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Liquid biopsy in glioblastoma: emerging technologies and translational opportunities.

Sep 2026 · Trends in Biotechnology · 0 citations · 84 references
Medicine

TL;DR

A multimodal decision matrix and an artificial intelligence (AI)-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics are introduced to enhance minimal residual disease detection and guide precision neuro-oncology.

Abstract

Glioblastoma remains highly aggressive, with limited treatment response and poor prognosis. Tissue biopsy (TB) is invasive and fail to capture tumor heterogeneity or temporal dynamics. Liquid biopsy (LB) provides a noninvasive alternative for real-time monitoring via circulating biomarkers, including cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in plasma, cerebrospinal fluid (CSF), urine, and saliva. This review advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability. We introduce a multimodal decision matrix and an artificial intelligence (AI)-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection and guide precision neuro-oncology.

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