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Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
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Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
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Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma

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Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma
Journal Article

Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma

2024
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Overview
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 (m MGMTp ) 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 m MGMTp 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 m MGMTp . The evaluation of GBM cell histomorphological heterogeneity is more effective for prognosis prediction in combination with m MGMTp analysis, indicating that histomorphological analysis is a practical and useful prognostication tool in an integrative diagnosis of GBMs.