Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 58 min ago
Peer-reviewedCC BY 4.0Translational Oncology

A multivariate plasma biomarker model for glioblastoma exclusion: a retrospective case-control study

  • Jonathan Decarpentrie1
  • Clara David2
  • Nathalie Donis2
  • Julien Favresse3
  • Julien Cabo4
  • Fabienne George5
  • Matteo Riva6
  • Lionel D’Hondt7
  • Clotilde Brisbois2
  • Jonathan Douxfils
7 affiliations
  1. 1Clinical Pharmacology and Toxicology Research Unit, Namur Research Institute for Life Sciences, University of Namur, Namur, 5000, Belgium
  2. 2QUALIblood s.a., QUALIresearch, Liège, 4000, Belgium
  3. 3Clinical Pharmacology and Toxicology Research Unit, Namur Research Institute for Life Sciences, University of Namur, Namur, 5000, Belgium; Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, 5000, Belgium
  4. 4Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, 5000, Belgium
  5. 5Biobank, CHU UCL Namur Site Godinne, Université Catholique de Louvain, Yvoir, 5530, Belgium
  6. 6Department of Neurosurgery, CHU UCL Namur Site Godinne, Université Catholique de Louvain, Yvoir, 5530, Belgium
  7. 7Department of Oncology, CHU UCL Namur Site Godinne, Université Catholique de Louvain, Yvoir, 5530, Belgium
Read full textPDFDOICite

Abstract

Background: Glioblastoma (GBM) remains the most aggressive primary brain tumour, with limited diagnostic tools able to capture its molecular heterogeneity in real time. Current clinical practice relies on MRI and invasive biopsies, which are poorly suited for early or dynamic disease monitoring. Sensitive and accessible blood-based biomarkers could offer a minimally invasive alternative to aid GBM diagnosis and exclusion. Methods: In this retrospective case-control study, plasma concentrations of seven neurological and inflammatory proteins (GFAP, UCH-L1, NfL, Tau, NSE, MPO, and PMN elastase) were quantified in 72 GBM patients and 102 healthy individuals (NAB-X biobank, n = 20; Clinique Saint-Luc Bouge, n = 82, age-matched) using ultrasensitive immunoassay platforms, complemented by conventional clinical laboratory assays and standard ELISA methods. Diagnostic accuracy was evaluated through ROC analysis and logistic regression modelling, with internal validation performed by 10-fold cross-validation and bootstrap resampling. Results: GFAP and NfL showed the highest individual discriminative performances (AUC = 0.995 and 0.957, respectively). A multivariate model integrating GFAP, NfL, Tau, NSE, MPO, and PMN elastase showed excellent discrimination between GBM and healthy controls (AUC = 0.999; sensitivity = 98.6%; specificity = 100%). The model’s negative predictive value remained above 0.999 in simulated population settings, supporting its use as a potential high-sensitivity exclusion tool. Conclusions: This study reports the possibility of using a plasma-based multivariate biomarker framework to discriminate GBM from healthy individuals with high sensitivity, providing proof-of-concept evidence supporting the future development of a plasma-based exclusion tool. This needs validation in clinically representative symptomatic populations. By integrating astrocytic, neuronal, and inflammatory markers into a single logistic model, this approach bridges analytical precision with clinical applicability, paving the way for a minimally invasive blood test to support diagnostic rule-out in neuro-oncology.

