Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

Faria, Andréia V.; Macedo, Fernando; Marsaioli, Anita J.; Ferreira, Márcia M. C.; Cendes, Fernando

doi:10.1590/s0100-879x2010007500146

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…For a clearer picture, Table includes the references relative to signal origin in neural lineages and in tumour cells as well as the intensities of the more relevant metabolite signals in the two GSC lines.…”

Section: Resultsmentioning

confidence: 99%

¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells

et al. 2013

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The metabolic profiles of glioblastoma stem-like cells (GSCs) growing in neurospheres were examined by (1)H NMR spectroscopy. Spectra of two GSC lines, labelled 1 and 83, from tumours close to the subventricular zone of the temporal lobe were studied in detail and compared with those of neural stem/progenitor cells from the adult olfactory bulb (OB-NPCs) and of the T98G glioblastoma cell line. In both GSCs, signals from myoinositol (Myo-I), UDP-hexosamines (UDP-Hex) and glycine indicated an astrocyte/glioma metabolism. For line 1, the presence of signals from N-acetyl aspartate, GABA and creatine pointed to a neuronal fingerprint. These metabolites were almost absent from line 83 spectra, whereas lipid signals, absent from normal neural lineages, were intense in line 83 spectra and remained low in those of line 1, irrespective of apoptotic fate. Spectra of OB-NPC cells displayed strong similarities with those from line 1, with low lipid signals and clearly detectable neuronal signals. In contrast, the spectral profile of line 83 was more similar to that of T98G, displaying high lipids and nearly complete absence of the neuronal markers. A mixed neural-astrocyte metabolic phenotype with a strong neuronal fingerprint was therefore found in line 1, while an astrocytic/glioma-like metabolism prevailed in line 83. We found a signal assigned to the amide proton of N-acetyl galactosamine in GSC lines and in OB-NPC spectra, whereas it was absent from those of T98G cells. This signal may be related to a stem-cell-specific protein glycosylation pattern and is therefore suggested as a marker of cell multipotency. Other GSC lines from patients with different clinical outcomes were then examined. Unsupervised analysis of spectral data from 13 lines yielded two clusters, with six lines resembling spectral features of line 1 and seven resembling those of line 83, suggesting that distinct metabolic phenotypes may be present in GSC lines.

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Section: Resultsmentioning

confidence: 99%

¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells

et al. 2013

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“…Previous studies have reported that low levels of myo-inositol are associated with higher aggressiveness of the glioma phenotype [ 37 , 38 ]. Kinoshita et al and Faria et al also found that inositol was lower in GBM as compared to astrocytoma grade II and III, while Wright and fellows found that the myo-inositol level in GBM where similar to the level in astrocytoma grade III but lower than in grade II [ 29 , 36 , 39 ]. We can confirm those findings in our study where we found that the level of myo-inositol was significantly lower in GBM as compared to oligodendrogliomas and that long survival patients from both tumor types have higher levels of myo-inositol than patients that die shortly after diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…The number of publications reporting on GABA in glioma is sparse and provides conflicting information. One study by Faria et al showed that GABA was detectable in low-grade astrocytomas and normal brain, however they failed to detect GABA in high grade tumors [ 29 ], while another study by Biachi et al found increased levels of GABA in GBM as compared to normal brain using microdialysis [ 30 ]. Another neurotransmitter, glutamate, is a substrate for GABA and an intermediate in the glutamine conversion to oxaloacetate in the amino acid, nucleotide and lipid synthesis [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information

et al. 2015

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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 (p 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.

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“…In metabolomics, PLS-based classification and PLS-discriminant analysis (PLS-DA) have been widely used to sharpen the separation between groups or observations. This is achieved by rotating PCs to maximize the separation between known classes, and to elucidate the variables that carry the class separating information [33,112-113]. Similarly to loading plots in PCA, S-plots visualize both the covariance and the correlation between metabolites and the modeled class designation.…”

Section: Data Analysis In Metabolomicsmentioning

confidence: 99%

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Sugimoto

Kawakami

Robert

et al. 2012

CBIO

275

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Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

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Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

Cited by 14 publications

References 39 publications

¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells

¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

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Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

Cited by 14 publications

References 39 publications

1H NMR detects different metabolic profiles in glioblastoma stem‐like cells

1H NMR detects different metabolic profiles in glioblastoma stem‐like cells

Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

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Product

Resources

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¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells

¹H NMR detects different metabolic profiles in glioblastoma stem‐like cells