Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model.

Overview

1. Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan 430060, China; (L.G.)
2. Department of Neurosurgery, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming 650032, China
3. Department of Oncology, Renmin Hospital of Wuhan University, Wuhan 430060, China

Abstract

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• geo:GSE271959, at NCBI GEO; found in the text, “2.5. Single-Cell Data Download and Preprocessing”

Other data links

• ncbi.nlm.nih.gov/geo, NCBI; found in the text, “2.5. Single-Cell Data Download and Preprocessing”

Data Availability Statement

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 1 funder, 32 references.

Cite

This paper

Gao, L., Zhang, R., Zhu, X., Xu, H., Chen, Q., Peng, M., & Liu, J. (2026). Ligand-Receptor Interaction Combined with Histopathology Improves Glioma Prognostic Model. Biomedicines, 14(5), 1110. https://doi.org/10.3390/biomedicines14051110

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.32604/or.2026.079221

Single-Cell Sequencing Reveals the Heterogeneity of Glioma and Identifies IGFBP2 as A Potential Therapeutic Target.

Journal: Oncology research

In common: genetics / omics, other condition, 4 references

[2] doi:10.3390/cancers18132092

TGFB2 as a Prognostic Biomarker Associated with Myeloid-Enriched, Multi-Checkpoint-Activated Immunosuppression in Diffuse Glioma: A Multi-Cohort Transcriptomic Study.

Journal: Cancers

In common: genetics / omics, other condition, 3 references

[3] doi:10.1038/s41586-026-10612-6 [code]

Acquired genetic and cell-state changes in IDH-mutant glioma progression.

Journal: Nature

In common: other condition, 3 references

[4] doi:10.1016/j.xcrm.2026.102766 [code]

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Journal: Cell reports. Medicine

In common: genetics / omics, other condition, 3 references

[5] doi:10.1111/cbdd.70396

Integrated Multi-Omics and Radiogenomic Analysis Identifies S100B and ITGB5 as Complementary Candidate Biomarkers of Glioblastoma Heterogeneity.

Journal: Chemical biology & drug design

In common: genetics / omics, other condition, 2 references

[6] doi:10.1093/nargab/lqag046 [code]

Beyond chromatin accessibility: bulk ATAC-seq as an integrative assay to portray genomes and epigenomes.

Journal: NAR genomics and bioinformatics

In common: genetics / omics, other condition, 2 references

[7] doi:10.1080/17501911.2026.2691031

Promoter methylation-associated brain-enriched long noncoding RNAs in glioblastoma: a multi-cohort public epigenomic re-analysis.

Journal: Epigenomics

In common: genetics / omics, other condition, 2 references

[8] doi:10.3389/fimmu.2026.1866830

Heterogeneity of immune checkpoint inhibitor-related inflammatory central nervous system adverse event reporting signals in primary and metastatic brain tumors: a pharmacovigilance study with single-cell and spatial transcriptomic contextualization.

Journal: Frontiers in immunology

In common: genetics / omics, other condition, 2 references

[9] doi:10.1016/j.isci.2026.115982 [code]

Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis.

Journal: iScience

In common: genetics / omics, other condition, 2 references

[10] doi:10.3390/ijms27136068 [code]

Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis.

Journal: International journal of molecular sciences

In common: genetics / omics, other condition, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.