Related

  1. Multivariate analysis of poverty and social exclusion across EU countries in the context of Sustainable Development Goal 1
    Silvia Megyesiova, Cecilia Olexova
    PLOS OnePeer-reviewedCC BY 4.0Full textStatisticsEpidemiology & public healthPLOS
  2. Nuclear Eccentricity as a Potential Biomarker for Tumor Biology and Survival in Glioblastoma.
    Luna Han, Teresia John, Mohid Hassan +1
    Current oncology (Toronto, Ont.)Peer-reviewedCC BYFull textMedical imaging & radiologyEurope PMC
  3. TMBIM4 acts as a novel prognostic biomarker for glioblastoma and is associated with temozolomide chemoresistance.
    Jianeng Pu, Tianhua Li, Shuai Zhou +1
    Translational cancer researchPeer-reviewedCC BY-NC-NDFull textOncologyEurope PMC
  4. Circulating miRNAs and Multivariate Biomarker Signatures in Ovarian Cancer: A Systematic Review
    Karen Ayache, Ana Vieira, Sofia Fragoso +10
    International Journal of Molecular SciencesPeer-reviewedCC BY 4.0Full textOncologyCrossrefEurope PMC
  5. Relative CT Number as a Quantitative Imaging Biomarker for Differentiating Sinonasal Polyp, Inverted Papilloma, and Squamous Cell Carcinoma: A Preliminary Retrospective Study
    Tuğba Öztürk Taşkıran, Servet Akyüz, Göksel Tuzcu
    TomographyPeer-reviewedCC BYFull textMedical imaging & radiologyEurope PMC

Cite

BibTeXRIS
BibTeX
@article{decarpentrie2026multivariate,
  title = {{A multivariate plasma biomarker model for glioblastoma exclusion: a retrospective case-control study}},
  author = {Jonathan Decarpentrie and Clara David and Nathalie Donis and Julien Favresse and Julien Cabo and Fabienne George and Matteo Riva and Lionel D’Hondt and Clotilde Brisbois and Jonathan Douxfils},
  journal = {Translational Oncology},
  year = {2026},
  month = oct,
  doi = {10.1016/j.tranon.2026.103079},
  url = {https://doi.org/10.1016/j.tranon.2026.103079},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A multivariate plasma biomarker model for glioblastoma exclusion: a retrospective case-control study
AU  - Jonathan Decarpentrie
AU  - Clara David
AU  - Nathalie Donis
AU  - Julien Favresse
AU  - Julien Cabo
AU  - Fabienne George
AU  - Matteo Riva
AU  - Lionel D’Hondt
AU  - Clotilde Brisbois
AU  - Jonathan Douxfils
PY  - 2026
DA  - 2026/10/23
JO  - Translational Oncology
DO  - 10.1016/j.tranon.2026.103079
UR  - https://doi.org/10.1016/j.tranon.2026.103079
AB  - Background: Glioblastoma (GBM) remains the most aggressive primary brain tumour, with limited diagnostic tools able to capture its molecular heterogeneity in real time. Current clinical practice relies on MRI and invasive biopsies, which are poorly suited for early or dynamic disease monitoring. Sensitive and accessible blood-based biomarkers could offer a minimally invasive alternative to aid GBM diagnosis and exclusion. Methods: In this retrospective case-control study, plasma concentrations of seven neurological and inflammatory proteins (GFAP, UCH-L1, NfL, Tau, NSE, MPO, and PMN elastase) were quantified in 72 GBM patients and 102 healthy individuals (NAB-X biobank, n = 20; Clinique Saint-Luc Bouge, n = 82, age-matched) using ultrasensitive immunoassay platforms, complemented by conventional clinical laboratory assays and standard ELISA methods. Diagnostic accuracy was evaluated through ROC analysis and logistic regression modelling, with internal validation performed by 10-fold cross-validation and bootstrap resampling. Results: GFAP and NfL showed the highest individual discriminative performances (AUC = 0.995 and 0.957, respectively). A multivariate model integrating GFAP, NfL, Tau, NSE, MPO, and PMN elastase showed excellent discrimination between GBM and healthy controls (AUC = 0.999; sensitivity = 98.6%; specificity = 100%). The model’s negative predictive value remained above 0.999 in simulated population settings, supporting its use as a potential high-sensitivity exclusion tool. Conclusions: This study reports the possibility of using a plasma-based multivariate biomarker framework to discriminate GBM from healthy individuals with high sensitivity, providing proof-of-concept evidence supporting the future development of a plasma-based exclusion tool. This needs validation in clinically representative symptomatic populations. By integrating astrocytic, neuronal, and inflammatory markers into a single logistic model, this approach bridges analytical precision with clinical applicability, paving the way for a minimally invasive blood test to support diagnostic rule-out in neuro-oncology.
SN  - 1936-5233
SN  - 1944-7124
ER  -