The Oncogenesis of Glial Cells in Diffuse Gliomas and Clinical Opportunities

Knockdown of CENPF induces cell cycle arrest and inhibits epithelial‑mesenchymal transition progression in glioma

Gliomas are among the most common malignant tumors of the central nervous system. Despite surgical resection followed by postoperative radiotherapy and chemotherapy, their prognosis remains unfavorable. The present study aimed to assess new mechanisms and explore promising prognostic biomarkers for patients with glioma using comprehensive bioinformatics analysis and in vitro and in vivo assays. Overlapping differentially expressed genes were screened from The Cancer Genome Atlas, GSE111260 and GSE16011 samples for protein-protein interaction networks, a risk score model, gene mutation analysis and a nomogram to identify the prognostic hub genes. Subsequently, an immunoassay was performed to determine key genes. Functional and animal assays were then performed to assess the tumorigenesis of the key genes in glioma. Using bioinformatics analysis, centromere protein F (CENPF), kinesin superfamily member 20A, kinesin superfamily protein 4A and marker of proliferation Ki-67 were identified as potential prognostic biomarkers for patients with glioma. Furthermore, CENPF knockdown was demonstrated to suppress the proliferation and metastasis of glioma cells, and induce G2 arrest in the cell cycle. Moreover, CENPF knockdown was revealed to decrease Vimentin and increase E-cadherin levels in glioma cells, and significantly reduce the size and mass of tumors in vivo. Overall, the present study identified new clinical biomarkers and revealed that CENPF may promote glioma progression by regulating the epithelial-mesenchymal transition pathway. By elucidating the complexities of glioma and identifying prognostic biomarkers, the present research enables further improvement of patient outcomes and the advancement of precision medicine for this disease.

Recent Developments in Glioblastoma-On-A-Chip for Advanced Drug Screening Applications

Glioblastoma (GBM) is an aggressive form of cancer, comprising ≈80% of malignant brain tumors. However, there are no effective treatments for GBM due to its heterogeneity and the presence of the blood-brain barrier (BBB), which restricts the delivery of therapeutics to the brain. Despite in vitro models contributing to the understanding of GBM, conventional 2D models oversimplify the complex tumor microenvironment. Organ-on-a-chip (OoC) models have emerged as promising platforms that recapitulate human tissue physiology, enabling disease modeling, drug screening, and personalized medicine. There is a sudden increase in GBM-on-a-chip models that can significantly advance the knowledge of GBM etiology and revolutionize drug development by reducing animal testing and enhancing translation to the clinic. In this review, an overview of GBM-on-a-chip models and their applications is reported for drug screening and discussed current challenges and potential future directions for GBM-on-a-chip models.

Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy.

Glioma Stem Cells-Features for New Therapy Design

On a molecular level, glioma is very diverse and presents a whole spectrum of specific genetic and epigenetic alterations. The tumors are unfortunately resistant to available therapies and the survival rate is low. The explanation of significant intra- and inter-tumor heterogeneity and the infiltrative capability of gliomas, as well as its resistance to therapy, recurrence and aggressive behavior, lies in a small subset of tumor-initiating cells that behave like stem cells and are known as glioma cancer stem cells (GCSCs). They are responsible for tumor plasticity and are influenced by genetic drivers. Additionally, GCSCs also display greater migratory abilities. A great effort is under way in order to find ways to eliminate or neutralize GCSCs. Many different treatment strategies are currently being explored, including modulation of the tumor microenvironment, posttranscriptional regulation, epigenetic modulation and immunotherapy.

Small Molecule Tyrosine Kinase Inhibitors (TKIs) for Glioblastoma Treatment

In the last decade, many small molecules, usually characterized by heterocyclic scaffolds, have been designed and synthesized as tyrosine kinase inhibitors (TKIs). Among them, several compounds have been tested at preclinical and clinical levels to treat glioblastoma multiforme (GBM). GBM is the most common and aggressive type of cancer originating in the brain and has an unfavorable prognosis, with a median survival of 15-16 months and a 5-year survival rate of 5%. Despite recent advances in treating GBM, it represents an incurable disease associated with treatment resistance and high recurrence rates. For these reasons, there is an urgent need for the development of new pharmacological agents to fight this malignancy. In this review, we reported the compounds published in the last five years, which showed promising activity in GBM preclinical models acting as TKIs. We grouped the compounds based on the targeted kinase: first, we reported receptor TKIs and then, cytoplasmic and peculiar kinase inhibitors. For each small molecule, we included the chemical structure, and we schematized the interaction with the target for some representative compounds with the aim of elucidating the mechanism of action. Finally, we cited the most relevant clinical trials.