GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas.
Overview
- Physiology Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
- Cancer Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran
Abstract
Objectives: Glioma tumors, especially glioblastoma (GBM), are among the most heterogeneous brain cancers, and this characteristic is considered the main cause of treatment resistance and disease recurrence. The aim of this study was to investigate the cellular dynamics and transcriptional pathways dependent on the GFAP gene in three clinical conditions, including grade III glioma, primary glioblastoma, and recurrent glioma.
Methods: Single-cell RNA sequencing (scRNA-seq) data from the GSE103224 collection were merged and clustered using the Seurat and Harmony software packages. Then, the Monocle3 tool was used to model cellular developmental pathways, and the Metascape database was used for functional enrichment of genes.
Results: The results showed that malignant cells, microglia, immune cells, and glial progenitors formed distinct clusters in all conditions, and the transition of radial glial cells to cancer cells was observed in all three conditions. Differential gene expression analysis identified six genes, APOE, AQP4, CLU, GFAP, ID3, and ID4 as stable and common genes between all tumor stages, whose expression was directly related to tumor persistence and recurrence. Functional enrichment also identified steroid hormone response pathways, NGF signaling, and the VEGF pathway as common processes in glioma progression.
Conclusion: Overall, these results indicate that GFAP and its associated co-expression network play an important role in tumor persistence and cellular rearrangement in glioma and can be used as molecular markers and novel therapeutic targets for precise and personalized treatment of patients.
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
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Data
Datasets cited
- geo:GSE103224 — at NCBI GEO; found in the text, “Single-Cell RNA-Seq Data Analysis”
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.*
Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026
- Funding: added Shahrekord University; Shahrekord University of Medical Sciences
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 3 authors, 6 keywords, 36 references.
Cite
This paper
Hadizadeh, M., Mousavi, S. Z., & Ghasemi, S. (2026). GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas. Cancer informatics, 25, 11769351261460625. https://
BibTeX
@article{hadizadeh2026gf
author = {Hadizadeh, Morteza and Mousavi, Seyedeh Zahra and Ghasemi, Sorayya},
title = {{GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas}},
journal = {Cancer informatics},
year = {2026},
month = jun,
volume = {25},
pages = {11769351261460625},
publisher = {SAGE Publications},
issn = {1176-9351},
doi = {10.1177/
url = {https://
pmid = {42368220},
pmcid = {PMC13309635}
}
RIS
TY - JOUR
AU - Hadizadeh, Morteza
AU - Mousavi, Seyedeh Zahra
AU - Ghasemi, Sorayya
TI - GFAP-Dependent Transcriptional Dynamics and Cellular Heterogeneity in Primary, Recurrent, and Grade III Gliomas
T2 - Cancer informatics
J2 - Cancer Inform
PY - 2026
DA - 2026/
VL - 25
SP - 11769351261460625
SN - 1176-9351
PB - SAGE Publications
DO - 10.1177/
UR - https://
LA - en
ER -
CSL-JSON
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