Accurately localizing brain motor areas is vital for protecting motor function during neurosurgical procedures. Magnetoencephalography (MEG) based on optically pumped magnetometer (OPM) improves the availability of MEG in clinical applications. The aim of this study is to evaluate the availability, accuracy and precision of "OPM-MEG" for localizing motor areas in brain tumor patients and healthy individuals. Participants were enrolled and subjected to primary motor area localization by both 3T-fMRI and 128-channel OPM-MEG examinations. The localization accuracy (ability of mapping on the anatomical location) and precision (activation signal centralization) were compared between the two methods, and accuracy was further validated by intraoperative direct cortical electrical stimulation (DCS) on the localized area with assistance of neuro-navigation system. A total of 12 participants (7 brain tumor patients and 5 healthy individuals) were enrolled and all had successful localization for motor areas by both methods. The average time of OPM-MEG examination for each limb function was approximately 9 minutes. The localizations by both methods mainly covered the anatomical location of primary motor cortex and were partially overlapped. The motor activation signal identified by OPM-MEG was more centralized than fMRI did. The centroid of motor area localized by the OPM-MEG deviated from it by fMRI, with a median distance of 19.7 mm and 27.48 mm for hand or foot localization, respectively. Furthermore, the OPM-MEG centroid for hand movement successfully triggered corresponding hand response by DCS. In this first-in-human study exploring the potential of OPM-MEG in functional localization of motor areas, we revealed its availability and reliability in mapping motor areas, demonstrating it as a promising tool in assisting neurosurgical practice and neuroscience research.

The objective of this study was to investigate IL13Ra2 expression in brainstem glioma (BSG) and its correlation with key markers, functions, and prognostic implications, evaluating its therapeutic potential. A total of 80 tumor samples from BSG patients were analyzed. Multiplex immunofluorescence was used to examine six markers—IL13Ra2, H3.3K27M, CD133, Ki67, HLA-1, and CD4—establishing relationships between IL13Ra2 and these markers. Survival analysis, employing Kaplan–Meier and Cox proportional hazard regression models, encompassed 66 patients with complete follow-up. RNA-Seq data from a previously published study involving 98 patients were analyzed using the DESeq2 library to determine differential gene expression between groups. Gene Ontology (GO) enrichment and single-sample gene set enrichment analysis (ssGSEA) via the clusterProfiler library were used to delineate the gene functions of differentially expressed genes (DEGs). Nearly all the BSG patients displayed varying IL13Ra2 expression, with 45.0% (36/80) exhibiting over a 20% increase. Elevated IL13Ra2 levels were notably observed in pontine gliomas, diffuse intrinsic pontine gliomas (DIPGs), H3F3A-mutant gliomas, and WHO IV gliomas. IL13Ra2 expression was strongly correlated with H3.3K27M mutant protein, Ki67, and CD133. Patients with IL13Ra2 expression >20% showed shorter overall survival compared to those with ≤20% IL13Ra2 expression. The Cox proportional hazard regression model identified H3F3A mutations, rather than IL13Ra2 expression, as an independent prognostic factor. Analysis of RNA-Seq data from our prior cohort confirmed IL13Ra2’s correlation with H3.3, CD133, and Ki67 levels. Widespread IL13Ra2 expression in BSG, particularly elevated in the H3F3A mutant group, was strongly correlated with H3F3A mutations, increased proliferation, and heightened tumor stemness. IL13Ra2 represents a promising therapeutic target for BSGs, potentially benefiting patients with H3K27M mutations, DIPGs, WHO Grade IV, and pontine location-specific BSGs, particularly those with H3K27M mutations.

