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Glioblastoma (GBM), a highly malignant tumour of the central nervous system, presents with a dire prognosis and low survival rates. The heterogeneous and recurrent nature of GBM renders current treatments relatively ineffective. In our study, we utilized an integrative systems biology approach to uncover the molecular mechanisms driving GBM progression and identify viable therapeutic drug targets for developing more effective GBM treatment strategies. Our integrative analysis revealed an elevated expression of in GBM tumours, designating it as an unfavourable prognostic gene in GBM, as supported by data from two independent GBM cohorts. Further, we pinpointed WZ-4002 as a potential drug candidate to modulate through computational drug repositioning. WZ-4002 directly targeted () and , affecting their dimerization and influencing the activity of adjacent genes, including . We validated our findings by treating U-138 MG cells with WZ-4002, observing a decrease in CHST2 protein levels and a reduction in cell viability. In summary, our research suggests that the WZ-4002 drug candidate may effectively modulate and adjacent genes, offering a promising avenue for developing efficient treatment strategies for GBM patients.
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http://dx.doi.org/10.3390/ijms25147868 | DOI Listing |
Bioinformatics
September 2025
The Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
Motivation: Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Drugs with human genetic evidence are more likely to advance successfully through clinical trials towards FDA approval. Single gene-based drug repositioning methods have been implemented, but approaches leveraging a broad spectrum of molecular signatures remain underexplored.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFNAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
View Article and Find Full Text PDFExpert Opin Drug Discov
September 2025
Biotechnological Center, CMCB and scads.ai, Dresden, Germany.
Background: Promiscuity of drugs and targets plays an important role in drug-target prediction, ranging from the explanation of side effects to their exploitation in drug repositioning. A specific form of promiscuity concerns drugs, which interfere with protein-protein interactions. With the rising importance of such drugs in drug discovery and with the large-scale availability of structural data, the question arises on the structural basis of this form of promiscuity and the commonalities of the underlying protein-ligand (PLI) and protein-protein interactions (PPI).
View Article and Find Full Text PDFNeuron
September 2025
Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Cancer Neuroscience Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson
The emerging field of cancer neuroscience has revealed profound bidirectional interactions between the nervous system and cancer cells, identifying novel therapeutic vulnerabilities across diverse malignancies. This review examines the unique challenges and strategies for translating these insights into effective therapies. We propose innovative approaches to overcome these barriers through drug repurposing, enhanced biomarker development, and optimized trial designs.
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