Abstract

   Brainstem gliomas (BSG) is a highly malignant central nervous system
   childhood tumors with 5-year survival rate <10%. Metabolism during
   radiotherapy is a dynamic and precisely programmed process, improving
   clinical outcomes and guiding therapy decisions of BSG. Here we
   construct diagnostic and prognostic assays of BSG via circulating
   metabolites based on both cross-sectional study and longitudinal cohort
   study with 106 BSG patients. We employ nanoparticle enhanced laser
   desorption/ionization mass spectrometry to characterize static and
   dynamic snapshots of metabolites during BSG radiotherapy. We show that
   this serological tool reaches the area under the curve of 0.933 for BSG
   diagnosis in an independent blind test and predicts risk of patients
   with significant differences (p < 0.05) in prognostic outcomes. We
   further identify eight distinct temporal patterns of metabolite
   regulation associated with radiotherapy responses and tracked the
   metabolic trajectory via dynamic metabolic snapshots throughout
   radiotherapy process. If further validated, this framework could be
   extended to derive comprehensive metabolic pictures for cancers
   including but not limited to BSG.

   Subject terms: Mass spectrometry, CNS cancer, Metabolomics
     __________________________________________________________________

   Brainstem gliomas are an aggressive form of paediatric cancer. Here,
   the authors use metabolomics to assess differences in metabolites
   during radiotherapy.

Introduction

   Brainstem gliomas (BSG), accounting for 10–15% of all childhood brain
   tumors, exhibit a peak incidence of 0.6 cases per million population
   annually within the age range of 6–9 years^[52]1–[53]3. Patients
   diagnosed with BSG display a poor prognosis, with a median survival
   time of 10–12 months and a 5-year survival rate of less than
   10%^[54]4,[55]5. Radiotherapy is a standard BSG treatment in clinical
   settings, capable of alleviating symptom progression and prolonging
   progression-free survival by a few months^[56]6–[57]9. Patients who
   undergo early and timely surgery exhibit prolonged overall survival
   (OS) time compared to those in late surgery group (median: 28.4 vs.
   18.7 months)^[58]10–[59]12. Accurate diagnosis, precise prognosis, and
   timely monitoring during radiotherapy are crucial in managing BSG
   patients. However, there are certain risks associated with biopsies,
   which involve sampling tissue for analysis, especially when dealing
   with tumors located in eloquent regions of the brain. Furthermore,
   biopsies hardly achieve dynamic monitoring via adequate sampling for
   effective therapy selection^[60]13,[61]14.

   Non-invasive radiologic imaging is the “one and only” technique to
   discover cancerous region before surgical intervention and evaluate
   tumor prognosis through timely monitoring. Nevertheless, imaging
   modalities afford an accuracy of only 65–73% in BSG detection due to
   the unsatisfactory image resolution (~1 mm), which could lead to the
   misdiagnosis of minimal lesions^[62]15,[63]16. The limited precision of
   imaging techniques highlights an urgent need for incorporating liquid
   biopsy diagnostics^[64]14,[65]17. Unlike many other solid tumors, BSG
   lacks clinically approved circulating biomarkers owing to the eloquent
   location of the brainstem within the intracranial region and the
   presence of the blood-brain barrier (BBB)^[66]18,[67]19. In addition,
   the current collaborative technological efforts (e.g., circulating
   tumor DNA^[68]5 and cell-free DNA sequencing^[69]20) almost all fail in
   testing on peripheral blood due to the limited mutations in circulating
   systems.

   In the context of BBB dysfunction, there may be transport-independent
   mechanisms for metabolites (size <1.8 nm) to cross the BBB. This is
   facilitated by the compatible pore size of brain microvessel
   endothelial cells, which typically range from about 1.4–1.8 nm,
   enabling metabolites to enter the bloodstream^[70]21–[71]24. Metabolic
   alterations in BSG disrupt normal homeostatic processes due to
   tumor-related stress in the blood, leading to bioenergetic
   disturbances, endocrine imbalances, and oxidative stress. BSG-derived
   metabolism may synergistically promote adverse clinical outcomes. Our
   previous study has validated the function of BBB in separating blood
   and brain/cerebrospinal fluid (CSF) systems, as well as in regulating
   the permeability that allows for metabolite transport under disease
   status^[72]25. The feasibility of metabolic profiling has also been
   explored to understand the molecular pathogenesis of other tumors in
   the brain, such as glioblastoma, melanoma brain metastases^[73]26, and
   neuroblastoma^[74]27. Serum metabolite profiles, including static and
   dynamic metabolic snapshots, may provide an alternative for capturing
   critical metabolic information about BSG and enable the tracking of
   tumor dynamics via metabolic endpoints throughout the complete course
   of a patient’s radiotherapy.

   Mass spectrometry (MS) is applied in the profiling and discovery of
   metabolic biomarkers by detecting the mass-to-charge ratio (m/z) of
   metabolites at a high resolution at ppm level. Recently,
   nanoparticle-enhanced laser desorption/ionization MS (NPELDI-MS) has
   been developed for capturing metabolic static and dynamic snapshots.
   This technique features few pretreatment operations, fast detection
   speed, and trace sample consumption^[75]28–[76]30, making it ideal for
   scenarios where expeditious reporting is critical^[77]31–[78]33. In
   particular, tailored inorganic nanoparticles have been introduced as
   chromatography alternatives prior to MS analysis to selectively enrich
   metabolites, which enhances its applicability for biofluid analysis and
   biomarker detection.

