during 2007–2017. Twelve patients were excluded due to an incomplete postoperative follow-up, degenerated biobank-stored tissue, or low DNA methylation quality. For the quantitative measurement of the tumor regrowth rate, we conducted a 3D volumetric analysis of tumor remnant volume via annual magnetic resonance imaging. A linear mixed effects model was used to examine whether different DNA methylation clusters had different regrowth patterns. Results: The DNA methylation profiling of 42 tissue samples showed robust DNA methylation clusters, comparable with previous findings. The subgroup of 33 nonfunctioning pituitary adenomas of an SF1-lineage showed five subclusters with an approximately unbiased score of 86%. There were no overall statistically significant differences when comparing hazard ratios for regrowth of 100%, 50%, or 0%. Despite this, plots of correlated survival estimates suggested higher regrowth rates for some clusters. The mixed effects model of accumulated regrowth similarly showed tendencies toward an association between specific DNA methylation clusters and regrowth potential. Conclusion: The DNA methylation profiling of nonfunctioning pituitary adenomas may potentially identify adenomas with increased growth and recurrence potential. Larger validation studies are needed to confirm the findings from this explorative pilot study. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
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Figure 1

Figure 1
<p>PRISMA flowchart of patient inclusion.</p> Full article ">Figure 2
<p>Unsupervised hierarchical cluster analysis (UHCA) of 42 tissue samples using full-genome (* is a symbol for all 766,404 CpGs included) methylation profiles. This shows distinct clustering that correlated with immunohistochemical staining for transcription factors SF1, TPIT, and PIT1.</p> Full article ">Figure 3
<p>Unsupervised hierarchical cluster analysis (UHCA) via a x200 bootstrap resampling procedure on 33 SF1-lineage NFPAs. Results demonstrate five robust clusters (AU = 86%), defined as 1–5. Pre- and postoperative tumor sizes (cm<sup>3</sup>) were grouped, while invasive growth, primary or repeated surgery, and the need for reintervention used binary outcomes (yes/no). Accumulated regrowth percentage was grouped into categorical values.</p> Full article ">Figure 4
<p>A heatmap illustrating the top 5000 most variable CpGs based on beta values reveals clustering patterns that align with both the comprehensive genomic analysis and clinical data. Notably, cluster 1 exhibits a notable reorganization relative to other clusters, stemming from its greater variability in differential methylation positions (DMPs) compared to the remaining clusters; however, discernible methylation patterns among the other clusters are not readily apparent.</p> Full article ">Figure 5
<p>Survival analysis based on Kaplan–Meier estimates: (<b>A</b>) for patients undergoing reintervention (reoperation or radiotherapy) during the follow-up period; (<b>B</b>) for tumor remnants showing no increase in volume; (<b>C</b>) for tumors with more than a 50% increase in volume; and (<b>D</b>) for tumors with more than a 100% increase in volume during the study period.</p> Full article ">Figure 6
<p>Linear mixed effects model of methylation profiles and regrowth potential, showing clear tendencies for different regrowth patterns dependent on methylation profiles. Cluster 2 has apparent, but statistically insignificant, regrowth potential compared to clusters 1 and 3.</p> Full article ">
13 pages, 1740 KiB  
Article
Alternations of Blood Pressure Following Surgical or Drug Therapy for Prolactinomas
by Yijun Cheng, Dapeng Wang, Hao Tang, Debing Tong, Weiguo Zhao, Shaojian Lin, Hong Yao, Wenwen Lv, Xun Zhang, Li Xue, Hanbing Shang and Zhe Bao Wu
Cancers 2024, 16(4), 726; https://doi.org/10.3390/cancers16040726 - 9 Feb 2024
Cited by 1 | Viewed by 1715
Abstract
Several subtypes of pituitary neuroendocrine tumors (PitNETs), such as acromegaly and Cushing’s disease, can result in hypertension. However, whether prolactinoma is associated with this complication remains unknown. Moreover, the effect of treatment with surgery or drugs on blood pressure (BP) is unknown. Herein, [...] Read more.
