Lange Falko, Gade Richard, Einsle Anne, Porath Katrin, Reichart Gesine, Maletzki Claudia, Schneider Björn, Henker Christian, Dubinski Daniel, Linnebacher Michael, Köhling Rüdiger, Freiman Thomas M, Kirschstein Timo
Oscar-Langendorff-Institute of Physiology, University Medical Center Rostock, Rostock, Germany.
Center for Transdisciplinary Neurosciences Rostock, University of Rostock, Rostock, Germany.
Front Oncol. 2024 May 21;14:1335401. doi: 10.3389/fonc.2024.1335401. eCollection 2024.
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.
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.
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 , , kainate receptors and , metabotropic receptor , transaminase and the glutamine synthetase (encoded by ). 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.
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.
高级别胶质瘤与颅外起源脑肿瘤的鉴别对于后续治疗方案的决策至关重要。在高级别胶质瘤中，肿瘤肿块的手术切除是当前标准治疗方案的基本组成部分，而在脑转移瘤中，则必须考虑原发肿瘤的负荷。然而，在没有癌症病史的情况下，通过影像学进行鉴别仍具有挑战性。因此，活检很常见，这可能有助于确定肿瘤的起源。一种支持鉴别诊断的额外工具可能会有很大帮助。为此，我们旨在基于一小份组织样本的表达分析来确定一个生物标志物组，以支持手术切除标本的病理分析。鉴于已确定异常的谷氨酸信号传导可驱动胶质母细胞瘤进展，我们聚焦于谷氨酸受体和谷氨酸稳态的关键因子。
基于55例脑肿瘤手术切除样本，通过逆转录聚合酶链反应（RT-PCR）分析离子型和代谢型谷氨酸受体以及谷氨酸稳态关键因子的表达。随后，进行受试者工作特征（ROC）分析，以确定其表达水平可能与胶质母细胞瘤或脑转移瘤相关的基因。
在总共分析的29个谷氨酸能基因中，9个基因在高级别胶质瘤和脑转移瘤之间呈现出显著不同的表达水平。其中，7个被确定为潜在的生物标志物候选基因，包括编码α-氨基-3-羟基-5-甲基-4-异恶唑丙酸（AMPA）受体GluA1、GluA2、海人酸受体GluK2和GluK5、代谢型受体mGluR2、转氨酶GOT1以及谷氨酰胺合成酶（由GLUL编码）的基因。总体而言，该生物标志物组在预测肿瘤类型方面的准确率达到了88%（95%置信区间：87.1，90.8）。然而，基因表达数据无法区分有癫痫发作的患者和无癫痫发作的患者。
我们已经确定了一组7个基因，其表达可作为在分子水平上鉴别胶质母细胞瘤和脑转移瘤的生物标志物组。经过进一步验证后，我们的生物标志物特征在诊断后后续治疗方案的决策中可能会有很大用途。
