ArticleScientific reports2024
Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Crosslink Between Neuroinflammation and Oxidative/Nitrosative Status in the Glioblastoma Tumor Microenvironment: Prognostic and Theranostic Impact.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Article
- Modeling glioblastoma relapse in vitro: a critical journey from 2D models to organ-on-chip alternatives.NPJ precision oncology · 2026Review
- Segmentation Methodologies for the Construction of Hyperspectral Cell Nuclei Databases in Histopathology.Bioengineering (Basel, Switzerland) · 2026Article
- Neutrophil Percentage-to-Albumin Ratio as a Novel Prognostic Biomarker in Adult Diffuse Gliomas: Retrospective Study Integrating 3 Machine Learning Models and Cox Regression.JMIR medical informatics · 2026Article
- Heterogeneity phenotypes in recurrent glioblastoma: a multimodal MRI-based spatial mapping framework for precision treatment.BMC medical imaging · 2025Article
- Neurooncology: 2025 update.Free neuropathology · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastomas (GBMs) are the most aggressive types of central nervous system tumors. Although certain genomic alterations have been identified as prognostic biomarkers of GBMs, the histomorphological features that predict their prognosis remain elusive. In this study, following an integrative diagnosis of 227 GBMs based on the 2021 World Health Organization classification system, the cases were histologically fractionated by cellular variations and abundance to evaluate the relationship between cellular heterogeneity and prognosis in combination with O-6-methylguanine-DNA methyltransferase gene promoter methylation (mMGMTp) status. GBMs comprised four major cell types: astrocytic, pleomorphic, gemistocytic, and rhabdoid cells. t-distributed stochastic neighbor embedding analysis using the histological abundance of heterogeneous cell types identified two distinct groups with significantly different prognoses. In individual cell component analysis, the abundance of gemistocytes showed a significantly favorable prognosis but confounding to mMGMTp status. Conversely, the abundance of epithelioid cells was correlated with the unfavorable prognosis. Linear model analysis showed the favorable prognostic utility of quantifying gemistocytic and epithelioid cells, independent of mMGMTp. The evaluation of GBM cell histomorphological heterogeneity is more effective for prognosis prediction in combination with mMGMTp analysis, indicating that histomorphological analysis is a practical and useful prognostication tool in an integrative diagnosis of GBMs.
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