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.

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001). CONCLUSION: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

Deep learning-based quantification of total bleeding volume and its association with complications, disability, and death in patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) remain uncertain. Here, the authors apply their recently developed automated deep learning technique to quantify total bleeding volume (TBV) in patients with aSAH and investigate associations between quantitative TBV and secondary complications, adverse long-term functional outcomes, and death. METHODS: Electronic health record data were extracted for adult patients admitted to a single institution within 72 hours of aSAH onset between 2018 and 2021. An automatic deep learning model was used to fully segment and quantify TBV on admission noncontrast head CT images. Patients were subgrouped by TBV quartile, and multivariable logistic regression, restricted cubic splines, and subgroup analysis were used to explore the relationships between TBV and each clinical outcome. RESULTS: A total of 819 patients were included in the study. Sixty-six (8.1%) patients developed hydrocephalus, while 43 (5.3%) experienced rebleeding, 141 (17.2%) had delayed cerebral ischemia, 88 (10.7%) died in the 12 months after discharge, and 208 (25.7%) had a modified Rankin Scale score ≥ 3 12 months after discharge. On multivariable analysis, patients in the highest TBV quartile (> 37.94 ml) had an increased risk of hydrocephalus (adjusted OR [aOR] 4.38, 95% CI 1.61-11.87; p = 0.004), rebleeding (aOR 3.26, 95% CI 1.03-10.33; p = 0.045), death (aOR 6.92, 95% CI 1.89-25.37; p = 0.004), and 12-month disability (aOR 3.30, 95% CI 1.62-6.72; p = 0.001) compared with the lowest TBV quantile (< 8.34 ml). The risks of hydrocephalus (nonlinear, p = 0.025), rebleeding, death, and disability (linear, p > 0.05) were positively associated with TBV by restricted cubic splines. In subgroup analysis, TBV had a stronger effect on 12-month outcome in female than male patients (p for interaction = 0.0499) and on rebleeding prevalence in patients with endovascular coiling than those with surgical clipping (p for interaction = 0.008). CONCLUSIONS: Elevated TBV is associated with a greater risk of hydrocephalus, rebleeding, death, and poor prognosis.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). METHODS: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. RESULTS: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. CONCLUSIONS: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

Claudin-3 facilitates the progression and mediates the tumorigenic effects of TGF-ß in glioblastoma multiforme.

Glioblastoma multiforme (GBM) is a significantly malignant and lethal brain tumor with an average survival time of less than 12 months. Several researches had shown that Claudin-3 (CLDN3) is overexpressed in various cancers and might be important in their growth and spread. In this study, we used qRT-PCR, western blotting, immunohistochemistry, and immunofluorescence staining assays to investigate the expression levels of various proteins. To explore the proliferation abilities of GBM cells, we conducted the CCK-8 and EdU-DNA formation assays. Wound healing and transwell assays were used to investigate the capacities of invasion and migration of GBM cells. Additionally, we constructed an intracranial xenograft model of GBM to study the in vivo role of CLDN3. Our study devoted to investigate the function of CLDN3 in the pathogenesis and progression of GBM. Our study revealed that CLDN3 was upregulated in GBM and could stimulate tumor cell growth and epithelial-mesenchymal transition (EMT) in both laboratory and animal models. We also discovered that CLDN3 expression could be triggered by transforming growth factor-ß (TGF-ß) and reduced by specific inhibitors of the TGF-ß signaling pathway, such as ITD-1. Further analysis revealed that increased CLDN3 levels enhanced TGF-ß-induced growth and EMT in GBM cells, while reducing CLDN3 levels weakened these effects. Our study demonstrated the function of CLDN3 in facilitating GBM growth and metastasis and indicated its involvement in the tumorigenic effects of TGF-ß. Developing specific inhibitors of CLDN3 might, therefore, represent a promising new approach for treating this devastating disease.

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

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.

Effect of Surgical Clipping versus Endovascular Coiling on the Incidence of Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage: A Multicenter Observational Cohort Study with Propensity Score Matching.

