Evidence map›Paper›PMID 42589345›Full record

ArticleInternational journal of molecular sciences2026

Integrated Proteomic and Metabolomic Analyses of Cerebrospinal Fluid from Pediatric Patients with Diffuse Intrinsic Pontine Glioma.

Yufan Chen, Yafei Wang, Yunkun Wang, Kun Zhang, Chenran Zhang

Abstract read
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Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yufan ChenDepartment of Pediatric Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.ORCID 0000-0002-4938-619X
Yafei WangDepartment of Pediatric Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Yunkun WangDepartment of Pediatric Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Kun ZhangShanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.ORCID 0000-0001-8759-7636
Chenran ZhangDepartment of Pediatric Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.

Funding

National Natural Science Foundation of China 82170798
6 · The paper itself

Abstract

Diffuse intrinsic pontine glioma (DIPG) is a rare and fatal pediatric brainstem malignancy for which effective treatment options are lacking. Cerebrospinal fluid (CSF) analysis can reveal intrinsic alterations and characteristic metabolic profiles of the tumor microenvironment. In this study, the proteome and metabolome of CSF from DIPG patients were comprehensively analyzed to identify potential biomarkers and the pathways involved. Functional annotation and pathway enrichment analyses were performed using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases. Bioinformatics methods were used to comprehensively analyze the proteomic and metabolomic results to identify key differentially expressed proteins, metabolites, and potential signaling pathways involved in DIPG. In total, 885 DEPs (differentially expressed proteins) were identified in cerebrospinal fluid from DIPG patients, of which 54 were upregulated and 831 were downregulated, primarily originating from the cytoplasm and cell membrane. Among the top 20 upregulated proteins, URB1 (nucleolar pre-ribosomal-associated protein 1) had the greatest statistical significance, while the remaining proteins were mostly immunoglobulin fragments. GO enrichment analysis revealed that the downregulated proteins were enriched primarily in cellular processes, metabolic processes, and binding functions. KEGG analysis revealed that upregulated proteins were significantly enriched in complement and coagulation cascades, whereas downregulated proteins were primarily associated with endocytosis and certain microbial infections. A total of 1372 metabolites were identified, of which 40 were differentially expressed: 24 were upregulated, and 16 were downregulated. Pathway analyses of the differentially expressed metabolites revealed that they were primarily related to purine metabolism and tyrosine metabolism. The multiomics analysis revealed that purine metabolism is particularly important in DIPG.

Indexed as

Brain Stem NeoplasmsDiffuse Intrinsic Pontine GliomaMetabolomeMetabolomicsProteomeProteomicsBiomarkers, TumorChildChild, PreschoolComputational BiologyFemaleGene Expression Regulation, NeoplasticHumansMaleMultiomicsBiomarkers, TumorProteomeCSFDIPGimmunoglobulinsmicrobial infectionsmultiomicspurine metabolism

Identifiers

PMID42589345
PMCPMC13467317

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.