Bufotalin enhances apoptosis and TMZ chemosensitivity of glioblastoma cells by promoting mitochondrial dysfunction via AKT signaling pathway.

Glioblastoma multiforme (GBM) is the most prevalent and lethal primary intracranial neoplasm in the adult population, with treatments of limited efficacy. Recently, bufotalin has been shown to have anti-cancer activity in a variety of cancers. This investigation aims to investigate the effect of bufotalin on GBM and elucidate its potential underlying mechanism. Our results show that bufotalin not only inhibits the proliferation and epithelial-mesenchymal transition (EMT) but also triggers apoptosis in GBM cells. The result of RNA-seq indicated that bufotalin could induce mitochondrial dysfunction. Moreover, our observations indicate that bufotalin induces an excessive accumulation of intracellular reactive oxygen species (ROS) in GBM cells, leading to mitochondrial dysfunction and the dephosphorylation of AKT. Moreover, bufotalin improved TMZ sensitivity of GBM cells in vitro and in vivo. In conclusion, bufotalin enhances apoptosis and TMZ chemosensitivity of glioblastoma cells by promoting mitochondrial dysfunction via AKT signaling pathway.

Use of circulating tumor cells and microemboli to predict diagnosis and prognosis in diffuse glioma.

OBJECTIVE: Circulating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma. METHODS: Sixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma. RESULTS: CTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation. CONCLUSIONS: The authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTM-based survival models can enable clinicians to evaluate patients' response to surgery as well as their outcomes.

A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury.

Introduction: Precise classification has an important role in treatment of pressure injury (PI), while current machine-learning or deeplearning based methods of PI classification remain low accuracy. Methods: In this study, we developed a deeplearning based weighted feature fusion architecture for fine-grained classification, which combines a top-down and bottom-up pathway to fuse high-level semantic information and low-level detail representation. We validated it in our established database that consist of 1,519 images from multi-center clinical cohorts. ResNeXt was set as the backbone network. Results: We increased the accuracy of stage 3 PI from 60.3% to 76.2% by adding weighted feature pyramid network (wFPN). The accuracy for stage 1, 2, 4 PI were 0.870, 0.788, and 0.845 respectively. We found the overall accuracy, precision, recall, and F1-score of our network were 0.815, 0.808, 0.816, and 0.811 respectively. The area under the receiver operating characteristic curve was 0.940. Conclusions: Compared with current reported study, our network significantly increased the overall accuracy from 75% to 81.5% and showed great performance in predicting each stage. Upon further validation, our study will pave the path to the clinical application of our network in PI management.

New trends in brain tumor immunity with the opportunities of lymph nodes targeted drug delivery.

Lymph nodes targeted drug delivery is an attractive approach to improve cancer immunotherapy outcomes. Currently, the depth of understanding of afferent and efferent arms in brain immunity reveals the potential clinical applications of lymph node targeted drug delivery in brain tumors, e.g., glioblastoma. In this work, we systematically reviewed the microenvironment of glioblastoma and its structure as a basis for potential immunotherapy, including the glial-lymphatic pathway for substance exchange, the lymphatic drainage pathway from meningeal lymphatic vessels to deep cervical lymph nodes that communicate intra- and extracranial immunity, and the interaction between the blood-brain barrier and effector T cells. Furthermore, the carriers designed for lymph nodes targeted drug delivery were comprehensively summarized. The challenges and opportunities in developing a lymph nodes targeted delivery strategy for glioblastoma using nanotechnology are included at the end.

Combination of multi-modal MRI radiomics and liquid biopsy technique for preoperatively non-invasive diagnosis of glioma based on deep learning: protocol for a double-center, ambispective, diagnostical observational study.

Background: 2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. Methods: This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Discussion: Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. Clinical trail registration: This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.

[Corrigendum] HHLA2 is a novel prognostic predictor and potential therapeutic target in malignant glioma.

Following the publication of this article, an interested reader drew to the authors' attention that, in Fig. 1F on p. 2311 showing a representative high­grade glioma specimen, the data were either duplicated or overlapping with the data featured in Fig. 1D, which showed a low­grade glioma specimen. After having consulted their original data, the authors have realized that the data for Fig. 1D were inadvertently selected incorrectly. The corrected version of Fig. 1, now showing the correct data for the high­magnification high­grade glioma specimen in Fig. 1F, is shown on the next page. The authors sincerely apologize for the error that was introduced during the preparation of this figure, thank the Editor of Oncology Reports for granting them the opportunity to publish a Corrigendum, and are grateful to the reader for alerting them to this issue. The authors also regret any inconvenience that this mistake may have caused. [Oncology Reports 42: 2309-2322, 2019; DOI: 10.3892/or.2019.7343].

