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  • 1.
    Mörén, Lina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bergenheim, A Tommy
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Ghasimi, Soma
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Brännström, Thomas
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Johansson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information2015In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 5, no 3, 502-520 p.Article in journal (Refereed)
    Abstract [en]

    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 (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 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 (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 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.

  • 2.
    Thysell, Elin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Chorell, Elin
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Svensson, Michael
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Sports Medicine.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Validated and predictive processing of gas chromatography-mass spectra screening studies, diagnostics and metabolite pattern verification2012In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 2, no 4, 796-817 p.Article in journal (Refereed)
    Abstract [en]

    The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

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