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ObjectivesPhosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma.MethodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC).ResultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).ConclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

Purpose We aim to investigate the impacts of extent of resection and adjuvant radiotherapy on survival of high-grade meningiomas (WHO grade II–III) according to modern diagnosis and management. Methods Patients with high-grade meningiomas were identified in the Surveillance Epidemiology and End Results (SEER) database between 2000 and 2015 and used for survival analysis. Propensity score matching (PSM) was conducted to reduce selection bias. Another 92 patients from Sun Yat-sen University Cancer Center (SYSUCC) were used for validation. Results 530 patients were enrolled from SEER. Patients with gross total resection (GTR) had no significantly different overall survival (OS) compared with those with subtotal resection (STR), even after performing PSM between these two groups. Multivariable analysis found that age ≥ 65 years (HR 2.22, P 6 cm (HR 1.59, P = 0.004) and grade III tumor (HR 4.31, P

Background and Aim: Approximately 60% of glioblastoma multiforme (GBM) patients possess an unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) gene, which confers a limited response to standard-of-care treatment with temozolomide (TMZ), resulting in a lower survival. Dianhydrogalactitol (VAL-083) is a novel bi-functional DNA-targeting agent that induces interstrand cross-links at N7-guanine, leading to DNA double-strand breaks and ultimately cell death. VAL-083 circumvents MGMT-mediated repair of the O6 guanine alkylator TMZ. A Phase 2 study has been initiated for VAL-083 in newly diagnosed MGMT unmethylated GBM. Subjects and Methods: The study has two parts: part 1 is a dose–escalation and induction format to enroll up to ten patients in which they received VAL-083 at 20, 30, or 40 mg/m2 per day for 3 days every 21 days concurrently with standard radiation treatment and VAL-083 for up to eight additional cycles. Part 2 comprises an expansion phase to enroll up to twenty additional patients. This study was performed with approval by the Institutional Review Board of Sun Yat-sen University Cancer Center (B2016-058-01) on January 13, 2017, and registered with the ClinicalTrials.gov (NCT03050736) on February 13, 2017. Results: After completion of dose escalation, VAL-083, 30 mg/m2 per day, in combination with radiation therapy, was generally safe and well tolerated. At the cutoff date, 23 patients had been enrolled, 14 of whom had been treated in the expansion phase. Consistent with prior studies, myelosuppression was the most common adverse event. Pharmacokinetic assessment indicated that the levels of VAL-083 were as high in the cerebrospinal fluid as in plasma, 2 h postinfusion. Of the 22 patients who had reached their four precycle magnetic resonance imaging assessments, 12 were assessed with disease progression, with a median progression-free survival of 9.9 (95% confidence interval 7.3–12.0) months for all the patients studied. Conclusion: These preliminary data support VAL-083 as a potentially valuable treatment option for newly diagnosed GBM.

Purpose Isocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI). Methods In this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non‐enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all‐relevant feature selection and a random forest classification for predicting IDH1. Four single‐region models and a model combining all‐region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients). Results Among the four single‐region radiomics models, the edema model achieved the best accuracy of 96% and the best F1‐score of 0.75 while the non‐enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor‐core model (accuracy 0.96, AUC 0.86 and F1‐score 0.75) and the whole‐tumor model (accuracy 0.96, AUC 0.88 and F1‐score 0.75) was slightly better than the single‐regional models. The 8‐feature all‐region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1‐score 0.78. Among all models, the model combining all‐region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1‐score 0.84. Conclusions The radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all‐region features performed better than the single‐region models, while combining age with all‐region features achieved the best performance.

Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Background Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment. Materials and methods Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively. Results A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit. Conclusion Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.