   Molecular characteristics of BSG remain poorly characterized due to the
   relatively low incidence rate. Here, we constructed a cross-sectional
   and longitudinal cohort of pediatric BSG, ranging from 1 to 15 years
   old, via both static and dynamic blood sampling (Fig. [79]1a–c). We
   performed NPELDI-MS to acquire a more comprehensive metabolic picture
   of BSG. Based on the static metabolic snapshots, we distinguished BSG
   patients from healthy donors (HDs) and further predicted patient
   survival. Furthermore, we identified eight distinct temporal patterns
   of metabolite regulation corresponding to different BSG subsets
   associated with radiotherapy responses. We monitored the dynamic
   disturbances of key metabolites during the course of therapy. Our study
   established an approach to reveal mechanisms and heterogeneity of
   diseases, including but not limited to brain tumors at metabolic level.

Fig. 1. Study design for analyzing serum metabolic profiles in diagnosis,
prognosis, and monitoring of brainstem glioma (BSG).

   [80]Fig. 1
   [81]Open in a new tab

   a Serum samples were enrolled from BSG patients and healthy donors
   (HDs) on micro-arrayed chips, for nanoparticle-enhanced laser
   desorption/ionization mass spectrometry (NPELDI-MS) analysis. b Machine
   learning of serum metabolic profiles was conducted to obtain the
   biomarker panel to achieve the diagnosis, prognosis, and dynamic
   monitoring of BSG. c Clinical cohorts were collected to acquire the
   static and dynamic metabolic snapshots. d Study design of participant
   enrollment, including 125 HDs and 174 BSG patients. e Longitudinal
   sample collection of 62 BSG patients at three time points as the
   dynamic metabolic snapshots. Created in BioRender. Yang, Y. (2025)
   [82]https://BioRender.com/uo0yzh1.

Results

Study design and patient cohort characteristics

   We first applied an analytic framework with a case-control design on
   the cross-sectional cohort (cohort 1) to diagnose BSG from the aspects
   of static metabolic snapshots. We successively prognosed the
   radiotherapy sensitivity among BSG patients (cohort 2) via univariant
   and multivariant Cox regression and validated the performance by
   log-rank test, including 96 BSG patients with a follow-up period of
   more than 1 year as prognostic outcomes. Lastly, we sought progressive
   metabolic evolution correlated with radiotherapy responses by
   clustering of metabolic trajectories with dynamic metabolic snapshots
   on the longitudinal cohort (cohort 3). Demographic and clinical
   characteristics of the enrolled participants are presented in
   Supplementary Tables [83]1 and [84]2.

   We established a Chinese BSG cohort within a duration of 6 years (from
   November 2017 to May 2023) at Xinhua Hospital Affiliated to Shanghai
   Jiao Tong University School of Medicine. The cohort comprised 174 BSG
   patients and 125 HDs, from which we enrolled 106 BSG patients
   satisfying the following criteria. These criteria included (1) BSG
   diagnosed by magnetic resonance imaging (MRI) presentation and clinical
   symptoms, (2) completion of radiotherapy, and (3) a follow-up period of
   more than a year (Fig. [85]1d). Given the complexities and inherent
   risks associated with performing biopsies in brainstem tumors,
   particularly in cases of diffuse intrinsic pontine glioma, obtaining
   WHO grades for all patients present challenges, especially before
   2020^[86]34. To ensure the accuracy of patient enrollment and diagnosis
   as BSG cases, the diagnosis of each patient was collaboratively
   reviewed by 2 oncologists and imaging specialists. If conflicting
   opinions arose, senior physicians were consulted to avoid any potential
   biases in data evaluation. To capture the dynamic metabolic snapshots
   at high resolution, we performed a design of high-density blood
   sampling on the longitudinal cohort consisting of 62 BSG patients.
   Consenting participants submitted three-weekly blood draws prior to
   radiotherapy, until the completion of one cycle of radiotherapy (three
   serum samples collected for each participant, Fig. [87]1e).

   To evaluate BSG-related metabolic snapshots, we employed a
   pretreatment-free MS approach that incorporated ferric nanoparticles to
   substitute the commonly used liquid/gas phase extraction and
   separation. The NPELDI-MS allowed sample multiplexing with reproducible
   metabolic profiles for native serum inputs as trace as 0.1 μL
   (Supplementary Fig. [88]1a, coefficient of variation <15%). In
   particular, a rapid analytical speed of ~2 s per sample (with 2000
   laser shots at a pulse frequency of 1000 Hz) and throughput of 384
   samples per batch could be accomplished by the NPELDI-MS platform
   (Supplementary Fig. [89]1b). As a result, detection process of 405
   serum samples in this project was completed within ~3 h.

   The raw MS spectrum recorded ~120,000 data points with a total ion
   count of ~7 × 10^7 within the mass range of 100–1000 Da. After quality
   control and data filtering (see details in [90]Supporting Information),
   we obtained 402 metabolic features (defined as the localized highest
   intensity of the raw MS data) across different samples (Supplementary
   Fig. [91]1c). Among these, 287 features (71.4%) varied from HDs to BSG
   patients (p < 0.05, Student’s t-test). Notably, the serum metabolic
   snapshot database, which contains a sufficient amount of signal
   intensity and feature number, serves as the foundation for the
   downstream analysis.

