Constitutive activation of the EGFR-STAT1 axis increases proliferation of meningioma tumor cells

Related mechanisms, current treatments, and new perspectives in meningioma

Meningiomas are non-glial tumors that are the most common primary brain tumors in adults. Although meningioma can possibly be cured with surgical excision, variations in atypical/anaplastic meningioma have a high recurrence rate and a poor prognosis. As a result, it is critical to develop novel therapeutic options for high-grade meningiomas. This review highlights the current histology of meningiomas, prevalent genetic and molecular changes, and the most extensively researched signaling pathways and therapies in meningiomas. It also reviews current clinical studies and novel meningioma treatments, including immunotherapy, microRNAs, cancer stem cell methods, and targeted interventions within the glycolysis pathway. Through the examination of the complex landscape of meningioma biology and the highlighting of promising therapeutic pathways, this review opens the way for future research efforts aimed at improving patient outcomes in this prevalent intracranial tumor entity.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Preclinical Models of Meningioma

The management of clinically aggressive meningiomas remains challenging due to limited treatment options aside from surgical removal and radiotherapy. High recurrence rates and lack of effective systemic therapies contribute to the unfavorable prognosis of these patients. Accurate in vitro and in vivo models are critical for understanding meningioma pathogenesis and to identify and test novel therapeutics. In this chapter, we review cell models, genetically engineered mouse models, and xenograft mouse models, with special emphasis on the field of application. Finally, promising preclinical 3D models such as organotypic tumor slices and patient-derived tumor organoids are discussed.

Receptor Tyrosine Kinases as Candidate Prognostic Biomarkers and Therapeutic Targets in Meningioma

Meningioma (MGM) is the most common type of intracranial tumor in adults. The validation of novel prognostic biomarkers to better inform tumor stratification and clinical prognosis is urgently needed. Many molecular and cellular alterations have been described in MGM tumors over the past few years, providing a rational basis for the identification of biomarkers and therapeutic targets. The role of receptor tyrosine kinases (RTKs) as oncogenes, including those of the ErbB family of receptors, has been well established in several cancer types. Here, we review histological, molecular, and clinical evidence suggesting that RTKs, including the epidermal growth factor receptor (EGFR, ErbB1), as well as other members of the ErbB family, may be useful as biomarkers and therapeutic targets in MGM.

Effects of Huangqin Decoction on ulcerative colitis by targeting estrogen receptor alpha and ameliorating endothelial dysfunction based on system pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Huangqin Decoction (HQD), a traditional Chinese medicinal (TCM) formula chronicled in Shang Han Lun, has been used to treat gastrointestinal diseases for nearly 1800 years.

OBJECTIVE

To investigate the effects and underlying mechanisms of HQD on ulcerative colitis (UC).

METHODS

The bioactive compounds in HQD were obtained from the traditional Chinese medicine systems pharmacology database. Then, the HQD and UC-related targets were analyzed by establishing HQD-Compounds-Targets (H-C-T) and protein-protein interaction (PPI) networks. Enrichment analysis was used for further study. The candidate targets for the effects of HQD on UC were validated using a dextran sulfate sodium-induced UC mouse experiment.

RESULTS

The results showed that 51 key targets were gained by matching 284 HQD-related targets and 837 UC-related targets. Combined with H-C-T and PPI network analyses, the key targets were divided into endothelial growth, inflammation and signal transcription-related targets. Further experimental validation showed that HQD targeted estrogen receptor alpha (ESR1) and endothelial growth factor receptors to relieve endothelial dysfunction, thereby improving intestinal barrier function. The expression of inflammatory cytokines and signal transducers was suppressed by HQD treatment and inflammation was inhibited.

CONCLUSIONS

HQD may acts on UC via the regulation of targets and pathways related to improving the intestinal mucosal barrier and ameliorating endothelial dysfunction. Additionally, ERS1 may be a new target to explore the mechanisms of UC.

Fibulin-2: A Novel Biomarker for Differentiating Grade II from Grade I Meningiomas

There is an unmet need for the identification of biomarkers to aid in the diagnosis, clinical management, prognosis and follow-up of meningiomas. There is currently no consensus on the optimum management of WHO grade II meningiomas. In this study, we identified the calcium binding extracellular matrix glycoprotein, Fibulin-2, via mass-spectrometry-based proteomics, assessed its expression in grade I and II meningiomas and explored its potential as a grade II biomarker. A total of 87 grade I and 91 grade II different meningioma cells, tissue and plasma samples were used for the various experimental techniques employed to assess Fibulin-2 expression. The tumours were reviewed and classified according to the 2016 edition of the Classification of the Tumours of the central nervous system (CNS). Mass spectrometry proteomic analysis identified Fibulin-2 as a differentially expressed protein between grade I and II meningioma cell cultures. Fibulin-2 levels were further evaluated in meningioma cells using Western blotting and Real-time Quantitative Polymerase Chain Reaction (RT-qPCR); in meningioma tissues via immunohistochemistry and RT-qPCR; and in plasma via Enzyme-Linked Immunosorbent Assay (ELISA). Proteomic analyses (p < 0.05), Western blotting (p < 0.05) and RT-qPCR (p < 0.01) confirmed significantly higher Fibulin-2 (FBLN2) expression levels in grade II meningiomas compared to grade I. Fibulin-2 blood plasma levels were also significantly higher in grade II meningioma patients compared to grade I patients. This study suggests that elevated Fibulin-2 might be a novel grade II meningioma biomarker, when differentiating them from the grade I tumours. The trend of Fibulin-2 expression observed in plasma may serve as a useful non-invasive biomarker.

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks

Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.

GATA-4, a potential novel therapeutic target for high-grade meningioma, regulates miR-497, a potential novel circulating biomarker for high-grade meningioma

BACKGROUND

Meningiomas are the most common primary intracranial tumours. They are classified as grade I, II, and III based on their histopathological features. While most meningiomas can be managed by surgery alone, adjuvant treatment may be required in case of recurrent, or high-grade tumours. To date, chemotherapy has proven ineffective in meningioma patients, reinforcing the need for novel therapeutic targets and molecular biomarkers.

METHODS

Using meningioma tissues and in vitro models, we investigated microRNA levels in meningioma samples of different grades, as well as their regulation. Based on this, we also investigated candidate miRNAs expression in serum, and their potential as biomarkers.

FINDINGS

We found that miR-497~195 cluster expression in meningioma decreases with increasing malignancy grade, and that Cyclin D1 overexpression correlated with downregulation of the miR-497~195 cluster. GATA binding protein 4, a transcription factor upregulated in malignant meningioma, caused increased cell viability by controlling the expression of the miR-497~195 cluster, resulting in increased Cyclin D1 expression. Accordingly, GATA-4 inhibition via the small-molecule inhibitor NSC140905 restored miR-497~195 cluster expression, resulting in decreased viability, and Cyclin D1 downregulation. Analysis of the miR-497~195 cluster expression in serum exosomes derived from high-grade meningioma patients, revealed lower levels of miR-497 compared to those of benign origin.

INTERPRETATION

Our data suggest that GATA-4 could be a novel potential therapeutic target, and miR-497 could serve as a potential non-invasive biomarker for high-grade meningioma.