Background Vessels with different microcirculation patterns are required for glioblastoma (GBM) growth. However, details of the microcirculation patterns in GBM remain unclear. Here, we examined the microcirculation patterns of GBM and analyzed their roles in patient prognosis together with two well‐known GMB prognosis factors (O6‐methylguanine DNA methyltransferase [MGMT] promoter methylation status and isocitrate dehydrogenase [IDH] mutations). Methods Eighty GBM clinical specimens were collected from patients diagnosed between January 2000 and December 2012. The microcirculation patterns, including endothelium‐dependent vessels (EDVs), extracellular matrix‐dependent vessels (ECMDVs), GBM cell‐derived vessels (GDVs), and mosaic vessels (MVs), were evaluated by immunohistochemistry (IHC) and immunofluorescence (IF) staining in both GBM clinical specimens and xenograft tissues. Vascular density assessments and three‐dimensional reconstruction were performed. MGMT promoter methylation status was determined by methylation‐specific PCR, and IDH1/2 mutations were detected by Sanger sequencing. The relationship between the microcirculation patterns and patient prognosis was analyzed by Kaplan‐Meier method. Results All 4 microcirculation patterns were observed in both GBM clinical specimens and xenograft tissues. EDVs were detected in all tissue samples, while the other three patterns were observed in a small number of tissue samples (ECMDVs in 27.5%, GDVs in 43.8%, and MVs in 52.5% tissue samples). GDV‐positive patients had a median survival of 9.56 months versus 13.60 months for GDV‐negative patients (P = 0.015). In MGMT promoter‐methylated cohort, GDV‐positive patients had a median survival of 6.76 months versus 14.23 months for GDV‐negative patients (P = 0.022). Conclusion GDVs might be a negative predictor for the survival of GBM patients, even in those with MGMT promoter methylation.

The intraoperative tumor-free principles refer to the operation techniques that must be carried out to prevent the exfoliation and planting of tumor cells during the operation, so as to prevent local recurrence and distant metastasis. Following the intraoperative tumor-free principles has been valued in resection of extracranial solid tumors. However, the significance of intraoperative tumor-free principles for brain tumors is still unclear. We retrospectively analyzed 106 patients with primary glioblastoma who underwent resection following the intraoperative tumor-free principles from 2010 to 2016. By February 28, 2019, 11 (10.4%) patients were lost to follow-up. The median overall survival (OS) was 20.2 months, and the 1-, 3-, and 5-year survival rates were 71%, 30%, and 26%, respectively. For patients with complete tumor resection, the median OS was 24.8 months, and the 1-, 3-, and 5-year survival rates were 80%, 38%, and 36%, respectively. Patients who received postoperative Stupp regimen had a median OS of 74.1 months, and the 1-, 3-, and 5-year survival rates were 82%, 51%, and 51%, respectively. A total of 36 patients had complete tumor resection followed by Stupp regimen. Their 1-, 3-, and 5-year survival rates were 94%, 68%, and 68%, respectively. Median OS was not reached. The patient with the longest OS had survived for 108 months and was still alive. In summary, the OS of patients in this study was relatively longer than that reported in most previous literatures, which suggests that following the intraoperative tumor-free principles in resection of GBM can benefit patient survival.

