Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Sevilla, Spain.
Department of Psychiatry, University of Cambridge, Cambridge, UK.
Brain. 2023 Mar 1;146(3):1200-1211. doi: 10.1093/brain/awac378.
Unravelling the complex events driving grade-specific spatial distribution of brain tumour occurrence requires rich datasets from both healthy individuals and patients. Here, we combined open-access data from The Cancer Genome Atlas, the UK Biobank and the Allen Brain Human Atlas to disentangle how the different spatial occurrences of glioblastoma multiforme and low-grade gliomas are linked to brain network features and the normative transcriptional profiles of brain regions. From MRI of brain tumour patients, we first constructed a grade-related frequency map of the regional occurrence of low-grade gliomas and the more aggressive glioblastoma multiforme. Using associated mRNA transcription data, we derived a set of differential gene expressions from glioblastoma multiforme and low-grade gliomas tissues of the same patients. By combining the resulting values with normative gene expressions from post-mortem brain tissue, we constructed a grade-related expression map indicating which brain regions express genes dysregulated in aggressive gliomas. Additionally, we derived an expression map of genes previously associated with tumour subtypes in a genome-wide association study (tumour-related genes). There were significant associations between grade-related frequency, grade-related expression and tumour-related expression maps, as well as functional brain network features (specifically, nodal strength and participation coefficient) that are implicated in neurological and psychiatric disorders. These findings identify brain network dynamics and transcriptomic signatures as key factors in regional vulnerability for glioblastoma multiforme and low-grade glioma occurrence, placing primary brain tumours within a well established framework of neurological and psychiatric cortical alterations.
揭示导致脑肿瘤特定级别空间分布的复杂事件需要来自健康个体和患者的丰富数据集。在这里，我们结合了来自癌症基因组图谱、英国生物银行和艾伦脑人类图谱的公开数据，以厘清多形性胶质母细胞瘤和低级别胶质瘤的不同空间发生与大脑网络特征和大脑区域的规范转录谱之间的联系。从脑肿瘤患者的 MRI 中，我们首先构建了低级别胶质瘤和侵袭性更强的多形性胶质母细胞瘤在区域发生的分级相关频率图。使用相关的 mRNA 转录数据，我们从同一患者的多形性胶质母细胞瘤和低级别胶质瘤组织中得出了一组差异基因表达。通过将所得值与来自尸检脑组织的规范基因表达相结合，我们构建了一个分级相关的表达图谱，指示哪些大脑区域表达在侵袭性胶质母细胞瘤中失调的基因。此外，我们还从全基因组关联研究（肿瘤相关基因）中得出了与肿瘤亚型相关的基因表达图谱。分级相关频率、分级相关表达和肿瘤相关表达图谱之间以及与神经和精神障碍相关的功能大脑网络特征（特别是节点强度和参与系数）之间存在显著关联。这些发现将大脑网络动力学和转录组特征确定为多形性胶质母细胞瘤和低级别胶质瘤发生的区域易感性的关键因素，将原发性脑肿瘤置于神经和精神皮质改变的既定框架内。
