Glioblastoma multiforme (GBM) is the most widespread and aggressive type of primary brain tumor. The prognosis following diagnosis with GBM is poor, with a median survival time of 14 months. Tumor cell invasion, metastasis and proliferation are the major causes of mortality in patients with GBM. In order to develop effective GBM treatment methods it is necessary to identify novel targets involved in these processes. Recently, there has been increasing interest in investigating the signaling pathways involved in GBM development, and the transforming growth factor-β (TGF-β) signaling pathway is understood to be significant for regulating the behavior of GBM, as well as stimulating its invasion and metastatic development. Particular interest has been given to investigating the modulation of TGF-β-induced epithelial-to-mesenchymal transition (EMT); during this process, epithelial cells transdifferentiate into mobile cells with a mesenchymal phenotype. The induction of EMT increases the invasiveness of various types of carcinoma; however, the role of TGF-β in this process remains to be elucidated, particularly in the case of GBM. The current study presents a comparative proteome mapping of the U87 human glioblastoma cell line, with and without TGF-β1 treatment. Proteome analysis identified numerous proteins involved in the molecular mechanisms of GBM oncogenesis and TGF-β1 signaling in glioblastoma. The results of the present study facilitated the identification of novel potential markers of metastasis and candidates for targeted glioblastoma therapy, which may potentially be validated and used in clinical medicine to develop improved approaches for GBM diagnosis and treatment.
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http://dx.doi.org/10.3892/ol.2016.4756 | DOI Listing |
Cell Death Dis
January 2025
Division of Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA, USA.
The association of necrosis in tumors with poor prognosis implies a potential tumor-promoting role. However, the mechanisms underlying cell death in this context and how damaged tissue contributes to tumor progression remain unclear. Here, we identified p38 mitogen-activated protein kinases (p38 MAPK, a.
View Article and Find Full Text PDFActa Biomater
January 2025
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America; Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America. Electronic address:
Pro-tumoral M2 tumor-associated macrophages (TAMs) play a critical role in the tumor immune microenvironment (TIME), making them an important therapeutic target for cancer treatment. Approaches for imaging and monitoring M2 TAMs, as well as tracking their changes in response to tumor progression or treatment are highly sought-after but remain underdeveloped. Here, we report an M2-targeted magnetic resonance imaging (MRI) probe based on sub-5 nm ultrafine iron oxide nanoparticles (uIONP), featuring an anti-biofouling coating to prevent non-specific macrophage uptake and an M2-specific peptide ligand (M2pep) for active targeting of M2 TAMs.
View Article and Find Full Text PDFSpine J
January 2025
Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha 410008, China. Electronic address:
Background: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. The application of AI-driven large language models (LLMs) shows great potential for improving the accuracy of this differential diagnosis.
Purpose: To evaluate the performance of various machine learning models and ChatGPT-4 in distinguishing between STB and ST.
Discov Oncol
January 2025
Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.
Methods: This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM.
Glioblastoma Multiforme (GBM) is the most prevalent and highly malignant form of adult brain cancer characterized by poor overall survival rates. Effective therapeutic modalities remain limited, necessitating the search for novel treatments. Neurodevelopmental pathways have been implicated in glioma formation, with key neurodevelopmental regulators being re- expressed or co-opted during glioma tumorigenesis.
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