OBJECTIVE: The effect of surgical clipping (SC) and endovascular coiling (EC) on the incidence of delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH) has always been a controversial topic. Hence, it is necessary to reanalyze the effects of the 2 surgical methods on DCI, which determines the choice of the most favorable method for patients who are suitable for both surgical modalities. METHODS: A multicenter retrospective observational cohort study was performed to evaluate all consecutive patients with aSAH admitted to 5 medical centers in China between April 2019 and June 2021. Univariable and multivariable analyses were used to confirm risk factors of DCI after aSAH. A 1:1 propensity score matching model was generated in the EC and SC groups to reduce the influence of all confounding factors on DCI. RESULTS: A total of 412 patients were included, and 115 patients (27.9%) developed DCI. After propensity score matching for controlling demographic information, past medical history, admission clinical status, aneurysm characteristics, and inflammatory factors associated with DCI, 133 patients with SC and 133 patients with EC treatment were matched. The results of the matched cohorts indicate a significantly lower incidence of DCI when patients received EC than SC (31.9% vs. 20%; adjusted odds ratio, 1.87; 95% confidence interval, 1.08-3.29; P = 0.027). CONCLUSIONS: The study found that the patients who received SC treatment had a higher incidence of DCI than did those who received EC and suggested that ruptured intracerebral aneurysm is preferentially coiled rather than clipped if the aneurysm is suitable for both surgical modalities.

Pioglitazone in spontaneous subarachnoid hemorrhage: study protocol of a multicenter, double-blind, randomized trial (PSSH).

Introduction: Spontaneous subarachnoid hemorrhage (SAH), is a disorder that may be fatal and is primarily caused by a ruptured brain aneurysm. Despite significant leaps forward in the methods to produce aneurysms, the long-term outcomes did not much improve. Pioglitazone is a medication that has been authorized by the FDA as an agonist for the peroxisome proliferator-activated receptor-gamma (PPARγ). Pioglitazone or PPARγ has neuroprotective benefits in animal experiments both during and after traumatic brain injury (TBI) and SAH. Nevertheless, the treatment impact of Pioglitazone on humans is still unknown at this time. As a result, we will conduct a randomized, double-blind, placebo-controlled trial to explore the impact of pioglitazone on SAH. Methods/Design: This trial will recruit 400 patients with SAH from four Chinese hospitals. These patients will be equally and randomly assigned to Pioglitazone and placebo control groups for up to 30 days. Scores on the modified Rankin scale (mRS) are the primary outcomes. The secondary outcomes are a 30-day all-cause mortality rate, 6 months of Montreal cognitive assessment (Mo-CA), delayed cerebral ischemia, the requirement for intensive care, the incidence of sepsis, etc. All serious adverse events (SAEs) were recorded during the hospital. Every primary and safety analysis was conducted based on the intention-to-treat technique. The participants were given either a matching placebo or 15 mg of pioglitazone, with dose titrated to a target of 45 mg daily. Data on the therapeutic use of pioglitazone after SAH will be provided as a consequence of the findings of this experiment. In addition, this pilot trial is the first to prospectively investigate the effectiveness and safety of pioglitazone in patients with SAH. Ethics and dissemination: Ethics approval was obtained from the Medical Ethics Committee of 904th Hospital of Joint Logistic Support Force of PLA (Wuxi Taihu Hospital, approval No. 20220701). The findings of the trial will be presented at conferences, discussed in relevant patient groups, and published in peer-reviewed journals. Clinical Trial Registration: clinicaltrials.gov, identifier ChiCTR2200062954.

Identification of Immunogenic Cell Death-Related Signature for Glioma to Predict Survival and Response to Immunotherapy.

