Background: Childhood glioblastoma multiforme (GBM) is a highly aggressive disease with low survival, and its etiology, especially concerning germline genetic risk, is poorly understood. Mitochondria play a key role in putative tumorigenic processes relating to cellular oxidative metabolism, and mitochondrial DNA variants were not previously assessed for association with pediatric brain tumor risk.
Methods: We conducted an analysis of 675 mitochondrial DNA variants in 90 childhood GBM cases and 2789 controls to identify enrichment of mitochondrial variant associated with GBM risk. We also performed this analysis for other glioma subtypes including pilocytic astrocytoma. Nuclear-encoded mitochondrial gene variants were also analyzed.
Results: We identified m1555 A>G was significantly associated with GBM risk (adjusted OR 29.30, 95% CI 5.25-163.4, value 9.5 X 10). No association was detected for other subtypes. Haplotype analysis further supported the independent risk contributed by m1555 G>A, instead of a haplogroup joint effect. Nuclear-encoded mitochondrial gene variants identified significant associations in European (rs62036057 in , adjusted OR = 2.99, 95% CI 1.88-4.75, -value = 3.42 X 10) and Hispanic (rs111709726 in , adjusted OR = 3.57, 95% CI 1.99-6.40, -value = 1.41 X 10) populations in ethnicity-stratified analyses.
Conclusion: We report for the first time a potential role played by a functional mitochondrial ribosomal RNA variant in childhood GBM risk, and a potential role for both mitochondrial and nuclear-mitochondrial DNA polymorphisms in GBM tumorigenesis. These data implicate cellular oxidative metabolic capacity as a contributor to the etiology of pediatric glioblastoma.
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http://dx.doi.org/10.1093/noajnl/vdac045 | DOI Listing |
Quant Imaging Med Surg
January 2025
Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Background: Although the spatial heterogeneity of glioblastoma (GBM) can be clearly mapped by the habitats generated by magnetic resonance imaging (MRI), the means to accurately predicting the spatial location of local recurrence (SLLR) remains a significant challenge. The aim of this study was to identify the different degrees enhancement of GBM, including the nontumor component and tumor component, and determine their relationship with SLLR.
Methods: A retrospective analysis was performed from three tertiary medical centers, totaling 728 patients with 109 radiation-induced temporal lobe necrosis (TLN) of nasopharyngeal carcinoma (NPC) and 619 with GBM.
Drug Des Devel Ther
January 2025
Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.
Purpose: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.
Methods: A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China.
Background: Venous thromboembolisms (VTE's) are the second leading cause of death in cancer patients. While previous analyses have demonstrated VTE rates are greater in GBM patients using smaller patient cohorts in high-grade glioma, since the release of the update 5 edition of the World Health Organization (WHO) classification a systematic analysis in a large-scale cohort of patients with IDH-wildtype GBM with clinical outcomes is lacking.
Methods: This study utilizes the online database, TriNetx, to build patient cohorts for outcomes analysis.
Front Public Health
January 2025
School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, Australia.
A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographic and Health Surveys Program (DHSP), with thirteen features ultimately selected as the most predictive features. Classification and generalization experiments are conducted to test the accuracy, F1-score, precision, and area under the curve (AUC) performance of these four algorithms.
View Article and Find Full Text PDFBrain Spine
October 2024
Department of Clinical Medicine, University of Bergen Faculty of Medicine and Dentistry, Bergen, Norway.
Introduction: Extraneural metastases (ENM) from glioblastoma (GBM) remain extremely rare with only a scarce number of cases described in the literature. The lack of cases leads to no consensus on the optimal treatment and follow-up of these patients.
Research Question: Do patient or tumor characteristics describe risk factors for ENM in GBM patients, and is it possible to identify mechanisms of action?
Material And Methods: This study presents a 55-year-old man with diagnosed GBM who was referred to a CT due to reduced general condition and mild back pain which revealed extensive systemic metastases.
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