Targeting CXCR4 to suppress glioma-initiating cells and chemoresistance in glioma

Cancer stem cell hypothesis 2.0 in glioblastoma: Where are we now and where are we going?

Over the past 2 decades, the cancer stem cell (CSC) hypothesis has provided insight into many malignant tumors, including glioblastoma (GBM). Cancer stem cells have been identified in patient-derived tumors and in some mouse models, allowing for a deeper understanding of cellular and molecular mechanisms underlying GBM growth and therapeutic resistance. The CSC hypothesis has been the cornerstone of cellular heterogeneity, providing a conceptual and technical framework to explain this longstanding phenotype in GBM. This hypothesis has evolved to fit recent insights into how cellular plasticity drives tumor growth to suggest that CSCs do not represent a distinct population but rather a cellular state with substantial plasticity that can be achieved by non-CSCs under specific conditions. This has further been reinforced by advances in genomics, including single-cell approaches, that have used the CSC hypothesis to identify multiple putative CSC states with unique properties, including specific developmental and metabolic programs. In this review, we provide a historical perspective on the CSC hypothesis and its recent evolution, with a focus on key functional phenotypes, and provide an update on the definition for its use in future genomic studies.

Recent Emerging Immunological Treatments for Primary Brain Tumors: Focus on Chemokine-Targeting Immunotherapies

Primary brain tumors are a leading cause of death worldwide and are characterized by extraordinary heterogeneity and high invasiveness. Current drug and radiotherapy therapies combined with surgical approaches tend to increase the five-year survival of affected patients, however, the overall mortality rate remains high, thus constituting a clinical challenge for which the discovery of new therapeutic strategies is needed. In this field, novel immunotherapy approaches, aimed at overcoming the complex immunosuppressive microenvironment, could represent a new method of treatment for central nervous system (CNS) tumors. Chemokines especially are a well-defined group of proteins that were so named due to their chemotactic properties of binding their receptors. Chemokines regulate the recruitment and/or tissue retention of immune cells as well as the mobilization of tumor cells that have undergone epithelial–mesenchymal transition, promoting tumor growth. On this basis, this review focuses on the function and involvement of chemokines and their receptors in primary brain tumors, specifically examining chemokine-targeting immunotherapies as one of the most promising strategies in neuro-oncology.

Mechanism of action of paclitaxel for treating glioblastoma based on single-cell RNA sequencing data and network pharmacology

Paclitaxel is an herbal active ingredient used in clinical practice that shows anti-tumor effects. However, its biological activity, mechanism, and cancer cell-killing effects remain unknown. Information on the chemical gene interactions of paclitaxel was obtained from the Comparative Toxicogenomics Database, SwishTargetPrediction, Binding DB, and TargetNet databases. Gene expression data were obtained from the GSE4290 dataset. Differential gene analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses were performed. Gene set enrichment analysis was performed to evaluate disease pathway activation; weighted gene co-expression network analysis with diff analysis was used to identify disease-associated genes, analyze differential genes, and identify drug targets via protein-protein interactions. The Molecular Complex Detection (MCODE) analysis of critical subgroup networks was conducted to identify essential genes affected by paclitaxel, assess crucial cluster gene expression differences in glioma versus standard samples, and perform receiver operator characteristic mapping. To evaluate the pharmacological targets and signaling pathways of paclitaxel in glioblastoma, the single-cell GSE148196 dataset was acquired from the Gene Expression Omnibus database and preprocessed using Seurat software. Based on the single-cell RNA-sequencing dataset, 24 cell clusters were identified, along with marker genes for the two different cell types in each cluster. Correlation analysis revealed that the mechanism of paclitaxel treatment involves effects on neurons. Paclitaxel may affect glioblastoma by improving glucose metabolism and processes involved in modulating immune function in the body.