The tumor microenvironment (TME) plays a crucial role in the progression and treatment response of glioblastoma (GBM). A comprehensive analysis of the brain TME revealed distinct immune cell alterations associated with different tumor types, including primary gliomas and brain metastases, highlighting the need for tailored therapeutic strategies (ref: Klemm doi.org/10.1016/j.cell.2020.05.007/). In a study investigating the immunotherapeutic potential of targeting CD133, a marker for cancer stem cells, three modalities were developed, demonstrating efficacy in reducing CD133+ cell populations in patient-derived GBM models (ref: Vora doi.org/10.1016/j.stem.2020.04.008/). Furthermore, the CheckMate 143 trial compared nivolumab and bevacizumab in recurrent GBM, revealing that nivolumab did not significantly improve overall survival compared to bevacizumab, indicating the complexity of immune evasion in GBM (ref: Reardon doi.org/10.1001/jamaoncol.2020.1024/). The study by Buccarelli et al. explored the deregulation of the DLK1-DIO3 region in GBM stem-like cells, suggesting a tumor suppressor role for lncRNA MEG3, which may influence the TME and tumor aggressiveness (ref: Buccarelli doi.org/10.1093/neuonc/). Additionally, alterations in the TME following radiation and temozolomide treatment were documented, emphasizing the dynamic nature of the TME and its impact on treatment outcomes (ref: Tamura doi.org/10.21037/atm.2020.03.11/).

Therapeutic strategies for glioblastoma are increasingly focusing on overcoming drug resistance mechanisms. The development of CD133-targeting immunotherapies has shown promise in addressing the challenges posed by cancer stem cells, which are implicated in treatment resistance (ref: Vora doi.org/10.1016/j.stem.2020.04.008/). In the CheckMate 143 trial, the comparison of nivolumab and bevacizumab highlighted the need for innovative approaches, as neither treatment demonstrated a significant survival advantage, underscoring the complexity of GBM treatment (ref: Reardon doi.org/10.1001/jamaoncol.2020.1024/). Temozolomide (TMZ), the standard of care, has been shown to antagonize oncolytic immunovirotherapy, complicating treatment regimens (ref: Saha doi.org/10.1136/jitc-2019-000345/). Moreover, the identification of lncRNA SOX2OT as a regulator of TMZ resistance through the Wnt5a/β-catenin signaling pathway presents a potential target for enhancing treatment efficacy (ref: Liu doi.org/10.1038/s41419-020-2540-y/). The addition of valganciclovir to standard therapy has also been associated with improved survival in glioblastoma patients, suggesting that viral targeting may be a viable adjunctive strategy (ref: Stragliotto doi.org/10.1158/1078-0432.CCR-20-0369/).

Molecular and genetic characterization of gliomas has revealed significant insights into their pathogenesis and potential therapeutic targets. The identification of FGFR3-TACC3 fusions in approximately 3% of gliomas highlights the need for targeted therapies, as these fusions are associated with distinct clinical and molecular profiles (ref: Di Stefano doi.org/10.1093/neuonc/). Additionally, genome-wide association studies have identified significant variants in genes such as D2HGDH and FAM20C, which are linked to IDH-mutated gliomas, providing potential biomarkers for prognosis and treatment stratification (ref: Eckel-Passow doi.org/10.1093/neuonc/). The discovery of extrachromosomal circular DNAs (eccDNAs) through ATAC-seq further emphasizes the complexity of glioma genetics, as these structures contribute to tumor heterogeneity and may serve as novel biomarkers (ref: Kumar doi.org/10.1126/sciadv.aba2489/).

The search for novel biomarkers and prognostic factors in glioblastoma is crucial for improving patient outcomes. The characterization of FGFR3-TACC3 fusions has been linked to specific clinical outcomes, suggesting their potential utility as prognostic markers (ref: Di Stefano doi.org/10.1093/neuonc/). Additionally, the study of the DLK1-DIO3 region in glioblastoma stem-like cells has uncovered the tumor suppressor role of lncRNA MEG3, which may influence tumor behavior and patient prognosis (ref: Buccarelli doi.org/10.1093/neuonc/). The implementation of deep learning techniques for automatic brain tumor segmentation has shown promise in enhancing diagnostic accuracy, potentially leading to better treatment planning (ref: Ben Naceur doi.org/10.1016/j.media.2020.101692/). Furthermore, the modulation of miR-4749-5p signaling by xanthohumol has been associated with increased temozolomide sensitivity, indicating a novel approach to enhance therapeutic efficacy (ref: Ho doi.org/10.1016/j.lfs.2020.117807/).