The RNA-binding protein CSTF2 regulates BAD to inhibit apoptosis in glioblastoma.

RNA-binding proteins (RBP) regulate several aspects of co- and post-transcriptional gene expression in cancer cells. CSTF2 is involved in the expression of many cellular mRNAs and involved in the 3'-end cleavage and polyadenylation of pre-mRNAs to terminate transcription. However, the role of CSTF2 in human glioblastoma (GBM) and the underlying mechanisms remain unclear. In the present study, CSTF2 was found to be upregulated in GBM, and its high expression predicted poor prognosis. Knockdown CSTF2 induced GBM cell apoptosis both in vitro and in vivo. Specific mechanism studies showed that CSTF2 unstabilized the mRNA of the BAD protein by shortening its 3' UTR. Additionally, an increase in the expression level of CSTF2 decreased the expression level of BAD. In conclusion, CSTF2 binds to the mRNA of the BAD protein to shorten its 3'UTR, which negatively affects the BAD mediated apoptosis and promotes GBM cell survival.

Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.

Background: Timely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Methods: This was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75-25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer-Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance. Results: A total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73-0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68-0.9, P = 0.46), 0.675 (95%CI: 0.56-0.79, P < 0.01), 0.677 (95%CI: 0.57-0.77, P < 0.01), and 0.78 (95%CI: 0.68-0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78-0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely. Conclusions: In this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions. Clinical Trial Registration: http://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.

Esterase-responsive and size-optimized prodrug nanoparticles for effective intracranial drug delivery and glioblastoma treatment.

Glioblastoma multiforme (GBM) is the intracranial malignancy with the highest rates of morbidity and mortality. Chemotherapy is often ineffective against GBM due to the presence of the blood-brain barrier (BBB); however, the application of nanotechnology is expected to overcome this limitation. Poly(lactic-co-glycolic acid) (PLGA) is a degradable and nontoxic functional polymer with good biocompatibility that is widely used in the pharmaceutical industry. Previous studies have shown that the ability of PLGA nanoparticles (NPs) to penetrate the BBB is largely determined by their size; however, determination of the optimal PLGA NP size requires further research. Here, we report a tandutinib-based prodrug (proTan), which responds to the GBM microenvironment, that was combined with NPs to overcome the BBB. AMD3100-PLGA NPs loaded with proTan inhibited tumor growth and effectively prolonged the survival of tumor-bearing mice.

A Novel Karyoplasmic Ratio-Based Automatic Recognition Method for Identifying Glioma Circulating Tumor Cells.

Background: Detection of circulating tumor cells (CTCs) is a promising technology in tumor management; however, the slow development of CTC identification methods hinders their clinical utility. Moreover, CTC detection is currently challenging owing to major issues such as isolation and correct identification. To improve the identification efficiency of glioma CTCs, we developed a karyoplasmic ratio (KR)-based identification method and constructed an automatic recognition algorithm. We also intended to determine the correlation between high-KR CTC and patients' clinical characteristics. Methods: CTCs were isolated from the peripheral blood samples of 68 glioma patients and analyzed using DNA-seq and immunofluorescence staining. Subsequently, the clinical information of both glioma patients and matched individuals was collected for analyses. ROC curve was performed to evaluate the efficiency of the KR-based identification method. Finally, CTC images were captured and used for developing a CTC recognition algorithm. Results: KR was a better parameter than cell size for identifying glioma CTCs. We demonstrated that low CTC counts were independently associated with isocitrate dehydrogenase (IDH) mutations (p = 0.024) and 1p19q co-deletion status (p = 0.05), highlighting its utility in predicting oligodendroglioma (area under the curve = 0.770). The accuracy, sensitivity, and specificity of our algorithm were 93.4%, 81.0%, and 97.4%, respectively, whereas the precision and F1 score were 90.9% and 85.7%, respectively. Conclusion: Our findings remarkably increased the efficiency of detecting glioma CTCs and revealed a correlation between CTC counts and patients' clinical characteristics. This will allow researchers to further investigate the clinical utility of CTCs. Moreover, our automatic recognition algorithm can maintain high precision in the CTC identification process, shorten the time and cost, and significantly reduce the burden on clinicians.

Rho family GTPase 1 (RND1), a novel regulator of p53, enhances ferroptosis in glioblastoma.