Several subtypes of pituitary neuroendocrine tumors (PitNETs), such as acromegaly and Cushing’s disease, can result in hypertension. However, whether prolactinoma is associated with this complication remains unknown. Moreover, the effect of treatment with surgery or drugs on blood pressure (BP) is unknown. Herein, a retrospective study reviewed 162 patients with prolactinoma who underwent transsphenoidal surgery between January 2005 and December 2022. BP measurements were performed 1 day before and 5 days after surgery. Accordingly, patients’ medical characteristics were recorded. In addition, in situ rat and xenograft nude-mice prolactinoma models have been used to mimic prolactinoma. In vivo BP and serum prolactin (PRL) levels were measured after cabergoline (CAB) administration in both rats and mice. Our data suggest that surgery can effectively decrease BP in prolactinoma patients with or without hypertension. The BP-lowering effect was significantly associated with several variables, including age, sex, disease duration, tumor size, invasion, dopamine agonists (DAs)-resistance, recurrence, and preoperative PRL levels. Moreover, in situ and xenograft prolactinomas induced BP elevation, which was alleviated by CAB treatment without and with a statistical difference in rats and mice, respectively. Thus, surgery or CAB can decrease BP in prolactinoma, indicating that pre- and postoperative BP management becomes essential. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
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Figure 1

Figure 1
<p>Pre- and postoperative BP changes in patients with prolactinoma. (<b>A</b>) Pre- and postoperative measurements of systolic and diastolic BP in all patients with prolactinoma. (<b>B</b>) Pre- and postoperative measurements of systolic and diastolic BP in hypertensive patients with prolactinoma. (<b>C</b>) Pre- and postoperative measurements of systolic and diastolic BP in nonhypertensive patients with prolactinoma. ** <span class="html-italic">p</span> &lt; 0.01 vs. preoperative group; * <span class="html-italic">p</span> &lt; 0.05 vs. preoperative group. Red indicates pre-operation and green indicates post-operation.</p> Full article ">Figure 2
<p>BP changes in patients with or without hormone control. * <span class="html-italic">p</span> &lt; 0.05 vs. hormone control group.</p> Full article ">Figure 3
<p>BP changes in rodent prolactinoma models. (<b>A</b>) Representative MRI images of in situ prolactinoma in each group. (<b>B</b>) Pituitary/tumor volumes of in situ prolactinomas in different rat groups; <span class="html-italic">n</span> = 4. (<b>C</b>) Serum PRL levels in different in situ prolactinoma groups; <span class="html-italic">n</span> = 8. (<b>D</b>) Systolic and diastolic BP in different in situ prolactinoma groups; <span class="html-italic">n</span> = 4. (<b>E</b>) Representative photos of xenograft prolactinoma in each group. The red circles shows the xenograft tumors. (<b>F</b>) Systolic BP and serum PRL levels in different xenograft prolactinoma groups; <span class="html-italic">n</span> = 6. (<b>G</b>) Correlation between changes in systolic BP and serum PRL level in the xenograft prolactinoma (Ctrl) group; <span class="html-italic">n</span> = 6. The values are presented as mean ± SD; * <span class="html-italic">p</span> &lt; 0.05 vs. sham group; ** <span class="html-italic">p</span> &lt; 0.01 vs. sham group; # <span class="html-italic">p</span> &lt; 0.05 vs. Ctrl group; ## <span class="html-italic">p</span> &lt; 0.01 vs. Ctrl group; NS, no significance.</p> Full article ">
20 pages, 1425 KiB  
Article
The Influence of Telomere-Related Gene Variants, Serum Levels, and Relative Leukocyte Telomere Length in Pituitary Adenoma Occurrence and Recurrence
by Greta Gedvilaite, Loresa Kriauciuniene, Arimantas Tamasauskas and Rasa Liutkeviciene
Cancers 2024, 16(3), 643; https://doi.org/10.3390/cancers16030643 - 2 Feb 2024
Cited by 1 | Viewed by 1272
Abstract
In this study, we examined 130 patients with pituitary adenomas (PAs) and 320 healthy subjects, using DNA samples from peripheral blood leukocytes purified through the DNA salting-out method. Real-time polymerase chain reaction (RT-PCR) was used to assess single nucleotide polymorphisms (SNPs) and relative [...] Read more.