Diagnostic value of circulating metabolites in BSG patients

   Given that metabolic reprogramming is crucial for cancer
   occurrence^[92]35–[93]37, we were thus interested in delineating the
   distinct metabolic patterns between HDs and BSG based on the static
   metabolic snapshots (Supplementary Fig. [94]2a). We built diagnostic
   classification models based on machine learning algorithms on the
   discovery set (n = 45/54, HDs/BSG). The computational pipeline for
   machine learning of serum metabolic profiles from the sets included
   feature selection and model building (Fig. [95]2a, Supplementary
   Fig. [96]2b). Specifically, there were no significant differences in
   age and gender (p > 0.05) between BSG and HDs in the discovery set
   (Supplementary Table [97]3). The classification performance was further
   evaluated on an independent validation set (n = 15/19, HDs/BSG).
   Notably, we obtained 81.2% power at 0.05 significance level with a
   sample size of 80 (40/40, HDs/BSG), based on the preliminary study on
   12 individuals (6/6, HDs/BSG, Supplementary Fig. [98]2c). The result
   indicated the discovery and validation sets hold adequate sample size
   for validating the classification models.

Fig. 2. Diagnostic value of circulating metabolites in BSG patients.

   [99]Fig. 2
   [100]Open in a new tab

   a Study design for BSG diagnosis using machine learning. Static
   metabolic snapshots from 99 participants (45/54, HDs/BSG) in the
   discovery set for feature selection and model building. The optimized
   model was tested in an independent validation set (15/19, HDs/BSG). b
   Coefficients of the least absolute shrinkage and selection operator
   (LASSO) and Ridge algorithms were shown as the evaluation index for
   seven metabolic features. c t-distributed stochastic neighbor embedding
   of serum metabolic profiles for 60 HDs and 73 BSG patients was plotted
   using identified metabolites. d Receiver operating characteristic (ROC)
   curves of the discovery set and validation set to distinguish HDs and
   BSG patients within the metabolic panel. The corresponding confusion
   matrices were shown as the inset. e ROC curves analysis was shown with
   each single feature (AUC of 0.662–0.818) of seven-feature diagnostic
   panel. f Violin-plot illustrated the differential regulation of
   normalized intensities for metabolic features in HDs and BSG.
   Statistical significance was calculated using two-sided Student’s
   t-test. ***p < 0.005, *p < 0.05. p = 5.224 × 10^−8 (LA); 0.004 (LA’);
   4.446 × 10^−5 (Val); 4.944 × 10^−8 (Leu); 4.422 × 10^−5 (Thr); 0.0008
   (2AAS); 0.025 (Orn). g Potential pathways were differentially regulated
   in HDs and BSG patients. Color and size of each circle were correlated
   to the p value and enrichment ratio. Statistical significance was
   calculated using enrichment analysis by MetaboAnalyst 5.0
   ([101]https://www.metaboanalyst.ca/). LA and LA’ lactic acid, Val
   valine, Leu leucine, Thr threonine, 2AAS 2-aminomuconic acid
   semialdehyde, Orn ornithine. Source data are provided as a [102]Source
   Data file.

   In the feature selection step, we sought to utilize dimension-reduction
   strategies to classify BSG versus HDs based on the outstanding features
   with more cost-effective combinations of the molecules (Supplementary
   Fig. [103]3a, b, Supplementary Table [104]4). Considering the p (number
   of predictors) » n (number of samples) problems, two widely used
   shrinkage methods, least absolute shrinkage and selection operator
   (LASSO) and Ridge, were employed to select the optimal biomarker
   combination based on the highest coefficient weights in two algorithms
   (Fig. [105]2b). As a result, seven metabolic features corresponding to
   six metabolites were identified as highly ranked biomarkers (mean
   intensity > 200, p value < 0.05, and coefficient weight > 0.18),
   including 2-aminomuconic acid semialdehyde, lactic acid, valine,
   leucine, threonine, and ornithine (Supplementary
   Table [106]5)^[107]38,[108]39. Unsupervised clustering analysis partly
   intuitively distinguished BSG from HDs, including t-distributed
   stochastic neighbor embedding (Fig. [109]2c) and principal component
   analysis (Supplementary Fig. [110]2d).

   We further conducted in-vitro studies to investigate the biological
   effects of metabolite biomarkers on SHSMU-DIPG-01 cells. Our findings
   revealed concentration-dependent effects of these metabolites on DIPG
   cell proliferation, as validated by the cell counting kit-8 assay
   (p < 0.05, Supplementary Fig. [111]4). These results suggest that these
   metabolites play a regulatory role in DIPG cell behavior, potentially
   by modulating metabolic pathways or inducing metabolic stress. This
   aligns with the broader impact of metabolic reprogramming on tumor
   development and progression^[112]37. The sensitive responses of DIPG
   cells to these metabolites further support their potential as
   biomarkers for distinguishing BSG from healthy individuals.

