磁共振灌注加权成像与弥散加权成像在脑胶质瘤分级诊断中的应用

目的:探讨磁共振灌注加权成像(perfusion weighted imaging,PWI)与弥散加权成像(diffusion weighted imaging,DWI)在脑胶质瘤分级诊断中的应用价值。方法:选取2012年1月-2017年6月在我院就诊并经病理证实为脑胶质瘤患者100例,其中高、低级别胶质瘤患者各44、56例。对所有患者行PWI、DWI检查,比较肿瘤不同区域表观扩散系数(apparent diffusion coefficient,ADC)、局部脑血流量(regional cerebral blood flow,rCBF),不同级别肿瘤实质区、瘤周水肿区rADC、rrCBF,根据ROC曲线分析rADC、rrCBF对不同级别胶质瘤的诊断阈值、敏感性、特异性。结果:与对侧相应正常脑实质比较,瘤周水肿区及肿瘤实质区ADC、rCBF均显著升高(P0.05);与瘤周水肿区比较,肿瘤实质区ADC、rCBF均显著升高(P0.05)。高级别肿瘤实质区rADC显著低于低级别肿瘤实质区(P0.05),rrCBF显著高于肿瘤实质区(P0.05)。高级别瘤周水肿区与低级别瘤周水肿区rADC间无显著差异(P0.05),高级别瘤周水肿区rrCBF显著高于低级别瘤周水肿区(P0.05)。在对高、低级别脑胶质瘤的分级中,rADC、rrCBF的曲线下面积(under the receiver operating characteristic curve,AUC)分别为0.957、0.978,均0.9。rADC诊断不同分级胶质瘤的敏感度是90.12%,特异度是95.26%,诊断阈值是13.12;rrCBF诊断不同分级胶质瘤的敏感度是92.31%,特异度是98.57%,诊断阈值是2.62。rADC与rrCBF诊断不同分级胶质瘤敏感度、特异度间无显著差异(P0.05)。结论:PWI、DWI能够为脑胶质瘤的分级诊断提供参考依据。     

研究报告: 第五版《世界卫生组织中枢神经系统肿瘤分类指南》下成人胶质瘤诊断及预后

目的 脑胶质瘤是最常见的恶性原发性中枢神经系统肿瘤,近年来分子病理的快速发展对胶质瘤诊断及分级带来了重要影响,在2021年发布的《世界卫生组织中枢神经系统肿瘤分类指南》（第五版）引入了更多分子指标对肿瘤的诊断和分级进行指导。本研究旨在临床队列中比较最新版指南和上一版指南对肿瘤诊断及预后评估的影响,以期为临床实践活动中新版指南的应用提供数据参考和依据。方法 回顾性纳入了癌症基因组图谱数据库512例胶质瘤样本,分别依据2016版和2021版《世界卫生组织中枢神经系统肿瘤分类指南》进行诊断、通过Kaplan-Meier进行生存曲线绘制和中位总生存期计算和生存差异分析。结果 对512例样本分别完成了上一版指南和最新版指南的诊断及分级。在新版指南下分别有53和72例异柠檬酸脱氢酶（IDH）突变型和IDH野生型的胶质瘤诊断级别升级为了4级,且这些诊断级别升高的胶质瘤的预后更差。结论 最新版指南较上一版指南可对胶质瘤进行更为精准地分类及分级,在有条件的情况下应尽快依据最新版指南开展诊断及分级。     

MEG Network Differences between Low- and High-Grade Glioma Related to Epilepsy and Cognition

Objective

To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups.

Methods

We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients.

Results

LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4–8 Hz), similar to NGL patients. HGG patients’ networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients.

Conclusion

Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients’ networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.     

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks

BackgroundGlioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks.ResultsAs a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers.ConclusionMachine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth.     