e14019 Background: High-grade glioma (HGG) is the most common malignant brain tumor and lacks effective treatment regimen. Anlotinib is a multikinase inhibitor blocking angiogenesis and tumor cell proliferation simultaneously. This study was performed to evaluate the efficacy and safety of anlotinib alone or in combination with temozolomide (TMZ) in the treatment of recurrent HGG. Methods: This is a single-center, retrospective study. Eligible patients (pts) were diagnosed with pathologically confirmed high grades (WHO III/IV) glioma and had recurrent or progressive disease on or after prior treatment. Other key eligibility criteria included Karnofsky Performance Status (KPS) ≥ 40, aged 16 ̃75 years and having at least one measurable lesion (RANO criteria). Pts were administrated with anlotinib once daily for 14 days every 3 weeks till disease progression, intolerable toxicities or death. The initial dose was 12mg for younger pts ( < 40 years old) with KPS ≥ 60 and 10 mg for others. Combination treatment was allowed if previous TMZ was effective and tolerable. TMZ was administered on dose-dense schedule (150mg/m2, QD, d1-d7 and d15-d21 every 28 days) or metronomic schedule (25-50mg/m2 QD). The primary endpoint was progression-free survival at 6 months (PFS6m) accessed according to RANO criteria. The second endpoints included overall survival (OS), objective response rate (ORR) and disease control rate (DCR). Results: Between August 2019 and June 2020, 23 pts with HGG (15 grade IV; 8 grade III; 12 males, 11 females) were enrolled. The median age and median KPS was 42 years and 60. 16 pts have multifocal or disseminated disease. 18 pts received ≥2 lines previous treatment. At the data cutoff date on September 2020, the median duration of treatment was 9 weeks (range: 3-33). The PFS6m was 39.1% and the median PFS was 4.2 months (95% CI: 2.8, 5.6). The median OS was not reached (95% CI: NE, NE) and the OS at 12 months (OS12m) was 54.8%. 8 pts observed tumor response and 9 pts had stable disease. The ORR and DCR were 34.8% and 73.9% respectively. The results of survival analysis for subgroups were summarized in table below. Grade 1 or 2 treatment-related adverse events (TRAEs) occurred in 65.2% pts. No ≥ grade 3 TRAE was found. All hematological TRAEs occurred in patients received combination regimen. No TRAE-induced treatment termination occurred. The lower incidence of TRAE may partly attributed to that most pts (18/23) received lower initial dose (10mg) of anlotinib and the relatively shorter treatment duration. Conclusions: This study showed treatment with anlotinib alone or in combination with TMZ had promising efficacy and favorable tolerability in patients with recurrent HGG.[Table: see text]

Background Ependymal tumors are pathologically defined intrinsic neoplasms originating in the intracranial compartments or the spinal cord that affect both children and adults. The recently integrated classification of ependymomas based on both histological and molecular characteristics is capable of subgrouping patients with various prognoses. However, the application of histological and molecular markers in Chinese patients with ependymomas has rarely been reported. We aimed to demonstrate the significance of histological characteristics, the v-relavian reticuloendotheliosis viral oncogene homolog A (RELA) fusions and other molecular features in ependymal tumors. Methods We reviewed the histological characteristics of ependymal tumors using conventional pathological slides and investigate the RELA fusions and Cylclin D1 (CCND1) amplification by Fluorescence in situ hybridization (FISH) and trimethylation of histone 3 lysine 27 (H3K27me3) expression by immunohistochemistry (IHC) methods. SPSS software was used to analyze the data. Results We demonstrated that hypercellularity, atypia, microvascular proliferation, necrosis, mitosis, and an elevated Ki-67 index, were tightly associated with an advanced tumor grade. Tumor location, necrosis, mitosis and the Ki-67 index were related to the survival of the ependymomas, but Ki67 was the only independent prognostic factor. Additionally, RELA fusions, mostly presented in pediatric grade III intracranial ependymomas, indicated decreased survival times of patients, and closely related to the patients’ age, tumor grade, cellularity, cellular atypia, necrosis and Ki67 index in the intracranial ependymal tumors, whereas reduction of H3K27me3 predicted the worse prognosis in ependymal tumors. Conclusions Histological and molecular features facilitate tumor grading and prognostic predictions for ependymal tumors in Chinese patients.