   In the model-building step, four commonly used algorithms, including
   LASSO, Ridge, K-nearest neighbor (kNN), and random forest (RF), were
   used to construct classification models on the metabolite biomarker
   combinations. The optimized model by RF reached the highest area under
   the curve (AUC) of 0.923 (95% confidence interval (CI) = 0.870–0.976)
   based on the receiver operating characteristic curve (ROC) analysis in
   the discovery set. We then tested the model on the validation set,
   resulting in a promising AUC of 0.933 (95% CI = 0.853–1.000) for
   distinguishing BSG patients from HDs (Fig. [113]2d, Supplementary
   Fig. [114]5a, b, Supplementary Table [115]4). We recruited a new cohort
   (Supplementary Table [116]2) from Shanghai Xinhua Hospital, with 161
   samples collected (96/65, BSG/HDs) as the external set. Notably, the
   external set was independent from the discovery and validation sets in
   model construction stage and blinding to the as-built model
   (Supplementary Fig. [117]6a). The situations for blood drawn were the
   same for all sample sets. We obtained the AUC of 0.847–0.852
   (Supplementary Fig. [118]6b), consistent with the previous results in
   the methodology development. The consistent diagnostic results
   validated the robustness of the biomarkers, facilitating translation of
   the biomarkers to larger patient populations.

   An evaluation of the reliability of the machine-learning strategy was
   conducted using confusion matrices, which demonstrated that samples
   were classified with a high accuracy of 86.9% in discovery set and
   88.2% in validation set (Fig. [119]2d). Importantly, the panel
   constructed of seven metabolic features together outperformed the
   diagnostic performance by single one of these features (AUC:
   0.662–0.818, Fig. [120]2e, Supplementary Table [121]5). Another major
   challenge in clinical practice is the differentiation of BSG from other
   neural diseases by MRI imaging^[122]40. We added the comparison between
   BSG and other neural diseases (28 encephalitis cases and 40 idiopathic
   epilepsy cases) through the static metabolic snapshots. We adopted the
   same protocol to detect the differences (Supplementary Fig. [123]7a).
   As a result, the models also achieved outstanding performance in
   differentiating BSG from encephalitis and idiopathic epilepsy, with AUC
   of 0.929 and 0.923, respectively (Supplementary Fig. [124]7b).

   Specifically, five features were over-regulated (p value < 0.05) in BSG
   patients, while two features were down-regulated (p value < 0.05) with
   BSG diagnosis (Fig. [125]2f). Furthermore, metabolite set enrichment
   analysis showed several BSG-disturbed metabolic pathways based on Small
   Molecule Pathway Database (SMPDB) dataset, such as valine, leucine, and
   isoleucine degradation, glycine and serine metabolism, threonine and
   2-oxobutanoate degradation, propanoate metabolism, as compared to HDs
   (Fig. [126]2g, Supplementary Table [127]6). Subsequently, we conducted
   metabolite set enrichment analysis using Kyoto Encyclopedia of Genes
   and Genomes (KEGG) dataset to identify the most strongly influenced
   metabolic pathways using the identified metabolites. Our findings
   revealed that the metabolites were notably enriched in six distinct
   metabolic pathways, aligning with the results obtained from the SMPDB
   dataset (Supplementary Fig. [128]8).

   To further investigate the biological mechanisms underlying the
   differences between BSG and HDs, we performed differential expression
   analysis using RNAseq data obtained from orthotopically xenografted BSG
   specimens (brainstem tissues, n = 4) and age-matched controls (normal
   brainstem tissues, n = 3, Supplementary Fig. [129]9). We also validated
   the correlation based on the analysis of the GEO dataset
   [130]GSE162976, comparing DIPG cells against two distinct neural
   progenitor controls: human neural stem cells (hNSC) and normal human
   astrocytes (NHA; Supplementary Fig. [131]10). As a result, we detected
   notable variances in gene expression within the perturbed metabolic
   pathways, which were reflected in the RNAseq expression data obtained
   in this study and further supported by the consistent results obtained
   from GEO database.

   Moreover, our exploration revealed the regulation of specific genes
   associated with the adaptive reprogramming of tumor cells. Notably, the
   genes Arg1, Arg2, Nos2, and P4ha1 were found to reprogram arginine and
   proline metabolism. Meanwhile, Arg1, Arg2, and Nos2 were shown to
   disturb arginine biosynthesis. Arginine was converted into ornithine
   through the action of arginase. Additionally, the genes Hpgds and
   Nat8f6 were identified as key regulators in glutathione metabolism.
   Gldc and Pgam2 were also found to influence the metabolism of glycine,
   serine, and threonine. Furthermore, Enpp1 and Enpp3, which encode two
   critical enzymatic components of the pantothenate and CoA biosynthesis
   pathway, were shown to be epigenetically regulated in brain tumors.
   These modulations closely related to the Warburg effect and the TCA
   cycle, and regulate the expression of lactate. Lastly, Aox4 was found
   to regulate tyrosine metabolism as well as the degradation of valine,
   leucine, and isoleucine, in conjunction with Hmgcs2. Therefore, we
   speculated that the unique metabolic signature arises from the
   metabolic rewiring of brain tumors, potentially offering valuable
   insights for BSG.

   Taken together, these findings suggest a unique metabolic signature
   existed in BSG and demonstrated the power of metabolic profiling for
   biomarker discovery. The panel constructed by circulating metabolites
   facilitates rapid detection of BSG more precise and accessible.

