Proteomic profiling of gliomas unveils immune and metabolism-driven subtypes with implications for anti-nucleotide metabolism therapy

Abstract

Gliomas exhibit high heterogeneity and poor prognosis. Despite substantial progress has been made at the genomic and transcriptomic levels, comprehensive proteomic characterization and its implications remain largely unexplored. In this study, we perform proteomic profiling of gliomas using 343 formalin-fixed and paraffin-embedded tumor samples and 53 normal-appearing brain samples from 188 patients, integrating these data with genomic panel information and clinical outcomes. The proteomic analysis uncovers two distinct subgroups: Subgroup 1, the metabolic neural subgroup, enriched in metabolic enzymes and neurotransmitter receptor proteins, and Subgroup 2, the immune subgroup, marked by upregulation of immune and inflammatory proteins. These proteomic subgroups show significant differences in prognosis, tumorigenesis, microenvironment dysregulation, and potential therapeutics, highlighting the critical roles of metabolic and immune processes in glioma biology and patient outcomes. Through a detailed investigation of metabolic pathways guided by our proteomic findings, dihydropyrimidine dehydrogenase (DPYD) and thymidine phosphorylase (TYMP) emerge as potential prognostic biomarkers linked to the reprogramming of nucleotide metabolism. Functional validation in patient-derived glioma stem cells and animal models highlights nucleotide metabolism as a promising therapy target for gliomas. This integrated multi-omics analysis introduces a proteomic classification for gliomas and identifies DPYD and TYMP as key metabolic biomarkers, offering insights into glioma pathogenesis and potential treatment strategies.

Fig. 1. Overview of glioma proteomics analyses.

A comprehensive proteomic analysis is performed on a cohort of 188 glioma patients, classified by WHO grades, including 343 tumor samples and 53 normal-appearing brain samples. Tissue specimens (0.5 × 0.5 mm) are extracted from formalin-fixed paraffin-embedded (FFPE) samples for proteomic analysis. Mouse liver samples are used as quality controls, alongside pooled samples for LC–MS/MS analysis. The analysis is conducted in 27 randomly assigned batches. The study integrates glioma genetic data, proteomic profiles, and clinical information, quantifying 8561 proteins for downstream analyses. Proteomic profiling reveals two distinct subgroups: S-Mn (metabolic-neural) and S-Im (immune), which show significant differences in prognosis within both the glioma cohort and an independent validation cohort. Metabolic pathways are explored, leading to the identification of potential biomarkers, which are validated using cellular and mouse models to confirm their accuracy and relevance. Cartoons are created with BioRender.com.

Fig. 2. Proteomic features vary among glioma clinical classifications.

A Heatmap illustrates 570 differentially expressed proteins between 343 tumor samples and 53 normal-appearing brain samples (Two-sided unpaired Welch’s t-test, B–H adjusted p < 0.001, fold change > 2). B Pathways are significantly enriched by IPA using the 570 differentially expressed proteins between tumor and normal-appearing brain samples (right-tailed Fisher’s Exact Test, p < 0.01). C Volcano plot shows 570 differentially expressed proteins between 343 tumor and 53 normal-appearing brain samples (Two-sided unpaired Welch’s t-test, B–H adjusted p < 0.001, fold change > 2). Immune-related and metabolism-related proteins are highlighted in red and purple, respectively (28 immune-related, 51 metabolism-related). D Manhattan plot displays 1047 differentially expressed proteins across four grades (one-way ANOVA test, B–H adjusted p < 0.001). Proteins associated with immunity and metabolism are marked in red and purple, respectively. E Enriched immune and metabolic processes, including nucleotide synthesis, antigen presentation, interferon signaling, and TGF-β pathways, are presented. Cartoons are created with BioRender.com. Statistical details for the relevant proteins are provided in Supplementary Data 3 (Welch’s t-test, B–H adjusted p < 0.001, fold change > 2). Source data are provided as a Source Data file.

Fig. 3. Proteomic metabolic-neural and immune subtypes of glioma could predict clinical outcomes.

A Heatmap shows two proteomic subtypes within our study cohort, based on 355 differentially expressed proteins between tumor and normal-appearing brain samples (proteins with missing rates over 20% were removed, missing values were imputed by substituting them with 80% of the minimum value, Two-sided unpaired Welch’s t-test, B–H adjusted P value < 0.05, fold change > 3). Subgroup 1 enriches both metabolic processes and neurotransmitter receptor activity proteins, designated as the metabolic-neural subgroup (S-Mn). Subgroup 2 exhibits significant upregulation of immune and inflammatory proteins, designated as the immune subgroup (S-Im) (two-sided unpaired Welch’s t-test, B–H adjusted p < 0.05, fold change > 3). Pathways are significantly enriched by IPA using the proteins in S-Mn (upper) and S-Im (lower) groups, with key proteins listed. B Kaplan–Meier survival curve with 95% confidence interval (CI) from our glioma cohort shows better clinical outcomes for S-Mn (N = 133) subtype compared to the S-Im subtype (N = 176) (log-rank test, p < 0.0001). C PCA analysis of the glioma proteome effectively stratifies the two subtypes. D Heatmap of TCGA gene expression data validates our two-subtype proteomic classification using the same 355 differentially expressed proteins as in (A). E Kaplan–Meier survival curve with 95% CI based on TCGA data shows better OS for the S-Mn subtype (n = 267) and poor OS for the S-Im subtype (N = 308) (log-rank test, p < 0.0001). F Heatmap of proteomic data from NC 2022 displays the top 2000 differentially expressed proteins, classified by our proteomic subtype. G Kaplan–Meier curves with 95% CI reveal a better prognosis for the S-Mn group (N = 36) compared to the S-Im group (N = 151) based on NC 2022 data (log-rank test, p < 0.0001). Source data are provided as a Source Data file.