In this study, we examined 130 patients with pituitary adenomas (PAs) and 320 healthy subjects, using DNA samples from peripheral blood leukocytes purified through the DNA salting-out method. Real-time polymerase chain reaction (RT-PCR) was used to assess single nucleotide polymorphisms (SNPs) and relative leukocyte telomere lengths (RLTLs), while enzyme-linked immunosorbent assay (ELISA) was used to determine the levels of TERF1, TERF2, TNKS2, CTC1, and ZNF676 in blood serum. Our findings reveal several significant associations. Genetic associations with pituitary adenoma occurrence: the TERF1 rs1545827 CT + TT genotypes were linked to 2.9-fold decreased odds of PA occurrence. Conversely, the TNKS2 rs10509637 GG genotype showed 6.5-fold increased odds of PA occurrence. Gender-specific genetic associations with PA occurrence: in females, the TERF1 rs1545827 CC + TT genotypes indicated 3.1-fold decreased odds of PA occurrence, while the TNKS2 rs10509637 AA genotype was associated with 4.6-fold increased odds. In males, the presence of the TERF1 rs1545827 T allele was associated with 2.2-fold decreased odds of PA occurrence, while the TNKS2 rs10509637 AA genotype was linked to a substantial 10.6-fold increase in odds. Associations with pituitary adenoma recurrence: the TNKS2 rs10509637 AA genotype was associated with 4.2-fold increased odds of PA recurrence. On the other hand, the TERF1 rs1545827 CT + TT genotypes were linked to 3.5-fold decreased odds of PA without recurrence, while the TNKS2 rs10509637 AA genotype was associated with 6.4-fold increased odds of PA without recurrence. Serum TERF2 and TERF1 levels: patients with PA exhibited elevated serum TERF2 levels compared to the reference group. Conversely, patients with PA had decreased TERF1 serum levels compared to the reference group. Relative leukocyte telomere length (RLTL): a significant difference in RLTL between the PA group and the reference group was observed, with PA patients having longer telomeres. Genetic associations with telomere shortening: the TERF1 rs1545827 T allele was associated with 1.4-fold decreased odds of telomere shortening. In contrast, the CTC1 rs3027234 TT genotype was linked to 4.8-fold increased odds of telomere shortening. These findings suggest a complex interplay between genetic factors, telomere length, and pituitary adenoma occurrence and recurrence, with potential gender-specific effects. Furthermore, variations in TERF1 and TNKS2 genes may play crucial roles in telomere length regulation and disease susceptibility. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
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Figure 1

Figure 1
<p>TNKS2, CTC1, ZNF676, TERF1, and TERF2 protein-protein connections.</p> Full article ">Figure 2
<p>Serum TERF2 levels in PA and reference groups. * Mann–Whitney U-test was used.</p> Full article ">Figure 3
<p>Serum TERF1 levels in PA and reference groups. * Mann–Whitney U-test was used.</p> Full article ">Figure 4
<p>Serum TNKS2 levels in PA and reference groups. * Mann–Whitney U-test was used.</p> Full article ">Figure 5
<p>Serum CTC1 levels in PA and reference groups. * Student’s <span class="html-italic">t</span>-test was used.</p> Full article ">Figure 6
<p>Serum ZNF676 levels in PA and reference groups. * Mann–Whitney U-test was used.</p> Full article ">Figure 7
<p>Relative leukocyte telomere length between PA and reference groups. * Mann–Whitney U-test was used.</p> Full article ">

Review

Jump to: Research

15 pages, 4572 KiB  
Review
Double PitNETs: A Case Report and Literature Review
by Mitsuru Nishiyama, Noriaki Fukuhara, Hiroshi Nishioka and Shozo Yamada
Cancers 2025, 17(4), 675; https://doi.org/10.3390/cancers17040675 - 17 Feb 2025
Viewed by 325
Abstract
Double pituitary neuroendocrine tumors (double PitNETs) are two distinct tumors in the same gland and are infrequent in clinical practice. In typical double PitNETs, an MRI detects two separate tumors that are diagnosed by pathology; they could also appear as a single tumor, [...] Read more.
