The role of genetic background in childhood-onset combined pituitary hormone deficiency (CPHD) has been extensively studied. The major contributors are the PROP1, POU1F1, LHX3, LHX4 and HESX1 genes coding transcription factors implicated in pituitary organogenesis. The clinical consequences of mutations encompass impaired synthesis of a growth hormone (GH) and one or more concurrent pituitary hormones (i.e. LH, FSH, TSH, PRL). Manifestation of the disorder may vary due to various mutation impacts on the final gene products or an influence of environmental factors during pituitary organogenesis. We describe the clinical and molecular characteristics of two brothers aged 47 and 39 years presenting an uncommon manifestation of congenital hypopituitarism. Sequencing of the PROP1, POU1F1, LHX3, LHX4 and HESX1 genes was performed to confirm the genetic origin of the disorder. A compound heterozygosity in the PROP1 gene has been identified for both probands. The first change represents a mutational hot spot (c.150delA, p.R53fsX164), whereas the second is a novel alteration (p.R112X) that leads to protein disruption. Based on precise genetic diagnosis, an in silico prediction of a p.R112X mutation on protein architecture was performed. The resulting clinical phenotype was surprisingly distinct compared to most patients with genetic alterations in PROP1 reported in the current literature. This may be caused by a residual activity of a newly identified p.R112X protein that preserves over 70 % of the homeodomain structure. This examination may confirm a key role of a DNA-binding homeodomain in maintaining PROP1 functionality and suggests a conceivable explanation of an unusual phenotype.
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http://dx.doi.org/10.1007/s13353-015-0328-z | DOI Listing |
Neurosurg Rev
January 2025
Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Surgical treatment of complex pituitary adenomas (PAs) presents a significant challenge. Here in, we compared the surgical outcomes between patients undergoing endoscope-assisted transcranial surgery and microscopic regimens to assess the safety and efficacy of endoscope-assisted transcranial surgery in treating complex PA. This retrospective case-control study was conducted at the First Affiliated Hospital of Soochow University, China.
View Article and Find Full Text PDFBMJ Case Rep
January 2025
Paediatrics, Bahrain Defence Force Royal Medical Services, Riffa, Bahrain.
This case report provides details of the first documented case of pituitary stalk interruption syndrome (PSIS) with coexistent focal cortical dysplasia (FCD) in a young boy. The child's initial presentation was an afebrile, generalised tonic-clonic seizure associated with postictal drowsiness. During his first episode, the physical examination revealed a short, obese child with a micropenis and left cryptorchidism.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
January 2025
Department of Neurology, Mayo Clinic, Rochester, Minnesota.
Background: Adamantinomatous craniopharyngiomas (ACPs) are slow-growing, cystic, highly morbid central nervous system tumors located adjacent to vital structures including the pituitary, hypothalamus, and optic chiasm. Tumor recurrence is common. Treatment relies on resection with or without adjuvant radiation and is highly individualized.
View Article and Find Full Text PDFCureus
December 2024
Neurological Surgery, Loyola University Medical Center, Maywood, USA.
Introduction Surgical resection remains a standard treatment of non-functioning pituitary adenomas (NFPA). These tumors have significant intratumoral variability of growth rates and texture hardness. This preliminary study aims to identify variations in gene expression of different locations and textures within the same tumor to better explain tumor pathophysiology.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.
Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.
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