BACKGROUND: Glioblastoma Multiforme (GBM) patients with isocitrate dehydrogenase 1 (IDH1) mutations have significantly improved prognosis than those without such mutations. We aimed to preoperatively predict IDH1 mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI). MATERIAL AND METHODS: In total 225 patients were recruited in this retrospective multicenter study. 1614 quantitative features were extracted from multiple tumor subregions (including enhancement area, non-enhancement area, necrosis and edema) in multiparametric MRI. After intensitynormalization, tumor subregion segmentation, resampling-based data balancing and relevant feature selection, a multiregional radiomics model was built using a machine-learning method for prediction of IDH1 mutation from a primary cohort (118 patients) and tested on an independent validation cohort (107 patients). Four single-region radiomics models with features from each tumor subregion, and a model combining multiregional features with clinical factors (age, sex, and Karnofsky performance status) were also built and tested. RESULTS: Among four single-region radiomics models, the model built from edema region achieved the best accuracy of 96% and the best F1-score of 0.75 in the independent validation cohort. The 8-feature multiregional radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90 and an F1-score 0.78 in the validation cohort, which significantly outperformed the single-region models. Among all predictive models, the model combining multiregional imaging features with patient age achieved the best performance of an accuracy 97%, an AUC 0.96 and an F1-score 0.84 in the validation cohort. CONCLUSION: The radiomics-based model built with a minimal set of multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The proposed predictor may serve as a potential noninvasive biomarker to guide preoperative GBM patient care.

This study aimed to build a reliable radiomics model from magnetic resonance imaging (MRI) for pretreatment prediction of MGMT methylation status in Glioblastoma. High-throughput radiomics features were automatically extracted from multiparametric MRI, including location features, geometry features, intensity features and texture features. A machine learning method was used to select a minimal set of all-relevant features. Based on these selected features, a radiomics model were built by using a random forest classifier for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing radiomics features and clinical factors were built and evaluated. The radiomics model with 6 all-relevant features allowed pretreatment prediction of MGMT methylation (AUC = 0.88, accuracy = 80%). Combing clinical factors with radiomics features did not benefit the prediction performance. The proposed radiomics model could provide a tool to guide preoperative patient care and made a step forward radiomics-based precision medicine for GBM patients.

Glioblastoma is a highly invasive and vascularized primary CNS tumor. Although anti-angiogenesis therapy was effective in some tumors but not in GBM. Vasculogenic mimicry (VM), a vessel-like structure formed by highly invasive tumor cells, has been considered responsible for the failure of GBM patients for anti-angiogenesis therapy. Tenascin-c (TNC), an extracellular protein involves in tumor angiogenesis, is overexpressed in GBM. However, how TNC contributes to VM formation remains unclear. We first knocked down the expression of TNC in two glioma cell lines (U251 and A172), which had TNC overexpression. Then the formations of VM were observed by three-dimensional culture. Transwell and wound-healing assay were applied to investigate the role of TNC in cell invasion and migration abilities. The expression of MMP2, MMP9 and the phosphorylation status of AKT were determined by western-blot, RT-qPCR and gelatin zymography. Furthermore, AKT inhibitor, MK-2206 was used to block the AKT/MMP2/MMP9 signaling pathway to investigate its role in TNC regulation at different concentration (0µm, 5µm, 10µm and 20µm) for 24 hours. The knockdown efficiency was detected by western blot and RT-qPCR in U251 and A172 after siRNA transfection for 48 hours. Three-dimensional culture showed that VM-like structure formation decreased in knockdown groups compared to controls dramatically (19.67 ± 6.65 and 46.34 ± 9.10 vs. 125.67 ± 8.34 p

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P

BACKGROUND AND OBJECTIVE: We have found, in previous study, there were four types of microcirculation pattern in human glioblastoma: endothelium dependent vessels, tumor cell dependent vessels, extracellular matrix (ECM) dependent vessels, and mosaic vessels. This study is, by utilizing live cell imaging technique, to visualize the differentiation of glioma stem cells (GSCs) into endothelial cells. METHODS: Live cell imaging was used to record the transforming process of GSCs. The immunofluorescence staining was also performed to evaluate the differentiation phenomenon during GSCs three dimensional culture at time points of 0 hour, 2 hours and 6 hours. RESULTS: Tubular structures were observed in GSC-1 three dimensional culture, which was derived from a GBM patient. Some GSC-1 cells were found endothelial maker CD34 expression, and lost stem phenotype following three dimensional culture. The number of CD34+ cells were increased, and reach to highest up to 6-hour three dimensional culture (P < 0.05). Two main phenomenon were recorded by live cell imaging: (1) the number of CD34+ cells was gradually increasing following GSCs three dimensional culture; (2) The process of a single GSC-1 cell from CD34- to CD34+ . CONCLUSIONS: The result of live cell imaging has confirmed the results from conventional immunofluorescence staining that glioma stem cell may differentiate into endothelial cells, suggesting that GSCs could contribute to vasculargenesis in GBM.

