Evidence map›Paper›PMID 40210624›Full record

ArticleNature communications2025

Plasma-to-tumour tissue integrated proteomics using nano-omics for biomarker discovery in glioblastoma.

Xinming Liu, Hanan Abmanhal-Masarweh, Olivia Iwanowytsch, Emmanuel Okwelogu, Kiana Arashvand, Konstantina Karabatsou, Pietro Ivo D'Urso, Federico Roncaroli, Kostas Kostarelos, Thomas Kisby and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xinming LiuNanoOmics Lab, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0001-8809-3907
Hanan Abmanhal-MasarwehNanoOmics Lab, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Olivia IwanowytschNanoOmics Lab, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Emmanuel OkweloguNanoOmics Lab, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Kiana ArashvandNanoTherapeutics Lab, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Konstantina KarabatsouDepartment of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester, UK.
Pietro Ivo D'UrsoDepartment of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester, UK.
Federico RoncaroliDivision of Neuroscience, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0003-3650-5572
Kostas KostarelosCentre for Nanotechnology in Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-2224-6672
Thomas KisbyNanoTherapeutics Lab, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. thomas.kisby@manchester.ac.uk.ORCID http://orcid.org/0000-0002-8432-7910
Marilena HadjidemetriouNanoOmics Lab, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. marilena.hadjidemetriou@manchester.ac.uk.ORCID http://orcid.org/0000-0003-4720-2112

Funding

Cancer Research UK (CRUK) ACED; EICEDAAP\100013Cancer Research UK (CRUK) EDDPMA-Nov21\100027RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/P00119X/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/S030719/1
6 · The paper itself

Abstract

Glioblastoma (GB) is the most lethal brain cancer, with patient survival rates remaining largely unchanged over the past two decades. Here, we introduce the Nano-omics integrative workflow that links systemic (plasma) and localised (tumour tissue) protein changes associated with GB progression. Mass spectrometry analysis of the nanoparticle biomolecule corona in GL261-bearing mice at different stages of GB revealed plasma protein alterations, even at low tumour burden, with over 30% overlap between GB-specific plasma and tumour tissue proteomes. Analysis of matched plasma and surgically resected tumour samples from high-grade glioma patients demonstrates the clinical applicability of the Nano-omics pipeline. Cross-species correlation identified 48 potential GB biomarker candidates involved in actin cytoskeleton organisation, focal adhesion, platelet activation, leukocyte migration, amino acid biosynthesis, carbon metabolism, and phagosome pathways. The Nano-omics approach holds promise for the discovery of early detection and disease monitoring biomarkers of central nervous system conditions, paving the way for subsequent clinical validation.

Indexed as

Biomarkers, TumorBrain NeoplasmsGlioblastomaProteomicsAnimalsCell Line, TumorFemaleHumansMaleMass SpectrometryMiceMice, Inbred C57BLNanoparticlesProteomeBiomarkers, TumorProteome

Identifiers

PMID40210624
PMCPMC11986092

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.