Double pituitary neuroendocrine tumors (double PitNETs) are two distinct tumors in the same gland and are infrequent in clinical practice. In typical double PitNETs, an MRI detects two separate tumors that are diagnosed by pathology; they could also appear as a single tumor, and pathology would then identify the two independent tumors. A literature review was conducted, and 142 cases were analyzed to determine the characteristics of double PitNETs. Of these cases, acromegaly (45.5%) was the most common clinical feature, followed by Cushing’s disease (35.1%) and prolactinoma (17.9%), indicating that double PitNETs are usually noticed by hormonal excess symptoms due to at least one functional tumor. The pathological analysis of 284 tumors showed that somatotroph (28.9%) and corticotroph (26.8%) tumors were predominant, with a recent increase in the proportion of gonadotroph tumors. Regarding transcription factors, 51.1% were of GH-PRL-TSH PIT1-lineage, 26.1% ACTH TPIT-lineage, and 17.9% LH-FSH SF1-lineage. The radiological analysis of 82 cases revealed that double tumors (45.1%) and single tumors (47.6%) were comparable, suggesting that double PitNETs are often detected as a single tumor, and attention should be paid to hidden micro-tumors during surgery. Double PitNETs are complicated by a wide variety of clinical, radiological, and pathological findings, but diagnostic and therapeutic approaches are advancing. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
►▼ Show Figures

Figure 1

Figure 1
<p>Magnetic resonance images: T1-weighted (<b>A</b>), T2-weighted (<b>B</b>), gadolinium-enhanced T1-weighted (<b>C</b>) in the coronal section, and T1-weighted image in the sagittal section (<b>D</b>), showing a 2.5 cm diameter macro-tumor with suspected cystic degeneration in the upper portion.</p> Full article ">Figure 2
<p>Microscopic examination of the pituitary tumor with hematoxylin/eosin (HE) staining and immunohistochemical staining of FSH, SF1, GH, TSH, and PIT1 (magnification ×400). The upper lesion (*1) in the MRI (same as <a href="#cancers-17-00675-f001" class="html-fig">Figure 1</a>) was determined as a gonadotroph tumor (indicated by white arrowheads in the MRI; FSH+, SF1+ in pathology), and the lower lesion (*2) was determined as a mature plurihormonal PIT1-lineage tumor (indicated by red arrows in the MRI; GH++, TSH±, PIT1+ in pathology).</p> Full article ">
26 pages, 3258 KiB  
Review
Topography and Radiological Variables as Ancillary Parameters for Evaluating Tissue Adherence, Hypothalamic–Pituitary Dysfunction, and Recurrence in Craniopharyngioma: An Integrated Multidisciplinary Overview
by Rosalinda Calandrelli, Gabriella D’Apolito, Matia Martucci, Carolina Giordano, Chiara Schiarelli, Giammaria Marziali, Giuseppe Varcasia, Luca Ausili Cefaro, Sabrina Chiloiro, Simone Antonio De Sanctis, Simona Serioli, Francesco Doglietto and Simona Gaudino
Cancers 2024, 16(14), 2532; https://doi.org/10.3390/cancers16142532 - 13 Jul 2024
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Abstract
Craniopharyngiomas continue to present a challenge in clinical practice due to their heterogeneity and unpredictable adherence to vital neurovascular structures, particularly the hypothalamus. This results in different degrees of hypothalamus–pituitary axis dysfunction and a lack of uniform consensus and treatment guidelines regarding optimal [...] Read more.