BACKGROUND: Vasculogenic mimicry(VM) has been reported in many malignant human tumors, including gliomas. In the present study, we have found four types of microcirculation vessels in human glioblastomas. Method and material: Thirty-seven human glioblastoma samples were examined for VM by CD34 and PAS dual staining,as well as immunofluorescence dual staining for CD34 and GFAP. The GFAP positive (GFAP+) cells were considered as glioma cells. Even though CD34 positive (CD34+) was endothelial cells, but both CD34 + / GFAP+ should be also considered as glioma cells. Result: CD34 and PAS dual staining revealed 4 types of microcirculation vessels in human glioblastoma samples: endothelium dependent vessels, tumor cell dependent vessels, extracellular matrix (ECM) dependent vessels, and mosaic vessels. While immunofluorescence study confirmed VM should be vessels lined with GFAP+ cells (could be CD34- or co-express CD34). Twelve samples were found VM by CD34 and PAS dual staining (32%), while 20 samples were revealed VM by immunofluorescence dual staining (57%). CONCLUSION: Our results suggest that glioblastoma cells have potential to express endothelial cell marker CD34. And immunofluorescence dual staining is a more sensitive as well accurate technique revealing VM vessels in glioblastoma.

Background In this study, we assessed the prognostic value of the lymph node ratio (LNR), established a hypothetical tumor–ratio–metastasis (TRM) staging system and compared it with the 7th edition International Union Against Cancer pathological N (pN) and tumor–node–metastasis (TNM) system. Patients and methods A total of 1343 gastric cancer patients undergoing D2 resection were staged using the TRM staging system and the 7th edition TNM system. Optimal cut points of LNR were calculated using X-tile software and validated by bootstrapping. Homogeneity, discriminatory ability, and monotonicity of gradients of the TRM and TNM systems were compared using linear trend χ2, likelihood ratio χ2 statistics, and Akaike information criterion (AIC) calculations. Results Optimal cut points classified patients into LNR0 (0%), LNR1 (1%–30%), LNR2 (31%–60%), and LNR3 (61%–100%) groups. In univariate, multivariate and stratified analyses, the LNR staging showed superiority to the 7th edition pN staging. The TRM staging system had higher linear trend and likelihood ratio χ2 scores and smaller AIC values compared with those for the TNM system, which represented the optimum prognostic stratification. Conclusions The novel TRM staging system predicts survival of gastric cancer more accurately than the 7th edition TNM system. It may be considered as an alternative to TNM system.

Glioblastoma is one of the most angiogenic human tumors and characterized by microvascular proliferations. A better understanding of glioblastoma vasculature is needed to optimize anti-angiogenic therapy that has shown a promising but incomplete efficacy. The present study examined 48 glioblastomas by CD34 endothelial marker periodic acid-Schiff (PAS) dual staining and found non-endothelial cell-lined blood vessels that were formed by tumor cells (vasculogenic mimicry, VM) existing in a fraction of these tumors. We hypothesized that CD133-positive glioblastoma stem-like cells (GSCs) may play a pivotal role in glioblastoma VM formation and then demonstrated in vitro and in vivo that a subset of GSCs were capable of vasculogenesis. Moreover, we found that several growth factors involved in normal angiogenesis were expressed in GSCs. We describe here a new mechanism of alternative glioblastoma vascularization and open a new perspective for the anti-vascular treatment strategy.