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Updated: Dec 29, 2025

Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions
Published on: February 24, 2021
Relationships between recurrence patterns and subventricular zone involvement or CD133 expression in glioblastoma
Tetsu Yamaki1, Ichiyo Shibahra2, Ken-Ichiro Matsuda1
1Department of Neurosurgery, Faculty of Medicine, Yamagata University, 2-2 Iida-Nishi, Yamagata, Yamagata, 990-9585, Japan.
Introduction:
We previously reported that CD133 expression correlated with the recurrence pattern of glioblastoma (GBM). Subventricular zone (SVZ) involvement may also be associated with distant recurrence in GBM. Therefore, we herein investigated whether the combined analysis of SVZ involvement and CD133 expression is useful for predicting the pattern of GBM recurrence.
Materials And Methods:
We retrospectively analyzed 167 cases of GBM. Tumors were divided into four groups based on spatial relationships between contrast-enhanced lesions (CEL) and the SVZ or cortex (Ctx) on MRI. The initial recurrence pattern (local/distant) was obtained from medical records. To identify factors predictive of recurrence, we examined CD133 expression by immunohistochemical, clinical (age, sex, KPS, Ki-67 labeling index, surgery, and MRI characteristics), and genetic (IDH1, MGMT, and BRAF) factors.
Results:
The CD133 expression rate was higher in SVZ-positive tumors than in SVZ-negative tumors (P = 0.046). Distant recurrence was observed in 21% of patients, and no significant difference was noted in recurrence patterns among the four groups. However, strong CD133 expression was associated with a shorter time to distant recurrence in univariate, multivariate, and propensity-matched scoring analyses (P < 0.0001, P = 0.001, and P = 0.0084, respectively). In the combined analysis, distant recurrence was the most frequent (70%) in group III (SVZ-negative, Ctx-positive) GBM and those with high CD133 expression rates (≥ 15%).
Conclusion:
An integrated analysis of CD133 expression and MRI-based tumor classification may be useful for predicting the recurrence pattern of GBM.
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