Syndecan-1 expression in human glioma is correlated with advanced tumor progression and poor prognosis

Syndecan-1 has been implicated in tumorigenesis and progression of various human malignancies. Recent studies have demonstrated that syndecan-1 may have a different function and biological activity depending on the specific tumor type. Therefore, the aim of this study was to investigate the clinical significance of syndecan-1 in human gliomas. One hundred and sixteen glioma patients (26 World Health Organization (WHO) grade I, 30 WHO grade II, 30 WHO grade III, and 30 WHO grade IV) and 15 normal brain specimens acquired from 15 patients undergoing surgery for epilepsy as control were collected. Immunohistochemistry assay, quantitative real-time PCR and Western blot analysis were carried out to detect the expression of syndecan-1 at gene and protein levels in glioma samples with different WHO grades. Syndecan-1 gene and protein levels were both higher in glioma tissues compared to controls (both P < 0.001). In addition, its expression levels increased with ascending tumor WHO grades according to the results of immunohistochemistry assay, quantitative real-time PCR and Western blot analysis. Moreover, the survival rate of syndecan-1-positive patients was significantly lower than that of syndecan-1-negative patients (P = 0.006). We further confirmed that the increased expression of syndecan-1 was an independent prognostic indicator in glioma by multivariate analysis (P = 0.01). Our data suggest for the first time that the increased expression of syndecan-1 at gene and protein levels is correlated with advanced tumor progression and poor outcome in patients with glioma. Syndecan-1 might serve as a potential prognosis predictor of this dismal tumor.     

A MiRNA Signature for Defining Aggressive Phenotype and Prognosis in Gliomas

Gliomas represent a disparate group of tumours for which there are to date no cure. Thus, there is a recognized need for new diagnostic and therapeutic approaches based on increased understanding of their molecular nature. We performed the comparison of the microRNA (miRNA) profile of 8 WHO grade II gliomas and 24 higher grade tumours (2 WHO grade III and 22 glioblastomas) by using the Affymetrix GeneChip miRNA Array v. 1.0. A relative quantification method (RT-qPCR) with standard curve was used to confirm the 22 miRNA signature resulted by array analysis. The prognostic performances of the confirmed miRNAs were estimated on the Tumor Cancer Genome Atlas (TCGA) datasets. We identified 22 miRNAs distinguishing grade II gliomas from higher grade tumours. RT-qPCR confirmed the differential expression in the two patients'' groups for 13 out of the 22 miRNAs. The analysis of the Glioblastoma Multiforme (GBM) and Lower Grade Glioma (LGG) datasets from TCGA demonstrated the association with prognosis for 6 of those miRNAs. Moreover, in the GBM dataset miR-21 and miR-210 were predictors of worse prognosis in both univariable and multivariable Cox regression analyses (HR 1.19, p = 0.04, and HR 1.18, p = 0.029 respectively). Our results support a direct contribution of miRNAs to glioma cancerogenesis and suggest that miR-21 and miR-210 may play a role in the aggressive clinical behaviour of glioblastomas.     

Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Background

Magnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.

Methods

An automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.

Results

Cho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/NAA and Cho/Cr in the AQoCE approach show a significant difference (p≤0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and Siemens MRS processing software are also discussed in this paper.

Conclusions

The AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel ¹H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of Siemens.     

A Molecular Predictor Reassesses Classification of Human Grade II/III Gliomas

Diffuse gliomas are incurable brain tumors divided in 3 WHO grades (II; III; IV) based on histological criteria. Grade II/III gliomas are clinically very heterogeneous and their prognosis somewhat unpredictable, preventing definition of appropriate treatment. On a cohort of 65 grade II/III glioma patients, a QPCR-based approach allowed selection of a biologically relevant gene list from which a gene signature significantly correlated to overall survival was extracted. This signature clustered the training cohort into two classes of low and high risk of progression and death, and similarly clustered two external independent test cohorts of 104 and 73 grade II/III patients. A 22-gene class predictor of the training clusters optimally distinguished poor from good prognosis patients (median survival of 13–20 months versus over 6 years) in the validation cohorts. This classification was stronger at predicting outcome than the WHO grade II/III classification (P≤2.8E-10 versus 0.018). When compared to other prognosis factors (histological subtype and genetic abnormalities) in a multivariate analysis, the 22-gene predictor remained significantly associated with overall survival. Early prediction of high risk patients (3% of WHO grade II), and low risk patients (29% of WHO grade III) in clinical routine will allow the development of more appropriate follow-up and treatments.     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...     