BACKGROUND: Ferroptosis is an iron dependent cell death closely associated with p53 signaling pathway and is aberrantly regulated in glioblastoma (GBM), yet the underlying mechanism needs more exploration. Identifying new factors which regulate p53 and ferroptosis in GBM is essential for treatment. METHODS: Glioma cell growth was evaluated by cell viability assays and colony formation assays. Lipid reactive oxygen species (ROS) assays, lipid peroxidation assays, glutathione assays, and transmission electron microscopy were used to assess the degree of cellular lipid peroxidation of GBM. The mechanisms of RND1 in regulation of p53 signaling were analyzed by RT-PCR, western blot, immunostaining, co-immunoprecipitation, ubiquitination assays and luciferase reporter assays. The GBM-xenografted animal model was constructed and the tumor was captured by an In Vivo Imaging System (IVIS). RESULTS: From the The Cancer Genome Atlas (TCGA) database, we summarized that Rho family GTPase 1 (RND1) expression was downregulated in GBM and predicted a better prognosis of patients with GBM. We observed that RND1 influenced the glioma cell growth in a ferroptosis-dependent manner when GBM cell lines U87 and A172 were treated with Ferrostatin-1 or Erastin. Mechanistically, we found that RND1 interacted with p53 and led to the de-ubiquitination of p53 protein. Furthermore, the overexpression of RND1 promoted the activity of p53-SLC7A11 signaling pathway, therefore inducing the lipid peroxidation and ferroptosis of GBM. CONCLUSIONS: We found that RND1, a novel controller of p53 protein and a positive regulator of p53 signaling pathway, enhanced the ferroptosis in GBM. This study may shed light on the understanding of ferroptosis in GBM cells and provide new therapeutic ideas for GBM.

A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.

Backgrounds: As a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop the conventional regression and tree-based models and determine which model has better prediction performance for DCI development in hospitalized elderly patients after aSAH. Methods: This was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70-30%. One conventional regression and tree-based model, such as least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), was developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% CI were employed to evaluate the model prediction performance. A DeLong test was conducted to calculate the statistical differences among models. Finally, we figured the importance weight of each feature to visualize the contribution on DCI. Results: There were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in the model internal validation, DT of 0.836 (95% CI: 0.747-0.926, p = 0.15), RF of 1 (95% CI: 1-1, p < 0.05), and XGBoost of 0.931 (95% CI: 0.885-0.978, p = 0.01) outperformed LASSO of 0.793 (95% CI: 0.692-0.893). However, the LASSO scored a highest AUC-ROC value of 0.894 (95% CI: 0.8-0.989) than DT of 0.764 (95% CI: 0.6-0.928, p = 0.05), RF of 0.821 (95% CI: 0.683-0.959, p = 0.27), and XGBoost of 0.865 (95% CI: 0.751-0.979, p = 0.69) in independent external validation. Moreover, the LASSO had a highest AUC-PR value of 0.681 than DT of 0.615, RF of 0.667, and XGBoost of 0.622 in external validation. In addition, we found that CT values of subarachnoid clots, aneurysm therapy, and white blood cell counts were the most important features for DCI in elderly patients with aSAH. Conclusions: The LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.

Predictive effects of admission white blood cell counts and hounsfield unit values on delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Neuroinflammatory response is deemed the primary pathogenesis of delayed cerebral ischemia (DCI) caused by aneurysmal subarachnoid hemorrhage (aSAH). Both white blood cell (WBC) count and Hounsfield Unit (HU) are gradually considered can reflect inflammation in DCI. This study aims to identify the relationship between WBC count and HU value and investigate the effects of both indicators in predicting DCI after aSAH. METHODS: We enrolled 109 patients with aSAH admitted within 24 h of onset in our study. A multivariate logistic regression analysis was used to evaluate the admission WBC count, HU value, and combined WBC-HU associated with DCI. The receiver operating characteristic curve and area under the curve (AUC) were used to determine thresholds and detect the predictive ability of these predictors. These indicators were also compared with the established inflammation markers. RESULTS: Thirty-six (33%) patients developed DCI. Both WBC count and HU value were strongly associated with the admission glucose level (ρ = .303, p = .001; ρ = .273, p = .004), World Federation of Neurosurgical Societies grade (ρ = .452, p < .001; ρ = .578; p < .001), Hunt-Hess grade (ρ = .450, p < .001; ρ = .510, p < .001), and modified Fisher scale score (ρ = .357, p < .001; ρ = .330, p < .001). After controlling these public variables, WBC count (ρ = .300, p = .002) positively correlated with HU value. An early elevated WBC (odds ratio [OR] 1.449, 95% confidence interval [CI]: 1.183-1.774, p < .001) count and HU value (OR 1.304, 95%CI: 1.149-1.479, p < .001) could independently predict the occurrence of DCI. However, only these patients with both WBC count and HU value exceeding the cut-off points (OR 36.89, 95%CI: 5.606-242.78, p < .001) were strongly correlated with DCI. Compared with a single WBC count (AUC 0.811, 95%CI: 0.729-0.892, p < .001) or HU value (AUC 0.869, 95%CI: 0.803-0.936, p < .001), the combined WBC-HU (AUC 0.898, 95%CI: 0.839-0.957, p < .001) demonstrated a better ability to predict the occurrence of DCI. Inspiringly, the prediction performance of these indicators outperformed the established inflammatory markers. CONCLUSION: An early elevated WBC count and HU value could independently predict DCI occurrence between 4 and 30 days after aSAH. Furthermore, WBC count was positively correlated with HU value, and the combined WBC-HU demonstrated a superior prediction ability for DCI development compared with the individual indicator.