Nanotechnology is revolutionizing drug delivery systems for glioblastoma treatment, addressing challenges such as the blood-brain barrier (BBB) and drug specificity. Self-assembled DNA nanocages have been developed as biocompatible drug delivery vehicles, demonstrating effective BBB penetration and cellular uptake, which is essential for targeted brain cancer therapy (ref: Tam doi.org/10.1021/acsami.0c02957/). Hybrid magnetic nanovectors have shown potential in promoting selective glioblastoma cell death through a combination of lysosomal membrane permeabilization and chemotherapy, highlighting the synergistic effects of nanotechnology in cancer treatment (ref: Pucci doi.org/10.1021/acsami.0c05556/). The use of dendrimer-based nanomedicines has also been explored, with findings indicating that specific dendrimer sizes can enhance tumor targeting and reduce off-target effects (ref: Liaw doi.org/10.1002/btm2.10160/).

The effects of radiation and chemotherapy on glioblastoma are complex and multifaceted. The CheckMate 143 trial comparing nivolumab and bevacizumab provided insights into the efficacy of these treatments, revealing that neither significantly improved overall survival, which underscores the need for innovative therapeutic strategies (ref: Reardon doi.org/10.1001/jamaoncol.2020.1024/). Temozolomide's interaction with oncolytic immunovirotherapy has been shown to complicate treatment outcomes, as it may antagonize the effects of immunotherapeutics (ref: Saha doi.org/10.1136/jitc-2019-000345/). Furthermore, the study of dendrimer size effects on selective brain tumor targeting has demonstrated the potential of nanomedicine to enhance drug delivery and efficacy in glioblastoma treatment (ref: Liaw doi.org/10.1002/btm2.10160/). The modulation of autophagy and apoptosis pathways by natural compounds in conjunction with temozolomide also presents new avenues for enhancing therapeutic responses (ref: Sumorek-Wiadro doi.org/10.1016/j.ejphar.2020.173207/).

Cancer stem cells (CSCs) are pivotal in glioblastoma heterogeneity and treatment resistance. The development of CD133-targeting immunotherapies aims to specifically target these CSCs, which are known to contribute to tumor recurrence and therapeutic failure (ref: Vora doi.org/10.1016/j.stem.2020.04.008/). The CheckMate 143 trial's findings on nivolumab and bevacizumab further illustrate the challenges in treating heterogeneous tumor populations, as neither treatment significantly improved survival outcomes (ref: Reardon doi.org/10.1001/jamaoncol.2020.1024/). Additionally, the deregulation of the DLK1-DIO3 region in glioblastoma stem-like cells has been linked to tumor aggressiveness, suggesting that targeting these pathways may improve treatment efficacy (ref: Buccarelli doi.org/10.1093/neuonc/). The prevalence of human cytomegalovirus in glioblastoma has also been explored, with findings indicating that antiviral treatments may enhance survival when added to standard therapies (ref: Stragliotto doi.org/10.1158/1078-0432.CCR-20-0369/).

Advancements in imaging and diagnostic techniques are essential for improving glioblastoma management. The application of diffusion magnetic resonance imaging (MRI) phenotypes has been validated as a predictive tool for overall survival in patients with recurrent glioblastoma, demonstrating its potential as a non-invasive biomarker (ref: Patel doi.org/10.1093/neuros/). The validation of diffusion MRI phenotypes in predicting responses to bevacizumab further emphasizes the importance of imaging in treatment planning (ref: Schell doi.org/10.1093/neuonc/). Additionally, the development of deep learning-based models for automatic brain tumor segmentation has shown promise in enhancing diagnostic accuracy and efficiency, potentially leading to better patient outcomes (ref: Ben Naceur doi.org/10.1016/j.media.2020.101692/). The exploration of COX-2 inhibitors in glioblastoma cell lines also highlights the need for integrating imaging with therapeutic strategies to assess treatment responses (ref: Palumbo doi.org/10.1186/s12935-020-01250-7/).