Krüppel-like factor 8 (KLF8) is expressed in gliomas of different WHO grades and is essential for tumor cell proliferation

Krüppel-like factor 8 (KLF8) has only recently been identified to be involved in tumor cell proliferation and invasion of several different tumor entities like renal cell carcinoma, hepatocellular carcinoma and breast cancer. In the present study, we show for the first time the expression of KLF8 in gliomas of different WHO grades and its functional impact on glioma cell proliferation. In order to get information about KLF8-mRNA regulation qPCR was performed and did not reveal any significant difference in samples (n = 10 each) of non-neoplastic brain (NNB), low-grade gliomas (LGG, WHO°II) and glioblastomas (GBM, WHO°IV). Immunohistochemistry of tissue samples (n = 7 LGG, 11 AA and 12 GBM) did not show any significant difference in the fraction of KLF8-immunopositive cells of all analyzed cells in LGG (87%), AA (80%) or GBM (89%). Tissue samples from cerebral breast cancer metastasis, meningiomas but also non-neoplastic brain demonstrated comparable relative cell counts as well. Moreover, there was no correlation between KLF8 expression and the expression pattern of the assumed proliferation marker Ki67, which showed high variability between different tumor grade (9% (LGG), 6% (AA) and 15% (GBM) of Ki67-immunopositive cells). Densitometric analysis of Western blotting revealed that the relative amount of KLF8-protein did also not differ between the highly aggressive and proliferative GBM (1.05) compared to LGG (0.93; p<0.05, studens t-test). As demonstrated for some other non-glial cancer entities, KLF8-knockdown by shRNA in U87-MG cells confirmed its functional relevance, leading to an almost complete loss of tumor cell proliferation. Selective blocking of KLF8 might represent a novel anti-proliferative treatment strategy for malignant gliomas. Yet, its simultaneous expression in non-proliferating tissues could hamper this approach.     

脑胶质瘤组织CMTM1、ME2表达与临床病理特征和复发的关系研究

摘要 目的：探讨脑胶质瘤组织含CKLF样MARVEL跨膜结构域的蛋白1（CMTM1）、苹果酸酶2（ME2）表达与临床病理特征和复发的关系。方法：选取2018年1月～2021年1月徐州医科大学附属医院接受切除手术的92例脑胶质瘤患者，根据术后是否复发分为复发组和未复发组。采用免疫组化法检测脑胶质瘤组织和瘤旁组织CMTM1、ME2表达，分析二者与临床病理特征的关系，采用多因素Logistic回归分析脑胶质瘤患者术后复发的影响因素。结果：与瘤旁组织比较，脑胶质瘤组织中CMTM1、ME2阳性表达率升高（P＜0.05）。不同分化程度、世界卫生组织（WHO）中枢神经系统肿瘤分类脑胶质瘤组织中CMTM1、ME2阳性表达率比较，差异有统计学意义（P＜0.05）。随访2年，92例脑胶质瘤患者术后复发率为47.83%（44/92）。多因素Logistic回归分析显示，低分化、WHO中枢神经系统肿瘤分类Ⅲ～Ⅳ级、部分切除和CMTM1、ME2阳性表达为脑胶质瘤患者术后复发的独立危险因素（P＜0.05）。结论：脑胶质瘤组织中CMTM1、ME2阳性表达率升高，与分化程度、WHO中枢神经系统肿瘤分类等级和术后复发有关，可能成为脑胶质瘤患者术后复发的辅助评估指标。     

Glioma: Application of Whole-Tumor Texture Analysis of Diffusion-Weighted Imaging for the Evaluation of Tumor Heterogeneity

Background and Purpose

To apply a texture analysis of apparent diffusion coefficient (ADC) maps to evaluate glioma heterogeneity, which was correlated with tumor grade.

Materials and Methods

Forty patients with glioma (WHO grade II (n = 8), grade III (n = 10) and grade IV (n = 22)) underwent diffusion-weighted imaging (DWI), and the corresponding ADC maps were obtained. Regions of interest containing the lesions were drawn on every section of the ADC map containing the tumor, and volume-based data of the entire tumor were constructed. Texture and first order features including entropy, skewness and kurtosis were derived from the ADC map using in-house software. A histogram analysis of the ADC map was also performed. The texture and histogram parameters were compared between low-grade and high-grade gliomas using an unpaired student’s t-test. Additionally, a one-way analysis of variance analysis with a post-hoc test was performed to compare the parameters of each grade.

Results

Entropy was observed to be significantly higher in high-grade gliomas than low-grade tumors (6.861±0.539 vs. 6.261±0.412, P  = 0.006). The fifth percentiles of the ADC cumulative histogram also showed a significant difference between high and low grade gliomas (836±235 vs. 1030±185, P = 0.037). Only entropy proved to be significantly different between grades III and IV (6.295±0.4963 vs. 7.119±0.3165, P<0.001). The diagnostic accuracy of ADC entropy was significantly higher than that of the fifth percentile of the ADC histogram (P = 0.0034) in distinguishing high- from low-grade glioma.