Immunogenic cell death (ICD) is a type of regulated cell death (RCD) and is correlated with the progression, prognosis, and therapy of tumors, including glioma. Numerous studies have shown that the immunotherapeutic and chemotherapeutic agents of glioma might induce ICD. However, studies on the comprehensive analysis of the role of ICD-related genes and their correlations with overall survival (OS) in glioma are lacking. The genetic, transcriptional, and clinical data of 1896 glioma samples were acquired from five distinct databases and analyzed in terms of genes and transcription levels. The method of consensus unsupervised clustering divided the patients into two disparate molecular clusters: A and B. All of the patients were randomly divided into training and testing groups. Employing the training group data, 14 ICD-related genes were filtered out to develop a risk-score model. The correlations between our risk groups and prognosis, cells in the tumor microenvironment (TME) and immune cells infiltration, chemosensitivity and cancer stem cell (CSC) index were assessed. A highly precise nomogram model was constructed to enhance and optimize the clinical application of the risk score. The results demonstrated that the risk score could independently predict the OS rate and the immunotherapeutic response of glioma patients. This study analyzed the ICD-related genes in glioma and evaluated their role in the OS, clinicopathological characteristics, TME and immune cell infiltration of glioma. Our results may help in assessing the OS of glioma and developing better immunotherapeutic strategies.

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.

BCAS3 accelerates glioblastoma tumorigenesis by restraining the P53/GADD45α signaling pathway.

As in many other cancers, highly malignant proliferation and disordered cell division play irreplaceable roles in the exceedingly easy recurrence and complex progression of glioblastoma multiforme (GBM); however, mechanistic studies of the numerous regulators involved in this process are still insufficiently thorough. The role of BCAS3 has been studied in other cancers, but its role in GBM is unclear. Here, our goal was to investigate the expression pattern of BCAS3 in GBM and its potential mechanism of action. Using TCGA database and human GBM samples, we found that BCAS3 expression was up-regulated in GBM, and its high expression predicted poor prognosis. To further investigate the relationship between BCAS3 and GBM characteristics, we up-regulated and down-regulated BCAS3 expression in GBM to detect its effect on cell proliferation and cell cycle. At the same time, we established U87 cells stably overexpressing BCAS3 and generated an intracranial xenograft model to investigate the Potential role of BCAS3 in vivo. Finally, based on in vitro cell experiments and in vivo GBM xenograft models, we observed that BCAS3 significantly regulates GBM cell proliferation and cell cycle and that this regulation is associated with p53/GADD45α Signaling pathway. Taken together, our findings suggest that BCAS3 is inextricably linked to the progression of GBM and that targeting BCAS3 may have therapeutic effects in GBM patients.

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.

TGF-ß induces GBM mesenchymal transition through upregulation of CLDN4 and nuclear translocation to activate TNF-α/NF-κB signal pathway.

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor. The unregulated expression of Claudin-4 (CLDN4) plays an important role in tumor progression. However, the biological role of CLDN4 in GBM is still unknown. This study aimed to determine whether CLDN4 mediates glioma malignant progression, if so, it would further explore the molecular mechanisms of carcinogenesis. Our results revealed that CLDN4 was significantly upregulated in glioma specimens and cells. The inhibition of CLND4 expression could inhibit mesenchymal transformation, cell invasion, cell migration and tumor growth in vitro and in vivo. Moreover, combined with in vitro analysis, we found that CLDN4 can modulate tumor necrosis factor-α (TNF-α) signal pathway. Meanwhile, we also validated that the transforming growth factor-ß (TGF-ß) signal pathway can upregulate the expression of CLDN4, and promote the invasion ability of GBM cells. Conversely, TGF-ß signal pathway inhibitor ITD-1 can downregulate the expression of CLDN4, and inhibit the invasion ability of GBM cells. Furthermore, we found that TGF-ß can promote the nuclear translocation of CLDN4. In summary, our findings indicated that the TGF-ß/CLDN4/TNF-α/NF-κB signal axis plays a key role in the biological progression of glioma. Disrupting the function of this signal axis may represent a new treatment strategy for patients with GBM.

TMBIM1 promotes proliferation and attenuates apoptosis in glioblastoma cells by targeting the p38 MAPK signalling pathway.

