Spatial transcriptomics has emerged as a pivotal tool in understanding glioma biology, particularly through innovative methodologies such as panoramic spatial enhanced resolution proteomics (PSERP). This technique allows for rapid quantitative profiling of proteomic spatial variability within whole tissue sections at sub-millimeter resolution, thereby retaining essential spatial information that is crucial for elucidating tumor architecture and heterogeneity (ref: Xu doi.org/10.1186/s13045-025-01710-5/). In parallel, the development of GBmap has facilitated the creation of a comprehensive single-cell and spatial atlas of IDH-wildtype glioblastoma, revealing significant insights into cellular heterogeneity and spatial organization, which are critical for understanding the aggressive nature of this malignancy (ref: Ruiz-Moreno doi.org/10.1093/neuonc/). Furthermore, multiomic profiling of glioblastoma metabolic lesions has highlighted the spatial genomic evolution within tumors, identifying dipeptidase-1 as a novel vascular marker, thus linking metabolic changes to genomic instability and providing potential diagnostic and therapeutic avenues (ref: Anand doi.org/10.1093/neuonc/).

The interplay between metabolic profiling and genomic evolution in glioblastoma is underscored by studies revealing that hypermetabolic lesions correlate with increased genomic abnormalities, suggesting a complex intratumoral evolution that may inform therapeutic strategies (ref: Anand doi.org/10.1093/neuonc/). In addition, the use of in vivo magnetic resonance spectroscopy to quantify glycine levels in gliomas has been highlighted as a critical method for assessing tumor growth, emphasizing the importance of metabolic intermediates in tumor biology (ref: Huang doi.org/10.1002/jmri.29824/). Moreover, a multiparametric radiomics approach has demonstrated superior predictive capabilities for molecular genotypes in adult-type diffuse gliomas, showcasing the potential of integrating imaging features with metabolic and genomic data to enhance diagnostic accuracy and inform treatment decisions (ref: Bai doi.org/10.1186/s12880-025-01729-7/).

The immune landscape of gliomas, particularly pediatric high-grade gliomas (pHGGs), has been extensively characterized using single-cell RNA sequencing and spatial transcriptomics, revealing significant differences in the immune microenvironment compared to adult gliomas (ref: LaBelle doi.org/10.1016/j.xcrm.2025.102095/). This comprehensive analysis has implications for therapeutic responses, particularly in the context of immune checkpoint inhibitors like PD-1 blockade, where induced B cell receptor diversity has been shown to predict treatment outcomes (ref: Che doi.org/10.1073/pnas.2501269122/). Additionally, targeting mesenchymal monocyte-derived macrophages has emerged as a promising strategy to enhance glioblastoma sensitivity to temozolomide, indicating that understanding the immune landscape can lead to novel therapeutic approaches (ref: Gao doi.org/10.1016/j.jare.2025.05.032/).

Addressing therapeutic resistance in glioblastoma remains a critical challenge, with studies focusing on the role of mesenchymal monocyte-derived macrophages in enhancing the efficacy of temozolomide treatment (ref: Gao doi.org/10.1016/j.jare.2025.05.032/). The identification of dipeptidase-1 as a vascular marker associated with hypermetabolic lesions further elucidates the relationship between metabolic changes and therapeutic resistance, suggesting that targeting these pathways could improve treatment outcomes (ref: Anand doi.org/10.1093/neuonc/). Moreover, the integration of radiomics in predicting molecular genotypes has shown promise in stratifying patients for personalized therapeutic approaches, thereby addressing the heterogeneity of glioblastoma and enhancing the precision of treatment strategies (ref: Bai doi.org/10.1186/s12880-025-01729-7/).

Radiomics has revolutionized glioma diagnosis by enabling the extraction of quantitative features from medical imaging, which can be correlated with molecular genotypes. A recent study demonstrated that a multiparametric radiomics signature derived from hybrid PET/MRI scans significantly outperformed traditional imaging modalities in predicting IDH, MGMT, and TERT statuses, achieving AUC values of 0.97, 0.86, and 0.90, respectively (ref: Bai doi.org/10.1186/s12880-025-01729-7/). Additionally, the PSERP approach has provided insights into the spatial proteomic landscape of gliomas, allowing for a deeper understanding of tumor biology and pathology at the molecular level (ref: Xu doi.org/10.1186/s13045-025-01710-5/). These advancements underscore the potential of integrating radiomics and proteomics to enhance diagnostic accuracy and inform treatment strategies in glioma management.