Static metabolic snapshots correlated with the prediction of BSG

   We proceeded to investigate the potency of using static metabolic
   snapshots in cohort 2 to predict prognosis of BSG. To determine the
   clinical outcome of this study, we enrolled patients who had been
   followed up for over one year and collected survival data as well as
   clinical information (including gender and age). A total of 96 patients
   were included in the prognosis study with OS being 4 months to 51
   months and a median OS being 11 months. Importantly, clinic follow-ups
   of these BSG patients were carried out via phone calls or health
   records to ensure the completeness of data (Fig. [132]3a). Similar to
   the diagnostic section, we analyzed the static metabolic snapshots from
   two randomly assigned sample sets, containing 58 observations with 41
   deaths for training set and 38 observations with 31 deaths for a
   testing set (Fig. [133]3b).

Fig. 3. Static metabolic snapshots correlated with the prediction of BSG.

   [134]Fig. 3
   [135]Open in a new tab

   a Clinical information of 96 BSG patients. Created in BioRender. Yang,
   Y.(2025) [136]https://BioRender.com/uo0yzh1. b Workflow for building
   the prognostic model via seven-feature panel by calculating risk scores
   based on static metabolic snapshots. c Plots of overall survival (OS)
   duration and risk score of each patient. The vertical dash line
   demonstrated the median score of −2.7 and stratifies patients into
   high-risk group (red) and low-risk group (blue) in training set (green)
   and testing set (blue). d Kaplan–Meier curves of OS of patients in the
   low-risk group (blue line) and high-risk group (red line) with
   p = 0.031 by log-rank test in the training set and p = 0.044 by
   log-rank test in the independent testing set. Source data are provided
   as a [137]Source Data file.

   We conducted a variable selection on the training set and built the
   composite risk score on the testing set (Fig. [138]3b). In particular,
   the univariate Cox regression was applied to reduce dimensionality and
   select metabolic features associated with the OS of BSG. Then, the
   multivariant Cox regression model was constructed to predict prognosis
   associated with metabolic vulnerabilities. Importantly, we identified a
   seven-feature panel correlated with clinical outcomes, consisting of
   lactic acid, oxalacetic acid, niacinamide, threonic acid, succinic
   acid, arginine, and glucose (Supplementary Table [139]7). Importantly,
   even after adjusting for key clinical covariates such as age, gender,
   BMI, and Lansky score, as suggested by previous studies^[140]41, the
   odds ratios of risk scores remained nearly unchanged (Supplementary
   Table [141]8).

   Our in vitro experiments demonstrated the specific effects of metabolic
   markers on cell viability in SHSMU-DIPG-01 models (Supplementary
   Fig. [142]11). In particular, we have already validated lactic acid in
   the diagnostic panel. Hence, its presence in the prognostic panel does
   not warrant further investigation. Besides, arginine, succinic acid,
   and lactic acid exhibited significant anti-proliferative effects on
   DIPG cells (p < 0.05). Glucose and niacinamide played a role in
   promoting cell growth (p < 0.05). These findings highlight the
   relevance of these metabolites in modulating tumor cell behavior.

   The prognostic panel consisted of differentially regulated metabolites
   together identified 14 potentially altered metabolic pathways
   (Supplementary Fig. [143]12a). We thus conducted RNAseq experiments on
   subcutaneous xenografted BSG specimens after radiotherapy (tumor
   tissues, n = 5) and age-matched BSG specimens before radiotherapy
   (tumor tissues, n = 6). We highlighted seven differentially expressed
   genes (DEGs) (ALDOC, CA9, FBP1, HKDC1, LOC102724560, NDUFA4L2, and
   P4HA2), associated with the metabolic pathways identified in the
   prognostic panel and expression data (Supplementary Fig. [144]12b). In
   particular, glycolysis/gluconeogenesis appeared enriched in tumor
   cells. Tumor growth is characterized by the upregulation of ALDOC,
   FBP1, and HKDC1 correlated with increased glucose and fructose
   consumption and lactate production^[145]42. Similarly, amino acid
   metabolism, regulated by P4HA2 and LOC102724560, a significant hallmark
   of glioblastoma impacting patient prognosis, has been confirmed through
   our metabolic and sequencing results and supported by existing
   literature^[146]43,[147]44.

   To mechanistically characterize the pathophysiological divergence
   between before RT (BRT) and after RT (ART) among BSG, we identified six
   significantly enriched pathways correlated with the prognostic
   metabolic signature in BSG through gene set enrichment analysis
   (Supplementary Fig. [148]12c). Enriched pathways involve TNF signaling,
   NF-κB signaling, p53 signaling, HIF-1 signaling, hippo signaling, and
   MAPK signaling, which have been demonstrated to impact on the
   sensitivity of tumors to radiotherapy^[149]45,[150]46. Intensive
   studies indicate that the prognostic panel of metabolites could be
   involved in the regulation of these signaling pathways, which have the
   potential to influence radiation prognosis. Specifically, lactate
   enhances the stability of HIF-1α, which promotes its transcriptional
   activity and subsequently regulates tumor growth and sensitivity to
   RT^[151]47. Glucose stress induces O-GlcNAcylation and further
   activates hippo-YAP pathways to promote tumorigenesis^[152]48.
   Niacinamide activates p53 activation and suppresses PARP1 to mediate
   DNA repair and cell proliferation in tumor^[153]49. Succinic acid
   promotes not only cancer cell migration and invasion but also cancer
   metastasis, mediated by PI3K-HIF-1α axis^[154]50. Arginine activates
   NF-κB signaling by regulating mTORC1-mediated lysosomal release of
   essential amino acids. It also induces the generation of intracellular
   nitric oxide, which stimulates HIF-1α signaling and further promotes
   tumor proliferation^[155]51. Threonine metabolism influences tumor cell
   proliferation by driving abnormal tRNA modifications and protein
   translation within tumors^[156]52. Thus, we conclude that the
   prognostic panel reflects the rewired metabolism of tumors.

