Visual summary — paper strengths & weaknesses (data-driven)

Evidence map — what the paper reports (short bullets, each claim cited)

1. Liquid biopsy analytes covered: cfDNA/ctDNA, cfRNA (miRNA/lncRNA/circRNA), extracellular vesicles (exosomes, microvesicles), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs) — with detection methods discussed: NGS, ddPCR, RT-qPCR, mass spectrometry, ultracentrifugation, microfluidic capture Review biomarker/method list.
2. CSF generally shows higher tumor-derived nucleic acid yield than plasma for CNS tumors; plasma sensitivity is improving with tumor-guided and personalized sequencing, but blood–brain barrier (BBB) limits ctDNA access to blood Wang et al. 2015 CSF ctDNA Bagley et al. 2020 plasma cfDNA pilot.
3. MGMT promoter methylation and TERT promoter/TERT mutations are detectable via ctDNA/EV-DNA in some studies and could inform prognosis / temozolomide response, but detection sensitivity is variable and depends on sample source and assay Rosas-Alonso et al. 2024 EV MGMT methylation Fontanilles et al. 2020 ctDNA TERT.
4. Extracellular vesicles (EVs) and exosomal miRNAs (e.g., miR-19b-3p, miR-183-5p, miR-323a-3p, and miR-21) are promising because cargo is protected and can cross the BBB, but EV isolation/measurement lacks standardization and many studies are small/heterogeneous Mahmoudi et al. 2015 EV review Di et al. 2025 EV miR-21 mouse study.
5. CTCs and CTC-derived genomic data can be obtained but are rare in GBM blood due to BBB; CSF can be richer for tumor cells and nucleic acids: paired blood–CSF studies show higher CTC detection/counts in CSF Dual-compartment CTC detection 2025 TEP thromboSeq 2018.

Critical appraisal — what the review does well and where it can mislead

Concrete, evidence-based takeaways for researchers & clinicians

• Use CSF when safe and clinically justified for maximum sensitivity for CNS-derived ctDNA/EV DNA; however, CSF sampling is invasive and not ideal for repeated surveillance — plasma-based personalized panels and tumor-guided sequencing improve detection and permit longitudinal monitoring in some cohorts Bagley et al. 2020 plasma cfDNA Mouliere et al. 2021 personalized cfDNA sequencing.
• Extracellular vesicle cargo offers functional information (RNA/protein) and is protected from nucleases, making EV-based assays attractive, but standardize isolation and validate cross-center reproducibility before clinical translation Del Bene et al. 2022 EV review.
• TEP RNA signatures (thromboSeq) show strong multi-cancer performance; TEPs are abundant, RNA-rich and stable, making them a high-potential blood biosource for multi-omic integration in GBM studies — but independent GBM-specific validation is required Best et al. 2018 TEPs.
• Integration & ML: multi-analyte, multi-compartment assays (cfDNA methylation + EV cargo + TEP RNA + imaging features) combined with robust machine learning have the best chance to overcome single-analyte limitations — but require large harmonized datasets, external validation, and attention to overfitting/biological confounders (e.g., inflammation).

What would change my assessment (falsification tests)

1. Large prospective multi-center studies (n>200) showing plasma liquid biopsy (standardized protocol) detects GBM recurrence and differentiates pseudoprogression with high sensitivity & specificity (e.g., >90%) would strongly increase confidence in clinical utility.
2. Conversely, well-powered trials showing lack of reproducibility (high inter-lab variance) or clinical irrelevance (no added diagnostic/prognostic value beyond MRI/tissue) would falsify routine use.

Practical, prioritized recommendations for next studies

1. Design a pre-registered, prospective multi-center study with standardized pre-analytic protocols (blood tube type, time-to-spin, storage), harmonized assays (ddPCR + targeted NGS panels with limit-of-detection reporting), and pre-specified primary endpoints (detection of recurrence; discrimination of pseudoprogression) with sample size powering.
2. Adopt multi-analyte panels: combine tumor-informed ctDNA panels (when tissue available), EV-DNA/RNA cargo, and TEP RNA, integrate with MRI radiomics and clinical features using nested cross-validation and external test sets to avoid overfitting.
3. Include CSF sampling where clinically feasible to provide a sensitivity anchor; collect matched plasma/urine/saliva to evaluate less-invasive compartment performance and to train transfer models.

Short interactive resources

Selected, precise citations used in this critique (key supporting primary studies)

• Tataranu 2025 Review
• Wang et al. 2015 CSF ctDNA
• Mouliere et al. 2021 personalized cfDNA
• Best et al. 2018 TEPs
• Rosas-Alonso et al. 2024 EV MGMT
• Bagley et al. 2020

Conclusions — short, evidence-weighted

The review provides a useful, current narrative synthesis of circulating biomarker modalities for glioblastoma and reasonably frames both promise and limits. It correctly emphasises that no circulating GBM biomarker is yet validated for routine clinical use, and charts the correct next steps (standardization, large prospective cohorts, multi-analyte integration). The main methodological limitation is the narrative format (no systematic search or quantitative synthesis) and a somewhat light treatment of assay analytical performance metrics and regulatory translation pathways. Confidence in the review’s high-level conclusions is moderate given correct citations and alignment with the wider literature; confidence drops where claims imply near-term clinical readiness without large prospective validation.

Interactive next-step

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