Background: Meningiomas are common brain tumors that are classified into three World Health Organization grades (benign, atypical and malignant) and are molecularly ill-defined tumors. The purpose of this study was identify molecular signatures unique to the different grades of meningiomas and to unravel underlying molecular mechanisms driving meningioma tumorigenesis.
Results: We have used a combination of gene expression microarrays and array comparative genomic hybridization (aCGH) to show that meningiomas of all three grades fall into two main molecular groups designated 'low-proliferative' and 'high-proliferative' meningiomas. While all benign meningiomas fall into the low-proliferative group and all malignant meningiomas fall into the high-proliferative group, atypical meningiomas distribute into either one of these groups. High-proliferative atypical meningiomas had an elevated median MIB-1 labeling index and a greater frequency of copy number aberrations (CNAs) compared to low-proliferative atypical meningiomas. Additionally, losses on chromosome 6q, 9p, 13 and 14 were found exclusively in the high-proliferative meningiomas. We have identified genes that distinguish benign low-proliferative meningiomas from malignant high-proliferative meningiomas and have found that gain of cell-proliferation markers and loss of components of the transforming growth factor-beta signaling pathway were the major molecular mechanisms that distinguish these two groups.
Conclusion: Collectively, our data suggests that atypical meningiomas are not a molecularly distinct group but are similar to either benign or malignant meningiomas. It is anticipated that identified molecular and CNA markers will potentially be more accurate prognostic markers of meningiomas.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2173907 | PMC |
http://dx.doi.org/10.1186/1476-4598-6-64 | DOI Listing |
Cureus
December 2024
Neurosurgery, Al-Azhar University, Giza, EGY.
Intradural disc herniation (IDDH) is a rare condition, accounting for less than 0.5% of herniated disc cases, primarily affecting the lumbar region and often presenting with severe nerve compression or cauda equina syndrome. This paper presents the case of a 60-year-old female with a history of hypertension, dyslipidemia, stroke, and hypothyroidism, who arrived with severe lower back pain, lower limb weakness, and urinary retention.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
F1000Res
January 2025
Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Introduction: Magnetic resonance imaging (MRI) is essential for brain imaging, but conventional methods rely on qualitative contrast, are time-intensive, and prone to variability. Magnetic resonance finger printing (MRF) addresses these limitations by enabling fast, simultaneous mapping of multiple tissue properties like T1, T2. Using dynamic acquisition parameters and a precomputed signal dictionary, MRF provides robust, qualitative maps, improving diagnostic precision and expanding clinical and research applications in brain imaging.
View Article and Find Full Text PDFNPJ Precis Oncol
January 2025
Department of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue Alley, Chengdu, China.
This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods.
View Article and Find Full Text PDFElectromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!