ArticleFrontiers in molecular biosciences2025
Biomarkers in glioblastoma and degenerative CNS diseases: defining new advances in clinical usefulness and therapeutic molecular target.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- TREM2 as a central hub of neuroimmune-metabolic crosstalk in central nervous system disorders: from microglial biology to therapeutic targeting.Frontiers in immunology · 2026Review
- Machine learning-integrated network toxicology uncovers glioma targets of DEHP.Frontiers in toxicology · 2026Article
- Material-based neuroimaging and biomarker detection for central nervous system disorder.Materials today. Bio · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Discovering biomarkers is central to the research and treatment of degenerative central nervous system (CNS) diseases, playing a crucial role in early diagnosis, disease monitoring, and the development of new treatments, particularly for challenging conditions like degenerative CNS diseases and glioblastoma (GBM). Methods: This study analyzed gene expression data from a public database, employing differential expression analyses and Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with degenerative CNS diseases and GBM. Machine learning methods, including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine - Recursive Feature Elimination (SVM-RFE), were used for case-control differentiation, complemented by functional enrichment analysis and external validation of key genes. Results: Ninety-five commonly altered genes related to degenerative CNS diseases and GBM were identified, with Conclusion: The study's integration of WGCNA and machine learning uncovered
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Registered trials
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