   The risk score for every patient in both the training set and the
   testing set was estimated based on the formula, by summing up the
   product of each feature intensity and coefficient weight. Based on the
   dichotomized label on the median risk score, the BSG patients were
   stratified into two groups: the high-risk group and the low-risk group.
   Notably, the high-risk group (risk score > −2.7) contained 29
   observations with 24 events in the training set and 14 observations
   with 14 events in the testing set. Meanwhile, there were 29
   observations with 17 events in the training set and 24 observations
   with 16 events in the testing set among the low-risk group (risk
   score < −2.7, Fig. [157]3c). Multivariate Cox regression analysis
   demonstrated that the risk score tightly correlated with mortality risk
   and became an important factor of survival in both the training and
   testing sets.

   We compared the OS duration of BSG patients. Kaplan-Meier curves were
   applied to evaluate the prognostic prediction efficiency of the risk
   score. Accordingly, the median OS duration in the low-risk group was
   significantly more prolonged than the OS duration in the high-risk
   group (p = 0.031 in the training set and p = 0.044 in the testing set,
   by log-rank test, Fig. [158]3d). In this study, we also collected the
   information of molecular subtypes and MRI imaging, and further explored
   the prognostic effectiveness of H3K27M mutation (p = 0.004, log-rank
   test, Supplementary Fig. [159]13a), TP53 mutation (p = 0.554, log-rank
   test, Supplementary Fig. [160]13b), and ring enhancement (p = 0.106,
   log-rank test, Supplementary Fig. [161]13c). These results suggested
   higher levels of risk score acquired from static metabolic snapshots
   are correlated with significant or borderline significant worse patient
   outcomes. Notably, we validated the prognosis efficiency of static
   metabolic snapshots in predicting prognostic risks of BSG patients.

Dynamic metabolic snapshots during radiotherapy

   Circulating metabolites undergo dynamic changes during radiotherapy and
   are closely associated with the therapeutic efficacy of this treatment
   modality^[162]53,[163]54. We examined the dynamic metabolic snapshots
   with a time series to evaluate the connection with the radiotherapy
   prognosis. Specifically, the metabolic profiles of 43 BSG patients were
   investigated at a total of three time points before, during, and after
   radiotherapy (BRT/DRT/ART, Fig. [164]4a). Corresponding MRI images were
   also collected for all enrolled patients to facilitate comprehensive
   analysis and interpretation of the data.

Fig. 4. Dynamic metabolic snapshots during radiotherapy.

   [165]Fig. 4
   [166]Open in a new tab

   a Sample information collected for 43 BSG participants. Created in
   BioRender. Yang, Y.(2025) [167]https://BioRender.com/uo0yzh1. b
   Calculation of the change in tumor size based on magnetic resonance
   imaging (MRI) images. c ROC curves to distinguish volume-decreasing
   participants from volume-increasing participants using the static
   metabolic snapshots collected BRT. d Clustering of metabolic
   trajectories using dynamic metabolic snapshots along HDs, BRT, DRT, and
   ART. e Probabilities of six biomarkers tracing with potential
   clustering tendency. f Scatter plots of six metabolites constructed the
   diagnostic panel among HDs (black), BRT (red), DRT (blue), and ART
   (green). Orange lines referred to the average intensity of the
   metabolites in each group. Statistical significance was calculated
   using pairwise comparison via multiple two-sided t test. g Dynamic
   alterations of lactic acid in low-risk (blue) and high-risk (red)
   groups. Statistical significance was calculated using two-sided
   Student’s t-test. *p < 0.05: p = 3.686 × 10^−7. n.s. p > 0.05. Source
   data are provided as a [168]Source Data file.

   Clinically, difference in tumor volume size measured by MRI is an
   evaluation criterion of radiotherapy^[169]55,[170]56. Based on the
   tendency of tumor change shown from MRI images (Fig. [171]4b), we
   achieved a good predictive capability in distinguishing
   volume-decreasing participants from volume-increasing participants
   based on the BRT metabolic profiles, with an AUC of 0.818 by 5-fold
   cross validation (95% CI: 0.690–0.946, Fig. [172]4c). The results
   demonstrated consistency between metabolic profiles and actual tumor
   volume changes.

   Our samples were mainly from the HDs and BSG (as cohort 3) in the
   different periods of radiotherapy, including 117 HDs, 101 BRT, 85 DRT,
   and 85 ART, revealed detailed temporal patterns of metabolic changes.
   Interestingly, we found that metabolites with uni-directional behaviors
   dominated the features, and over half of them were significantly
   different between HDs and disease groups, as well as made a change in
   the development of radiotherapy (Supplementary Fig. [173]14a). These
   findings highlighted that even though the amount of each compound
   dynamically changes between HDs and BSG during radiotherapy, a highly
   coordinated metabolite regulatory network underlies the therapy
   process.