Glioblastoma multiforme (GBM) is the most common and most fatal primary malignant brain tumour in adults. The average survival time of patients after diagnosis is only 12-15 months. And its characteristics of excessive proliferation and apoptosis evasion play a crucial role in the poor prognosis of patients. Therefore, it is worth investigating the molecular mechanism of GBM to find an effective therapeutic target to overcome the dilemma. In the current study, Transmembrane BAX inhibitor motif containing 1 (TMBIM1) was highly expressed in GBM tissues and high TMBIM1 expression in GBM cell lines (U87 and U251) could promote cell proliferation and inhibit cell cycle arrest. In addition, TMBIM1 could significantly attenuate GBM cell apoptosis and decrease the sensitivity of GBM cells to temozolomide (TMZ). In terms of the molecular mechanism, we revealed that TMBIM1 interferes with the p38/MAPK pathway by inhibiting p38 phosphorylation to promote cell proliferation and attenuate cell apoptosis. In vivo experiments showed that the survival time of mice in TMBIM1 knockdown group was significantly prolonged. Our discovery provided an important basis for future intensive molecular mechanism research in GBM and presented a potential target for the treatment of GBM.

MICAL2 Promotes Proliferation and Migration of Glioblastoma Cells Through TGF-ß/p-Smad2/EMT-Like Signaling Pathway.

Recent studies showed that molecule interacting with CasL2 (MICAL2) could be a novel tumor growth factor, and it is closely associated with tumor growth and invasion. However, the role it plays in glioblastoma (GBM) and its potential mechanisms are currently unknown. Our study is designed to identify the effect of MICAL2 on GBM cells and the potential mechanisms behind it. Here, we found that MICAL2 interacts with TGF receptor-type I (TGFRI) and promotes the proliferation and migration of glioblastoma through the TGF-ß/p-Smad2/EMT-like signaling pathway. MICAL2-knockdown inhibited the proliferation of glioblastoma cells, which was related to cell cycle arrest and downregulation of DNA replication. The invasion abilities of U87 and U251 cells were reduced after the knockdown of MICAL2. MICAL2 promoted the growth of GBM in nude mice. High MICAL2 predicts poor outcome of GBM patients. MICAL2 could be identified as a novel promising therapeutic target for human GBM.

Atorvastatin ameliorates early brain injury after subarachnoid hemorrhage via inhibition of pyroptosis and neuroinflammation.

Subarachnoid hemorrhage (SAH) is a subtype of stroke with high mortality and morbidity due to the lack of effective therapy. Atorvastatin has been reported to alleviate early brain injury (EBI) following subarachnoid hemorrhage (SAH) via reducing reactive oxygen species, antiapoptosis, regulated autophagy, and neuroinflammation. Which was the related to the pyroptosis? Pyroptosis can be defined as a highly specific inflammatory programmed cell death, distinct from classical apoptosis and necrosis. However, the precise role of pyroptosis in atorvastatin-mediated neuroprotection following SAH has not been confirmed. The present study aimed to investigate the neuroprotection and potential molecular mechanisms of atorvastatin in the SAH-induced EBI via regulating neural pyroptosis using the filament perforation model of SAH in male C57BL/6 mice, and the hemin-induced neuron damage model in HT-22. Atorvastatin or vehicle was administrated 2 h after SAH and hemin-induced neuron damage. The mortality, neurological score, brain water content, and neuronal death were evaluated. The results show that the atorvastatin treatment markedly increased survival rate, neurological score, greater survival of neurons, downregulated the protein expression of NLRP1, cleaved caspase-1, interleukin-1ß (IL-1ß), and IL-18, which indicated that atorvastatin-inhibited pyroptosis and neuroinflammation, ameliorated neuron death in vivo/vitro subjected to SAH. Taken together, this study demonstrates that atorvastatin improved the neurological outcome in rats and reduced the neuron death by against neural pyroptosis and neuroinflammation.

Exosomes derived from microRNA-512-5p-transfected bone mesenchymal stem cells inhibit glioblastoma progression by targeting JAG1.