Conclusion

A texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.     

Stathmin 在微血管内皮细胞的表达与胶质瘤恶性程度关系

目的:检测Stathmin在正常脑组织及不同级别胶质瘤微血管内皮细胞中的表达情况。方法:利用结合CD105单克隆抗体的免疫磁珠内皮细胞分选系统特异性分选出68例胶质瘤微血管内皮细胞(其中低级别胶质瘤(WHO分级Ⅰ-Ⅱ)24例,高级别胶质瘤(WHO分级Ⅲ-Ⅳ)44例)和20例正常脑组织微血管内皮细胞。应用免疫组化、RT-PCR和Western blot检测Stathmin在胶质瘤微血管内皮细胞和正常脑组织微血管内皮细胞中的表达。结果:免疫组化证实Stathmin在正常脑组织微血管内皮细胞、低级别胶质瘤微血管内皮细胞和高级别胶质瘤微血管内皮细胞的表达百分率分别是20%,66%和95%(P<0.05)。RT-PCR和Western blot法检测显示,Stathmin在胶质瘤微血管内皮细胞中的表达明显增高。低级别胶质瘤组、高级别胶质瘤组分别与正常组比较,均有显著性差异(P<0.01);且低级别胶质瘤组与高级别胶质瘤组比较,有显著性差异(P<0.01),随着胶质瘤恶性程度的增加,Stathmin表达上调,具有统计学意义。结论:Stathmin在脑胶质瘤微血管内皮细胞中表达随肿瘤恶性程度增高而增加,可能为脑胶质瘤的生物治疗提供一个新靶点。     

Increased expression of microRNA-9 predicts an unfavorable prognosis in human glioma

microRNA-9 (miR-9) has been found to be upregulated along with tumor progression of gliomas by microarray-based expression profiling, and also be strongly linked to glioblastoma subtypes. However, its prognostic value in glioma is still elusive. miR-9 expression in human gliomas and nonneoplastic brain tissues was measured by real-time quantitative RT-PCR assay. miR-9 expression in glioma tissues was significantly higher than that in corresponding nonneoplastic brain tissues (P < 0.001). The increased expression of miR-9 was more frequently observed in glioma tissues with high WHO grade than those with low WHO grade tissues (P = 0.001). The expression levels of miR-9 in glioma tissues with low Karnofsky performance score (KPS) were also significantly higher than those with high KPS (P = 0.008). Moreover, the overall survival of glioma patients with high miR-9 expression was obviously lower than that with low miR-9 expression (P < 0.001). Multivariate analysis further showed that high miR-9 expression was an independent prognostic factor for overall survival in glioma patients (P = 0.01). More importantly, the subgroup analyses indicated that the overall survival of glioma patients with high WHO grade (III–IV) was significantly worse for high miR-9 expression group than for low miR-9 expression group (P < 0.001), but no significant difference was found for patients with low WHO grade (I–II). These findings suggest for the first time that the increased expression of miR-9 may play an important role in tumor progression in human gliomas. miR-9 might be a useful marker for predicting the clinical outcome of glioma patients, especially for advanced subtypes.     

Genetic analysis to complement histopathological diagnosis of brain tumors

Gliomas, the most frequent tumors originating in the human nervous system, are divided into various subtypes. Currently, microscopic examination alone is insufficient for classification and grading so that genetic profiles are increasingly being emphasized in recognition of the emerging role of molecular diagnostic approaches to glioma classification. Glioblastomas (WHO grade IV) may develop de novo (primary glioblastomas) or through progression from lower-grade astrocytomas (secondary glioblastomas), while both glioblastomas show similar histological features. In contrast, they do constitute distinct disease entities that evolve through different genetic pathways, and are likely to differ in prognosis and response to therapy. Oligodendrogliomas (WHO grade II) account for 2.7% of brain tumors and 5-18% of all gliomas. Since this tumor is recognized as a particular subtype of glioma that shows remarkable responses to chemotherapy, a correct diagnosis is of prime importance. The difficulty is that histological differentiation of oligodendrogliomas from diffuse astrocytomas is highly subjective in cases without typical morphological features and there is a lack of reliable immunohistochemical markers. While histological distinction of low-grade gliomas from reactive astrocytes is also often difficult, reactive astrocytes usually lack genetic alterations. More biological and molecular approaches to glioma classification thus appear warranted to provide improved means to achieve correct diagnoses.