   We further performed c-means clustering using the differential
   metabolites among four phases (HDs, BRT, DRT, and ART) and identified
   eight distinct clusters (Fig. [174]4d and Supplementary Fig. [175]14b).
   Notably, 23.1% of all metabolites in cluster 1 and cluster 2 showed a
   marked alteration in BSG patients from HDs and a stepwise increase or
   decrease during radiotherapy. Metabolites in cluster 3 showed an
   increase and achieved the top in DRT, then a large decrease afterward,
   whereas metabolites in cluster 4 had a marked increase in disease
   groups and a large decrease with radiotherapy, suggesting that unique
   metabolic vulnerabilities may appear in disease groups which could be
   exploited to monitoring the therapeutic effects of radiotherapy.

   According to the diagnostic biomarker panel, we collected the
   probabilities of these biomarkers tracing in the potential cluster
   tendency (Fig. [176]4e). In particular, the 2-aminomuconic acid
   semialdehyde approached cluster 1 and gradually increased deviated the
   normal levels. Valine and threonine from cluster 2 were opposite of the
   tendency in cluster 1. Lactic acid and ornithine were included in
   cluster 3 and largely influenced by the radiotherapy process. Leucine
   from cluster 4 had no difference during the radiotherapy process
   (Fig. [177]4f). Importantly, we found lactic acid involved in pyruvate
   metabolism had different tendencies along with radiotherapy progression
   in low-risk and high-risk groups within the longitudinal cohorts of 62
   BSG participants (Fig. [178]4g).

   We further performed preclinical animal study using mouse tumor models
   to acquire paired samples (i.e., mice brain tumor and mice serum before
   and after radiation) with the same analysis from discovery set. The
   preclinical animal study also indicated significant alterations in
   metabolite levels before and after radiotherapy treatment in BSG,
   aligning with observations in paired serum samples (Supplementary
   Fig. [179]15).

   Taken together, these findings depicted a programmed metabolic
   trajectory over the course of radiotherapy, providing a systematic
   value for treatment response and prognostic implications.

Discussion

   Precise diagnosis and prognosis contribute to improving clinical
   outcomes and guiding therapy decisions of BSG patients, highlighting an
   urgent need to gain deep mechanistic insights into the comprehensive
   alterations associated with tumor occurrence and responses. Metabolic
   phenotypes have been reported to be involved in metabolism regulation
   within cancer progression. Stem-like glioma cells alter their metabolic
   process in order to sustain self-renewal and provide energy for the
   tumor hierarchy, uncovering potential cancer vulnerabilities that can
   be targeted with personalized therapy. In this study, we captured the
   static metabolic snapshots in BSG from HDs, as well as tracked the
   metabolic trajectory via dynamic metabolic snapshots throughout
   radiotherapy process.

   A highly detailed metabolic picture of a cross-sectional cohort with
   specific outcomes at a given point in time can provide a static view of
   that sample and aid in disease diagnosis and prognosis. We first built
   a diagnostic model using six selected metabolic biomarkers. We found
   that this model could accurately and invasively discriminate BSG
   patients from HDs with higher accuracy of 88.2% with AUC of 0.933,
   sensitivity of 94.7%, and specificity of 80%, compared with the
   accuracy of 82.5% of CSF ctDNA samples and 38% of plasma ctDNA^[180]5.
   The accuracy of this model for BSG diagnosis was also superior to those
   acquired from the traditional MRI information (accuracy of
   ~76%)^[181]57. While it is acknowledged that the presence of BSG would
   typically be confirmed by imaging modalities, such as MRI, it is
   essential to consider the global healthcare landscape, particularly in
   low-income countries where imaging facilities may not be uniformly
   available. The World Health Organization reports that less than 30% of
   low-income countries have adequate imaging facilities, leading to a
   substantial number of undiagnosed cases and delayed
   interventions^[182]58. BSG, especially in pediatric cases, exhibits
   atypical clinical presentations that include subtle mental signs like
   emotional and behavioral changes or acquired
   torticollis^[183]59,[184]60. These symptoms can be easily missed or
   misinterpreted, resulting in delays in diagnosis and treatment
   initiation^[185]61. Given the aggressive nature of BSG and its rapid
   progression, delays in the commencement of essential treatments, such
   as radiotherapy, can significantly impact patient outcomes and reduce
   survival periods.

   Subsequently, a prognostic prediction model was developed by employing
   a seven-feature panel. With this model, the calculated risk score
   successfully differentiated patients of BSG based on their respective
   prognoses, and the validity as an independent prognostic risk factor
   was confirmed through log-rank test. When compared to other prognostic
   risk factors (MRI imaging ring enhancement^[186]62 and
   genotyping^[187]15,[188]63,[189]64), the discrimination potential of
   the score was found to be superior (Supplementary Fig. [190]13a–c).
   Significantly, the H3K27M mutation demonstrated superior distinguishing
   ability between high- and low-risk groups compared to the risk scores
   via static metabolic snapshots (p = 0.031, log-rank test,
   Fig. [191]3d). Unlike molecular testing on tissue biopsies, risk scores
   from static metabolic snapshots were derived from serum samples,
   facilitating treatment monitoring through serial liquid biopsy.
   Furthermore, metabolic profiling using NPELDI MS technology offers
   rapid and cost-effective analysis compared to sequencing methodologies.
   Overall, regular monitoring of metabolic snapshots allows for prompt
   assessment of the patients’ treatment effectiveness and provides
   valuable support in clinical decision-making processes.