Heat shock protein peptide complex 96 (HSPPC-96) has been proven to be a safe and preliminarily effective therapeutic vaccine in treating newly diagnosed glioblastoma multiforme (GBM) (NCT02122822). However, the clinical outcomes were highly variable, rendering the discovery of outcome-predictive biomarkers essential for this immunotherapy. We utilized multidimensional immunofluorescence staining to detect CD4⁺ CD8⁺ and PD-1⁺ immune cell infiltration levels, MxA and gp96 protein expression in pre-vaccination GBM tissues of 19 patients receiving HSPPC-96 vaccination. We observed low MxA expression was associated with longer OS than high MxA expression (48 months vs. 20 months, p=0.038). Long-term survivors (LTS) exhibited significantly lower MxA expression than short-term survivors (STS) (p= 0.0328), and ROC curve analysis indicated MxA expression as a good indicator in distinguishing LTS and STS (AUC=0.7955, p=0.0318). However, we did not observe any significant impact of immune cell densities or gp96 expression on patient outcomes. Finally, we revealed the association of MxA expression with prognosis linked to a preexisting TCR clone (CDR3-2) but was independent of the peripheral tumor-specific immune response. Taken together, low MxA expression correlated with better survival in GBM patients receiving HSPPC-96 vaccination, indicating MxA as a potential biomarker for early recognition of responsive patients to this immunotherapy.Clinical Trial Registration: ClinicalTrials.gov (NCT02122822) http://www. chictr.org.cn/enindex.aspx (ChiCTR-ONC-13003309).

Lipid metabolic reprogramming represents a fundamental oncogenic mechanism. However, clinical relevance of lipid metabolism (LM) alterations in gliomas remain to be fully elucidated. LM-based classification and subsequent correlative analyses were performed using glioma bulk RNA-seq datasets retrieved from The Cancer Genome Atlas (TCGA). Radiomic models were constructed and validated using magnetic resonance imaging (MRI) datasets from The Cancer Imaging Archive (TCIA) and our in-house glioma cohort. Immunohistochemical (IHC) staining was employed to detect the protein expression of the key gene in glioma tissue samples. Single cell RNA-seq datasets from the GBMap database were used to characterize the distribution and functional roles of the key gene in the tumor microenvironment (TME) of gliomas. Consensus clustering based on LM pathways identified three distinct subtypes, respectively dominated by steroid metabolism (ST-type), triglyceride metabolism (TC-type), and sphingolipid metabolism (SP-type). The SP-type was independently associated with poorer prognosis and displayed enhanced activity in pathways linked to aggressive tumor phenotypes and radiotherapy resistance. Radiomic features enabled accurate identification of SP-type gliomas, thereby offering a non-invasive strategy for predicting this subtype. Genes GLA, GBL1, and HSD3B7 were identified as signature genes of the SP-type. Higher expression of HSD3B7 was associated with poor prognosis and exerted functional effects on multiple signaling pathways across various cellular components of the TME, which may contribute to glioma progression. Gliomas exhibited marked heterogeneity in LM and can be classified into three subtypes, with the SP-type exhibiting the most aggressive clinical behavior.

Background Lipid metabolic reprogramming represents a fundamental oncogenic mechanism. However, clinical relevance of lipid metabolism (LM) alterations in gliomas remain to be fully elucidated. Methods LM classification and following correlative analyses were conducted using glioma bulk RNA-seq datasets downloaded from The Cancer Genome Atlas (TCGA). Radiomic models were constructed and validated using MRI datasets from The Cancer Imaging Archive and our glioma cohort. Immunohistochemical (IHC) staining was used to measure protein expression of key gene in glioma tissues. Single-cell RNA-seq datasets from the GBMap dataset were utilized to characterize distribution and functional relevance of key gene in tumor microenvironment (TME) of gliomas. Results Consensus clustering of LM pathways revealed three subtypes that were respectively dominated by steroid metabolism (ST-type), triglyceride metabolism (TC-type), and sphingolipid metabolism (SP-type). The SP-type independently correlated with worse outcomes, and exhibited increased activity in pathways related to aggressive phenotypes and radiotherapy resistance. Radiomic features were capable of accurate identification of SP-type gliomas, thus providing a non-invasive approach to predict outcomes. Genes GLA, GBL1, and HSD3B7 emerged as signature genes for the SP-type. Higher expression level of HSD3B7 associtaed with poor prognosis and exhibited functional relevance to multiple signaling pathways within diverse TME cellular components that eventually promote glioma progression. Conclusion Gliomas exhibited marked heterogeneity in LM and can be classified into three subtypes, with the SP-type exhibiting the most aggressive clinical behavior.