Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However,
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Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (ptumor
= 2.46 × 10−8
= 1.3 × 10−5
) and oligodendroglioma grade II from oligodendroglioma grade III (ptumor
= 0.01, pserum
= 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (ptumor
= 0.006, pserum
= 0.004; AUROCCtumor
= 0.846 (0.647–1.000), AUROCCserum
= 0.958 (0.870–1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (ptumor
= 0.01, pserum
= 0.001; AUROCCtumor
= 1 (1.000–1.000), AUROCCserum
= 1 (1.000–1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.