   Notably, both diagnostic and prognostic assays were built on a
   NPELDI-MS platform (see workflow in Supplementary Fig. [192]16),
   featuring little sample pretreatment (~min) and fast analytical speed
   (2 s per sample) for metabolic profiling of tracing serum sample (down
   to 100 nL). The typical platforms require complex derivatization or
   chromatographic separation prior to MS detection, to address the sample
   complexity and enrich the target metabolites from clinical biofluids
   (usually consuming 50–200 μL). The key is to introduce nanoparticles to
   selectively enhance the desorption and ionization efficiency of
   metabolites. Notably, coupled with throughput of 384 samples per chip
   can be achieved by microarray design automatically, indicating
   application potency for biomarker discovery especially suitable for
   high-throughput cohorts.

   While radiotherapy is the standard treatment for BSG, chemotherapy is
   often utilized in combination with radiotherapy for other brain tumors
   like GBMs. The metabolism of tumors is expected to undergo changes
   based on the chemoradiation regimens employed^[193]65. Importantly, we
   have previously observed significant alterations in carbohydrates and
   amino acids during the chemotherapy process for various cancer types
   such as gastric cancer, pancreatic cancer, and breast
   cancer^[194]29,[195]66. Thus, it is feasible to extend this framework
   to incorporate the combined use of chemotherapy and radiation in brain
   tumor treatment.

   The alteration in tumor volume directly reflects the growth and
   progression of the tumor. Metabolic reprogramming of tumor cells plays
   a critical role in the initiation, proliferation, and advancement of
   cancer. The behavior of cancer cells is regulated by both intrinsic
   cellular metabolism and the availability of metabolites in the tumor
   microenvironment^[196]67. Therefore, BRT metabolic profiles can serve
   as indicators of the growth and development status of tumors, enabling
   the prediction of tumor volume changes (Fig. [197]4c), a concept that
   is further supported by existing literature^[198]68,[199]69.

   Static snapshots of the blood provide accurate diagnosis and prognosis,
   monitoring measurements longitudinally enables the most precise
   evaluation of metabolic transitions throughout therapy. Although
   previous molecular profiling studies adopted a cross-sectional
   approach, the challenge remains in understanding how the circulating
   metabolites as a whole change, given the metabolic heterogeneity among
   different individuals^[200]70–[201]72. Relatively few studies have
   assessed the whole metabolites at many time points, hardly predicting
   the dynamic alteration through snapshots of its current state. Several
   studies have combined untargeted metabolomics and high-density sampling
   to develop prediction models for specific activities within sampling
   time points, such as peak oxygen consumption during acute physical
   activity or delivery time^[202]73,[203]74. Dynamic metabolic profiling
   for predicting follow-up activity is essential for better healthcare,
   but remains unexplored, for instance in predicting regrowth rhythmicity
   of remaining cancer cells after therapy^[204]75,[205]76.

   The use of serum samples as a less invasive alternative to traditional
   pathological molecular testing offers promising implications for the
   diagnosis and prognosis of tumors, particularly in
   challenging-to-biopsy brain tumors such as BSG. The non-invasive nature
   of serum metabolic testing yields benefits such as reduced patient
   anxiety, fewer postoperative complications, and decreased physical and
   mental burdens, fostering a positive doctor-patient relationship.
   Regular monitoring of metabolite levels enables a timely assessment of
   the patient’s condition and treatment efficacy. This approach allows
   for personalized treatment adjustments and the development of
   individualized dietary plans, leading to improved patient care and
   outcomes.

   Thanks to the non-invasive sampling and facile operation, our assay was
   further expanded to determine if serial sampling of serum could be used
   to monitor tumor metabolic dynamics by tracking response to therapy.
   Observational studies utilizing serology tests require a well-designed
   blood sampling interval that bridges the appropriate molecular assays
   with the outcomes of interest. For instance, weekly blood sampling
   during pregnancy reflects rapid metabolic changes in pregnant
   women^[206]74. Our designed scheme entailed a dense sequential blood
   sampling interval of three weeks, which is different from previous
   cohorts that had annual blood sampling for mortality evaluation. Eight
   metabolic clusters were determined with different metabolism patterns
   leading to distinct survival outcomes. As a proof-of-concept
   demonstration in clinical use, the actual OS can be predicted via
   static metabolic profiles, allowing the regimen to be adjusted timely
   via dynamic metabolic monitoring. This longitudinal cohort study
   depicted a programmed metabolic trajectory over the course of
   radiotherapy, filling the clinical gap related to cancer’s poor
   prognosis, including but not limited to BSG.

   Strikingly, we also performed further comparisons between diagnostic
   and prognostic panels to demonstrate the metabolic differences and
   similarities among HDs and BSG groups. Diagnostic biomarkers here were
   singular parameters that cannot be used to directly predict the
   probability associated with tumor development, due to the metabolic
   preferences with a tumor change during cancer