Fig. 4. Pyrimidine metabolism associate with glioma patients' survival.

A Heatmap shows 18 different expressed proteins between the long-survival (N = 48) and short-survival (N = 41) groups, filtered through the methods shown in Supplementary Fig. S8B. B Kaplan–Meier survival curve with 95% CI (high-risk group, N = 41; low-risk group, N = 48) illustrates the risk-scoring model based on these 18 proteins (log-rank test, p < 0.0001). C Pathway enrichment analysis indicates that pyrimidine metabolism is predominantly involved. D Kaplan–Meier survival curve with 95% CI using TCGA data (high-risk group, N = 288; low-risk group N = 287) validates the risk-scoring model (log-rank test, p < 0.0001). Source data are provided as a Source Data file.

Fig. 5. DPYD is required for GSC proliferation and self-renewal.

A IHC validation of dihydropyrimidine dehydrogenase (DPYD) expression based on proteomic profiling. The optical density of IHC staining is measured quantitatively, confirming the different expression between the two groups (high group, N = 21; low group, N = 21; Student’s t-test, p < 0.0001; scale bar = 50 μm). B DPYD mRNA levels and protein levels in T4121 and Mes28 cells after knockdown show an efficiency greater than 50% (mRNA, mean ± SEM, N = 4; Student’s t-test; in T4121, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001; in Mes28, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001). C Proliferation assay demonstrates that targeting DPYD impairs proliferation, as assessed by cell number on day 3 (mean ± SEM, N = 4, Student’s t-test; in T4121, shCont vs shDPYD-1, p = 0.0104 and shCont vs shDPYD-2, p = 0.0198; in Mes28, shCont vs shDPYD-1, p = 0.0406 and shCont vs shDPYD-2, p = 0.0599). GSC proliferation is significantly inhibited on day 5 (mean ± SEM, N = 4, Student’s t-test; in T4121, shCont vs shDPYD-1, p = 0.0001 and shCont vs shDPYD-2, p < 0.0001; in Mes28, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p = 0.0014). D In vitro limiting dilution assays (analysis was performed using software available at http://bioinf.wehi.edu.au/software/elda with simple line regression; in T4121, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p = 0.0001; in Mes28, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001) and (E, G) tumorsphere formation reveal impaired self-renewal of DPYD knockdown GSCs. Sphere number (E, mean ± SEM, N = 8, Student’s t-test; in T4121, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001; in Mes28, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001) and size (G, scale bar = 20 μm) are significantly decreased in DPYD knockdown GSCs. H, F Immunofluorescence shows increased phospho-γH2A.X foci in the nucleus of GSCs after DPYD knockdown, indicating severe DNA damage (F, mean ± SEM, N ≥ 4, Student’s t-test in foci > 15 as positive; in T4121, shCont vs shDPYD-1, p < 0.0001 and shCont vs shDPYD-2, p < 0.0001; in Mes28, shCont vs shDPYD-1, p = 0.0009 and shCont vs shDPYD-2, p = 0.0028). The percentage of phospho-γH2A.X positive cell percentage is calculated, indicating elevated DNA damage after DPYD knockdown. I Western blot shows increased protein levels of phospho-γH2A.X and Cleaved Caspase-3 (CC3) proteins following DPYD knockdown. J Knockdown of DPYD in T4121 (each group, N = 8) and Mes28 (each group, N = 6) significantly increases mouse survival (log-rank test; in T4121, shCont vs shDPYD-1, p = 0.0171 and shCont vs shDPYD-2, p = 0.0041; in Mes28, shCont vs shDPYD-1, p = 0.0005 and shCont vs shDPYD-2, p = 0.0024). H&E staining of mouse brain shows that DPYD inhibition suppresses GSC growth (N = 3, scale bar = 1 mm). K An external validation of another cohort of glioma patients (N = 43) confirms differential expression by IHC, and the Kaplan–Meier survival curve shows poor prognosis in the high DYPD expression group (log-rank test, p < 0.0001; scale bar = 100 μm). Source data are provided as a Source Data file.