Craniopharyngiomas continue to present a challenge in clinical practice due to their heterogeneity and unpredictable adherence to vital neurovascular structures, particularly the hypothalamus. This results in different degrees of hypothalamus–pituitary axis dysfunction and a lack of uniform consensus and treatment guidelines regarding optimal management. MRI and CT are complementary techniques in the preoperative diagnostic phase, enabling the precise definition of craniopharyngioma size, shape, and consistency, as well as guiding classification into histopathological subtypes and topographical categories. Meanwhile, MRI plays a crucial role in the immediate postoperative period and follow-up stages by identifying treatment-related changes and residual tumors. This pictorial essay aims to provide an overview of the role of imaging in identifying variables indicative of the adherence degree to the hypothalamus, hypothalamic–pituitary dysfunction, the extent of surgical excision, and prognosis. For a more comprehensive assessment, we choose to distinguish the following two scenarios: (1) the initial diagnosis phase, where we primarily discuss the role of radiological variables predictive of adhesions to the surrounding neurovascular structures and axis dysfunction which may influence the choice of surgical resection; (2) the early post-treatment follow-up phase, where we discuss the interpretation of treatment-related changes that impact outcomes. Full article
(This article belongs to the Special Issue Pituitary Tumors: Molecular Insights, Diagnosis, and Targeted Therapy (2nd Edition))
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<p>Flow diagram of literature search and article selection. n: number.</p> Full article ">Figure 2
<p>Relationship between tumor and leptomeningeal surface of the third ventricular floor in craniopharyngiomas. Normal representation of pituitary gland and meningeal layers (<b>a</b>). Dura mater layer is illustrated in brown; pia mater layer is shown in red; arachnoid layer is shown in orange; nervous layer is shown in light brown; ependymal layer is represented in blue. The TVF is covered from the ventricular ependymal layer. Sellar–suprasellar category. Sellar–suprasellar CP in (<b>b</b>). The CP arises below the sellar diaphragm but grows within the infrasellar and/or intrasellar cavity. It is separated from the TVF by the leptomeningeal layer (arachnoid and pia mater). Suprasellar category. (1) Pseudo-third ventricle CP in (<b>c</b>). It originates in the suprasellar subarachnoid spaces and exhibits a cap-like adhesion to the outer leptomeningeal surface of the TVF. The arachnoid layer typically wraps around the tumor. (2) Secondary intraventricular CP in (<b>d</b>). The CP originates in the suprasellar subarachnoid spaces. Initially, it develops beneath the 3 V but, in late stages of development, it extends into the 3 V after breaking through the TVF (black arrows) and becomes intraventricular. It has attachment to the pial surface of the infundibulum and/or tuber cinereum, without an intervening arachnoid layer. Intraventricular category. (1) Strictly intraventricular CP in (<b>e</b>). The tumor is located above an intact TVF and protrudes into the ventricular cavity without an ependymal cellular layer or an identifiable neural tissue-covering layer. Not-strictly intraventricular CP in (<b>f</b>). It is considered a truly intra-hypothalamic lesion. It is situated within the ventricular floor, featuring an ependymal cellular layer and a nervous layer covering the tumor’s surface. The leptomeningeal surface of the TVF is interrupted (black arrows). CPs, craniopharyngiomas; TVF, third ventricular floor; 3 V, third ventricle; DS, dorsum sellae.</p> Full article ">Figure 3
