The tumor microenvironment (TME) plays a critical role in the progression and treatment response of gliomas. A study by Cakmak et al. highlighted the presence of tertiary lymphoid structures (TLSs) in 15% of a cohort of 642 gliomas, which were associated with a remodeled perivascular space and changes in extracellular matrix components, suggesting a potential avenue for enhancing immune responses in these typically 'cold' tumors (ref: Cakmak doi.org/10.1016/j.immuni.2025.09.018/). In another investigation, Savani et al. utilized stable isotope tracing in human plasma-like media to explore glioma metabolism, revealing that traditional in vitro models often fail to replicate the nutrient conditions and immune interactions present in human gliomas, thereby limiting our understanding of glioma metabolism and immune modulation (ref: Savani doi.org/10.1093/neuonc/). Furthermore, Ghosh et al. employed spatial transcriptomics and immunofluorescent staining to identify tumor-associated astrocyte subpopulations and their interactions within the TME, uncovering multiple functional phenotypes that contribute to glioma heterogeneity (ref: Ghosh doi.org/10.1371/journal.pbio.3002893/). These findings collectively underscore the complexity of the glioma TME and its implications for therapeutic strategies, particularly in the context of immune evasion and metabolic reprogramming driven by factors such as IDH1 mutations, as discussed by Nguyen et al. (ref: Nguyen doi.org/10.1093/oncolo/).

Metabolic pathways and molecular characteristics are pivotal in understanding glioma biology and treatment resistance. Chen et al. introduced a novel inositol-related gene score (INScore) to quantify inositol metabolism in lower-grade gliomas (LGGs), linking it to clinical features and tumor aggressiveness (ref: Chen doi.org/10.1097/JS9.0000000000003604/). This integrative analysis utilized bulk, single-cell, and spatial transcriptomics to elucidate the functional significance of inositol metabolism, highlighting its role in immune evasion. Additionally, Huang et al. explored the interplay between various programmed cell death (PCD) modalities and the immune microenvironment, developing a prognostic signature that could guide therapeutic decisions in glioma management (ref: Huang doi.org/10.3389/fcell.2025.1677290/). The study by Chen et al. further revealed that COL22A1 mediates the malignant progression of mesenchymal-like cells in glioblastoma, emphasizing the importance of spatial heterogeneity in tumor behavior (ref: Chen doi.org/10.1007/s12672-025-03559-z/). These studies collectively illustrate the intricate metabolic networks and molecular pathways that underpin glioma pathophysiology, offering potential targets for therapeutic intervention.

Pediatric low-grade gliomas (PLGG) represent a unique challenge due to their variable clinical behavior and potential for dissemination. Levine et al. conducted a comprehensive study involving 269 patients with disseminated PLGG, providing detailed clinical and molecular characterization, including DNA sequencing and methylome profiling (ref: Levine doi.org/10.1093/neuonc/). This work sheds light on the factors associated with tumor dissemination, which remains poorly understood despite the generally favorable prognosis of PLGG. In the context of high-grade gliomas (HGGs), Tripathy et al. developed a tumor organoid model to facilitate precision oncology approaches, addressing the significant heterogeneity observed in IDH-wildtype and IDH-mutant subtypes (ref: Tripathy doi.org/10.3390/bioengineering12101121/). This model aims to enhance treatment selection and improve outcomes for patients with HGGs, highlighting the need for tailored therapeutic strategies that consider the diverse genetic and microenvironmental landscapes of pediatric gliomas.

The development of effective therapeutic strategies for gliomas is hindered by the complex interplay of tumor biology and the immune microenvironment. Nguyen et al. investigated the combination of ivosidenib and nivolumab in tumors with IDH1 mutations, revealing that mutant IDH1 contributes to immune exclusion and metabolic reprogramming within the TME, which can be reversed by IDH inhibition (ref: Nguyen doi.org/10.1093/oncolo/). Keretsu et al. characterized MAIT cells in glioblastoma, uncovering their potential immunosuppressive role through interactions with neutrophils, which may further complicate treatment responses (ref: Keretsu doi.org/10.1093/noajnl/). Additionally, Nie et al. identified LGMN as a prognostic and immunoregulatory biomarker in glioma, linking its expression to immune infiltration and clinical outcomes (ref: Nie doi.org/10.1002/iub.70055/). These studies emphasize the necessity of understanding resistance mechanisms and immune dynamics to develop more effective therapeutic interventions for gliomas.

Spatial transcriptomics has emerged as a powerful tool for elucidating the complex cellular interactions within gliomas. Ghosh et al. utilized this technology to identify tumor-associated astrocyte subpopulations and their interactions within the TME, revealing significant heterogeneity and functional diversity that may influence glioma progression (ref: Ghosh doi.org/10.1371/journal.pbio.3002893/). Similarly, Yoshimoto et al. integrated spatial and single-cell transcriptomics to investigate ligand-receptor interactions in glioblastoma, uncovering poor prognostic pairs that could inform therapeutic strategies (ref: Yoshimoto doi.org/10.3390/cells14191540/). Furthermore, Zhang et al. demonstrated that targeting Wnt/β-catenin activation in combination with temozolomide significantly inhibited glioblastoma growth in preclinical models, suggesting a promising therapeutic avenue (ref: Zhang doi.org/10.1016/j.gendis.2025.101624/). Collectively, these studies highlight the potential of spatial transcriptomics to enhance our understanding of glioma biology and inform the development of novel therapeutic strategies.