Evidence map›Paper›PMID 42464264›Full record

ArticleJournal of neuroinflammation2026

Circulating immune profiling reveals impaired monocyte states and trajectories driving immunosuppression in glioblastoma.

Andrea Scafidi, Tony Kaoma, Claudia Cerella, Eleonora Campus, Kamil Grzyb, Bakhtiyor Nosirov, Frida Lind-Holm Mogensen, Eliane Klein, Raul Da Costa, Alexander Skupin and 10 more

Abstract read
In one paragraph

Article in Journal of neuroinflammation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Andrea Scafidi *Neuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg.
Tony Kaoma *Bioinformatics and AI, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg.
Claudia Cerella *Neuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg.
Eleonora CampusNeuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg.
Kamil GrzybIntegrative Cell Signaling Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, L-4362, Luxembourg.
Bakhtiyor NosirovNORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, L-1210, Luxembourg.
Frida Lind-Holm MogensenNeuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg.
Eliane KleinNORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, L-1210, Luxembourg.
Raul Da CostaNational Cytometry Platform, Translational Medicine Operation Hub, Luxembourg, Institute of Health, Esch-Sur-Alzette, L-4354, Luxembourg.
Alexander SkupinIntegrative Cell Signaling Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, L-4362, Luxembourg.
Frank HertelCentre Hospitalier de Luxembourg, Luxembourg, L-1210, Luxembourg.
Guy BerchemCentre Hospitalier de Luxembourg, Luxembourg, L-1210, Luxembourg.
Michel MittelbronnFaculty of Science, Technology and Medicine, University of Luxembourg, Esch-Sur-Alzette, L-4365, Luxembourg.
Antonio CosmaNational Cytometry Platform, Translational Medicine Operation Hub, Luxembourg, Institute of Health, Esch-Sur-Alzette, L-4354, Luxembourg.
Beatrice MelinDepartment of Diagnostics and Intervention, Oncology, Umeå University, Umeå, 901 87, Sweden.
Simone P NiclouFaculty of Science, Technology and Medicine, University of Luxembourg, Esch-Sur-Alzette, L-4365, Luxembourg.
Petr V NazarovBioinformatics and AI, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg.
Anna GolebiewskaNORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, L-1210, Luxembourg.
Aurélie PoliNeuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg.
Alessandro MichelucciNeuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, Luxembourg, L-1210, Luxembourg. Alessandro.Michelucci@lih.lu.ORCID https://orcid.org/0000-0003-1230-061X

Funding

Fonds De La Recherche Scientifique - FNRS 7.8513.18/7651720FFonds National de la Recherche Luxembourg C21/BM/15739125/DIOMEDESFonds National de la Recherche Luxembourg C24/BM/18858278/GRALLFonds National de la Recherche Luxembourg INTER/DFG/17/11583046Fonds National de la Recherche Luxembourg P16/BM/11192868Fonds National de la Recherche Luxembourg PRIDE/14254520/I2TRONFonds National de la Recherche Luxembourg PRIDE21/16763386/CANBIO2
6 · The paper itself

Abstract

backgroundGlioblastoma (GBM) is an aggressive and lethal brain tumor marked by profound local and systemic immune dysfunction. Despite evidence of peripheral immune impairment, the clinical relevance of these alterations for diagnostic or therapeutic purposes remains poorly defined.

methodsWe performed multimodal single-cell profiling of peripheral blood mononuclear cells from a single-center cohort of treatment-naïve GBM patients and healthy donors, integrating mass and flow cytometry with single-cell RNA-sequencing. Unsupervised clustering, pseudo-temporal trajectory analyses and cell-cell communication inference were applied to map immune states and their interactions.

resultsGBM blood profiles were characterized by heterogeneous changes in classical monocytes, encompassing expanded, reduced and unchanged subsets with distinct functional states, including antigen-presenting, interferon and metabolic programs. Additional myeloid adaptations included myeloid-derived suppressor cell (MDSC) expansion and loss of non-classical monocytes. Trajectory analyses identified a differentiation continuum, evolving from antigen-presenting to metabolic monocyte subsets, and positioning MDSCs as an intermediate state. Antigen‑presenting monocytes displayed tumor‑migratory, precursor‑like profiles that corresponded to tumor‑associated macrophages in public GBM datasets. Across subsets, circulating monocytes shared a "GBM-classical monocytic signature" characterized by low MHC class II expression, altered cell-cell communication and upregulation of anti-inflammatory mediators, including IL1R2 and CD163. Notably, complementary myeloid expression signatures were identified across patients, indicating distinct systemic immune phenotypes. In parallel, lymphocyte alterations included decreased proportions of CD4

conclusionsThese findings delineate systemic immune reprogramming in primary GBM, characterized by coordinated myeloid and lymphocyte alterations. The identification of circulating monocyte states with transcriptional continuity to the tumour microenvironment, alongside distinct patient-level systemic myeloid signatures, provides a framework for exploring peripheral blood as a source of immune biomarkers in GBM.

Indexed as

Brain NeoplasmsGlioblastomaMonocytesCohort StudiesFemaleHumansMaleCyTOFGlioblastomaLymphocytesMonocytesMyeloid cell trajectoriesSingle-cell RNA sequencingTumour-associated macrophages

Identifiers

PMID42464264
PMCPMC13555986

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