<p>Topographical categories of craniopharyngiomas. Preoperative sagittal (<b>a</b>–<b>e</b>) and coronal (<b>f</b>–<b>j</b>) T2 magnetic resonance images show the five major topographical categories of craniopharyngiomas. Sellar–suprasellar category. Sellar–suprasellar CP (<b>a</b>–<b>f</b>): the chiasmatic cistern is occupied by the tumor, the PS is not visible, the chiasm is stretched upward (thick white arrow in (<b>a</b>)), the mammillary body angle (MBA) is acute (65°), and the hypothalamus (thin white arrows in (<b>f</b>)) is around the upper-third level of the tumor; the 3 V is free of tumor. Suprasellar category. Pseudo-third ventricle CP (<b>b</b>,<b>g</b>): the sella, chiasm cistern, and 3 V are occupied by a giant tumor; the PS is not visible, the chiasm is stretched upward (thick white arrow in (<b>b</b>)), the mammillary body angle is obtuse (120°), and the hypothalamus (thin white arrows in (<b>g</b>)) is around the upper third of the tumor. Secondary intraventricular CP (<b>c</b>,<b>h</b>): the chiasm cistern and the 3 V are occupied by the tumor, the PS is not visible, the chiasm is stretched forward (thick white arrow in (<b>c</b>)), the angle of the mammillary body is acute (40°), and the hypothalamus is located around the mid-third of the tumor (thin white arrows in (<b>h</b>)). Intraventricular category. Strictly intraventricular CPs (<b>d</b>,<b>i</b>): the chiasm cistern is tumor-free, the PS is entirely visible, the chiasm is compressed downward (thick white arrow in (<b>d</b>)), the angle of the mammillary body is acute (35°), and the hypothalamus is in the lower-middle third of the tumor (thin white arrows in (<b>h</b>)). Not-strictly intraventricular CPs (<b>e</b>,<b>j</b>): the chiasm cistern is occupied by tumor, the chiasm is compressed forward (thick white arrow in e), the angle of the mammillary body is hyperacute (30°), and the hypothalamus is located around the mid-third of the tumor (thin white arrows in (<b>j</b>)). 3 V, third ventricle; Ch, chiasm; MBA, mammillary body angle; t, tumor.</p> Full article ">Figure 4
<p>Key morphologic features in adamantinomatous and papillary craniopharyngiomas. CT scan (<b>a</b>,<b>f</b>); sagittal T1 (<b>b</b>,<b>g</b>), axial T2 (<b>c</b>,<b>h</b>), axial FLAIR (<b>d</b>,<b>i</b>), and sagittal T1 with contrast medium (<b>e</b>,<b>j</b>) MRI. Adamantinomatous craniopharyngioma (<b>a</b>–<b>e</b>). Note a predominantly cystic multilobulated suprasellar mass with an intratumoral coarse calcification pattern (black arrowhead in (<b>a</b>)). The MRI shows the following: T1 shortening and FLAIR/T2 hyperintensity within the multicystic tumor component due to machine oil-like proteinaceous fluid (white arrows in (<b>b</b>,<b>d</b>)); T1 hypointense elements representing calcifications (white arrowhead in (<b>b</b>)); rim enhancement of the wall of the cysts on T1 with a contrast medium (white arrow in (<b>e</b>)). Parenchymal perifocal edema-like changes along the left optic tract and chiasm are shown in (<b>d</b>) (*). Papillary craniopharyngioma (<b>f</b>–<b>j</b>). Note a predominantly solid suprasellar mass without evidence of calcifications. The solid component appears hyperdense on the CT (<b>f</b>) with a homogeneous enhancement; the small cystic component is caudal and shows the rim enhancement of the wall (white arrow in (<b>j</b>)).</p> Full article ">Figure 5
<p>Radiomics in craniopharyngioma. Machine learning (ML) and deep learning (DL) techniques are AI subfields that use different diagnostic algorithms in quantitative bioimaging analysis. ML modeling (<b>a</b>–<b>e</b>): The computer receives inputs and features to create its own program for the desired output. Steps include ROI segmentation, feature extraction, feature selection (first- and second-order, higher-order, and shaped-based), and classification using models like the Random Forest, SelectKBest, least absolute shrinkage and selection operator (LASSO), and Support Vector Machine (SVM). DL modeling (<b>a</b>,<b>f</b>,<b>e</b>): It is a subset of ML, where the computer receives inputs and identifies features to generate the desired output. Convolutional neural networks (CNNs) learn from vast data to train a model.</p> Full article ">
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