In this study, we demonstrate that bone mesenchymal stem cell (BMSC)-derived exosomes alter tumor phenotypes by delivering miR-512-5p. miR-512-5p was downregulated in glioblastoma tissues and cells, and Jagged 1 (JAG1) was the target gene of miR-512-5p. We clarified the expression patterns of miR-512-5p and JAG1 along with their interactions in glioblastoma. Additionally, we observed that BMSC-derived exosomes could contain and transport miR-512-5p to glioblastoma cells in vitro. BMSC-derived exosomal miR-512-5p inhibited glioblastoma cell proliferation and induced cell cycle arrest by suppressing JAG1 expression. In vivo assays validated the in vitro findings, with BMSC-exosomal miR-512-5p inhibiting glioblastoma growth and prolonging survival in mice. These results suggest that BMSC-derived exosomes transport miR-512-5p into glioblastoma and slow its progression by targeting JAG1. This study reveals a new molecular mechanism for glioblastoma treatment and validates miRNA packaging into exosomes for glioblastoma cell communication.

FCGBP Is a Prognostic Biomarker and Associated With Immune Infiltration in Glioma.

The Fc Fragment of IgG Binding Protein (FCGBP) has been proven to participate in intestinal tumor immunity. However, the biological role of FCGBP has remained unclear in glioma. The differential expression of FCGBP was explored by Oncomine and GEPIA databases. The effect of FCGBP on prognosis was analyzed via Kaplan-Meier plotter and GEPIA. The Tumor Immune Estimation Resource (TIMER) tool was used to determine the correlations of FCGBP expression with tumor immune infiltration. Firstly, FCGBP was highly expressed in glioma and correlated with a worse prognosis. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed that the differentially expressed genes (DEGs) and co-expression genes of FCGBP were mainly involved in the immune response. Furthermore, FCGBP expression was positively associated with multiple immune cells infiltrates as well as the expression levels of multiple immune markers in glioma. FCGBP co-expression networks mostly participated in the regulation of immune response. Finally, immunohistochemistry (IHC) assays were conducted to explore the expression of FCGBP, PD-L1, CCL2 and CD8 in glioma and correlations between them. We found that PDL1 and FCGBP were synchronously upregulated in glioma tissues. These findings revealed a new mechanism by which FCGBP participates in the immune tolerance of glioma, and implied the potential of FCGBP as a therapeutic target or predictive marker for patients.

MicroRNA­576­3p inhibits the migration and proangiogenic abilities of hypoxia­treated glioma cells through hypoxia­inducible factor­1α.

The most common and aggressive type of brain cancer in adults is glioblastoma multiforme (GBM), and hypoxia is a common feature of glioblastoma. As the histological features of glioma include capillary endothelial cell proliferation, they are highly prone to invading the surrounding normal brain tissue, which is often one of the reasons for the failure of treatment. However, the mechanisms involved in this process are not fully understood. MicroRNAs (miRs) are a class of non­coding RNA that are able to inhibit the malignant progression of tumor cells through the regulation of downstream genes. In the present study, the low expression of miR­576­3p was detected in glioma samples and hypoxia­treated glioma cells using a reverse transcription­quantitative polymerase chain reaction. The present study focused on the effects of miR­576­3p on hypoxia­induced glioma. The results of the functional experiments revealed that the overexpression of miR­576­3p significantly inhibited the migration and pro­angiogenic abilities of the glioma cells under hypoxic conditions (P<0.05) compared with in the lentivirus­miR­negative control group. Furthermore, luciferase reporter gene assays were used to validate the hypothesis that miR­576­3p interacts with the 3'­untranslated region of hypoxia­inducible factor­1α (HIF­1α) and induces a reduction in the protein levels of matrix metalloproteinase­2 and vascular endothelial growth factor. Rescue experiments demonstrated that the restoration of HIF­1α expression attenuated the effect of miR­576­3p on the migration and proangiogenic abilities of glioma cells. In conclusion, the present study confirms that miR­576­3p is a novel GBM inhibitor and its inhibition of the migration and proangiogenic capacity of hypoxia­induced glioma cells is mediated by HIF­1α.