Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase () mutation and is associated with a clinically favorable overall survival (OS); however, the exact underlying mechanism remains unclear. Long non-coding RNAs (lncRNAs) have recently been suggested to regulate carcinogenesis and prognosis in cancer patients. Here, we performed in silico analyses using low-grade gliomas from datasets obtained from The Cancer Genome Atlas to investigate the effects of ceRNA with 1p/19q codeletion on ODs. Thus, we selected modules of differentially expressed genes that were closely related to 1p/19q codeletion traits using weighted gene co-expression network analysis and constructed 16 coding RNA-miRNA-lncRNA networks. The ceRNA network participated in ion channel activity, insulin secretion, and collagen network and extracellular matrix (ECM) changes. In conclusion, ceRNAs with a 1p/19q codeletion can create different tumor microenvironments via potassium ion channels and ECM composition changes; furthermore, differences in OS may occur. Moreover, if extrapolated to gliomas, our results can provide insights into the consequences of identical gene expression, indicating the possibility of tracking different biological processes in different subtypes of glioma.
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http://dx.doi.org/10.3390/cancers12092543 | DOI Listing |
Neuroradiology
January 2025
Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Background And Purpose: The cortical high-flow sign has been more commonly reported in oligodendroglioma, IDH-mutant and 1p/19q-codeleted (ODG IDHm-codel) compared to diffuse glioma with IDH-wildtype or astrocytoma, IDH-mutant. Besides tumor types, higher grades of glioma might also contribute to the cortical high flow. Therefore, we investigated whether the histological cortical vascular density or CNS WHO grade was associated with the cortical high-flow sign in patients with ODG IDHm-codel.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, China.
Purpose: To investigate the prognostic significance of contrast enhancement (CE) in grade 2 oligodendroglioma (ODG) and explore its clinical implications.
Methods: Patients diagnosed with isocitrate dehydrogenase (IDH)-mutant, 1p/19q co-deleted ODG between 2009 and 2016 were retrospectively enrolled from a single institution. The presence of CE was identified on preoperative MRIs, and clinical, radiologic, and histopathological data that was extracted.
Ther Clin Risk Manag
January 2025
Department of Oncology, Gaoxin Branch of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.
Background: The relationship between molecular phenotype and prognosis in high-grade gliomas (WHO III and IV, HGG) treated with radiotherapy and chemotherapy is not fully understood and needs further exploration.
Methods: The HGG patients following surgery and treatment with radiotherapy and chemotherapy. Univariate and multivariate Cox analyses were used to assess the independent prognostic factors.
J Neuropathol Exp Neurol
January 2025
Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Neurooncol Adv
December 2024
Division for Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background: This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.
Methods: Radiomic features were extracted from preoperative MRI of = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe).
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