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Updated: Aug 17, 2026

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR
Published on: January 22, 2014
Metabolomic profile of cerebrospinal fluid from patients with diffuse gliomas
Nora Möhn1, Harold F Hounchonou2, Sandra Nay1
1Department of Neurology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany.
Background:
Diffuse gliomas are among the most common brain tumors in adults and are associated with a dismal prognosis, especially in patients with glioblastoma. To date, tumor tissue acquisition is mandatory for conclusive diagnosis and therapeutic decision-making. In this study, we aimed to identify possible diagnostic and prognostic biomarkers in cerebrospinal fluid (CSF) and blood.
Methods:
During glioma surgery at our institution, CSF and blood samples were collected from patients. Subsequently, targeted metabolomics analysis was used to detect and quantify circulating metabolites. The metabolome profiles of glioma patients were compared with those of patients in a control group who had undergone neurosurgery for other entities, such as nonglial tumors or hydrocephalus, and were correlated with established glioma diagnostic molecular markers.
Results:
In this study, a total of 30 glioma patients were included, along with a control group of 21 patients without glioma. Serum metabolomic analysis did not detect any significant differences between the groups, whereas CSF-metabolome analysis revealed increased levels of six metabolites in glioma patients. Among these, the most pronounced differences were found for the biogenic amine putrescine (p = 0.00005). p-Cresol sulfate was identified as a potential CSF marker for determining isocitrate dehydrogenase (IDH) status in glioma patients (p = 0.0037).
Conclusion:
CSF-metabolome profiling, unlike blood profiling, shows promise as a diagnostic tool for glioma patients with the potential to assign molecular subtypes. The next step will involve a larger multicenter study to validate these findings, with the ultimate objective of integrating CSF metabolomics analysis into clinical practice.
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