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Updated: Jun 24, 2025
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
Published on: January 9, 2019
A glutamatergic biomarker panel enables differentiating Grade 4 gliomas/astrocytomas from brain metastases
Falko Lange1,2, Richard Gade1, Anne Einsle1
1Oscar-Langendorff-Institute of Physiology, University Medical Center Rostock, Rostock, Germany.
Background:
The differentiation of high-grade glioma and brain tumors of an extracranial origin is eminent for the decision on subsequent treatment regimens. While in high-grade glioma, a surgical resection of the tumor mass is a fundamental part of current standard regimens, in brain metastasis, the burden of the primary tumor must be considered. However, without a cancer history, the differentiation remains challenging in the imaging. Hence, biopsies are common that may help to identify the tumor origin. An additional tool to support the differentiation may be of great help. For this purpose, we aimed to identify a biomarker panel based on the expression analysis of a small sample of tissue to support the pathological analysis of surgery resection specimens. Given that an aberrant glutamate signaling was identified to drive glioblastoma progression, we focused on glutamate receptors and key players of glutamate homeostasis.
Methods:
Based on surgically resected samples from 55 brain tumors, the expression of ionotropic and metabotropic glutamate receptors and key players of glutamate homeostasis were analyzed by RT-PCR. Subsequently, a receiver operating characteristic (ROC) analysis was performed to identify genes whose expression levels may be associated with either glioblastoma or brain metastasis.
Results:
Out of a total of 29 glutamatergic genes analyzed, nine genes presented a significantly different expression level between high-grade gliomas and brain metastases. Of those, seven were identified as potential biomarker candidates including genes encoding for AMPA receptors GRIA1, GRIA2, kainate receptors GRIK1 and GRIK4, metabotropic receptor GRM3, transaminase BCAT1 and the glutamine synthetase (encoded by GLUL). Overall, the biomarker panel achieved an accuracy of 88% (95% CI: 87.1, 90.8) in predicting the tumor entity. Gene expression data, however, could not discriminate between patients with seizures from those without.
Conclusion:
We have identified a panel of seven genes whose expression may serve as a biomarker panel to discriminate glioblastomas and brain metastases at the molecular level. After further validation, our biomarker signatures could be of great use in the decision making on subsequent treatment regimens after diagnosis.