An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH. Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer-Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram. Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer-Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities. Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

SAA1 knockdown promotes the apoptosis of glioblastoma cells via downregulation of AKT signaling.

Serum amyloid A1 (SAA1) is an inflammatory associated high-density lipoprotein. And It is also considered as a predictor and prognostic marker of cancer risk. However, its role and mechanisms in glioblastoma (GBM) still unclear. In this study, we validate that SAA1 is up-regulated in GBM, and its high expression predicts poor prognosis. SAA1 knockdown promotes the apoptosis of GBM cell. Mechanistically, SAA1 knockdown can inhibit serine/threonine protein kinase B (AKT) phosphorylation, thereby regulating the expression of apoptosis-related proteins such as Bcl2 and Bax, leading to GBM cell death. Moreover, Gliomas with low SAA1 expression have increased sensitivity to Temozolomide (TMZ). Low SAA1 expression segregated glioma patients who were treated with Temozolomide (TMZ) or with high MGMT promoter methylation into survival groups in TCGA and CGGA dataset. Our study strongly suggested that SAA1 was a regulator of cells apoptosis and acted not only as a prognostic marker but also a novel biomarker of sensitivity of glioma to TMZ.

Notch intracellular domain regulates glioblastoma proliferation through the Notch1 signaling pathway.

Notch intracellular domain (NICD), also known as the activated form of Notch1 is closely associated with cell differentiation and tumor invasion. However, the role of NICD in glioblastoma (GBM) proliferation and the underlying regulatory mechanism remains unclear. The present study aimed to investigate the expression of NICD and Notch1 downstream gene HES5 in human GBM and normal brain samples and to further detect the effect of NICD on human GBM cell proliferation. For this purpose, western blotting and immunohistochemical staining were performed to analyze the expression of NICD in human GBM tissues, while western blotting and reverse-transcription quantitative PCR experiments were used to analyze the expression of Hes5 in human GBM tissues. A Flag-NICD vector was used to overexpress NICD in U87 cells and compound E and small interfering (si) Notch1 were used to downregulate NICD. Cellular proliferation curves were generated and BrdU assays performed to evaluate the proliferation of U87 cells. The results demonstrated that compared with normal brain tissues, the level of NICD protein in human GBM tissues was upregulated and the protein and mRNA levels of Hes5 were also upregulated in GBM tissues indicating that the Notch1 signaling pathway is activated in GBM. Overexpression of NICD promoted the proliferation of U87 cells in vitro while downregulation of NICD by treatment with compound E or siNotch1 suppressed the proliferation of U87 cells in vitro. In conclusion, NICD was upregulated in human GBM and NICD promoted GBM proliferation via the Notch1 signaling pathway. NICD may be a potential diagnostic marker and therapeutic target for GBM treatment.

Circulating Tumor Cells for Glioma.

Liquid biopsy has entered clinical applications for several cancers, including metastatic breast, prostate, and colorectal cancer for CTC enumeration and NSCLC for EGFR mutations in ctDNA, and has improved the individualized treatment of many cancers, but relatively little progress has been made in validating circulating biomarkers for brain malignancies. So far, data on circulating tumor cells about glioma are limited, the application of circulating tumor cells as biomarker for glioma patients has only just begun. This article reviews the research status and application prospects of circulating tumor cells in gliomas. Several detection methods and research results of circulating tumor cells about clinical research in gliomas are briefly discussed. The wide application prospect of circulating tumor cells in glioma deserves further exploration, and the research on more sensitive and convenient detection methods is necessary.

Immune Checkpoint Targeted Therapy in Glioma: Status and Hopes.

Glioma is the most malignant primary tumor of the central nervous system and is characterized by an extremely low overall survival. Recent breakthroughs in cancer therapy using immune checkpoint blockade have attracted significant attention. However, despite representing the most promising (immunotherapy) treatment for cancer, the clinical application of immune checkpoint blockade in glioma patients remains challenging due to the "cold phenotype" of glioma and multiple factors inducing resistance, both intrinsic and acquired. Therefore, comprehensive understanding of the tumor microenvironment and the unique immunological status of the brain will be critical for the application of glioma immunotherapy. More sensitive biomarkers to monitor the immune response, as well as combining multiple immunotherapy strategies, may accelerate clinical progress and enable development of effective and safe treatments for glioma patients.

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma.

Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.