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Mörén, Lina
Publications (9 of 9) Show all publications
Mörén, L., Perryman, R., Crook, T., Langer, J. K., Oneill, K., Syed, N. & Antti, H. (2018). Metabolomic profiling identifies distinct phenotypes for ASS1 positive and negative GBM. BMC Cancer, 18, Article ID 167.
Open this publication in new window or tab >>Metabolomic profiling identifies distinct phenotypes for ASS1 positive and negative GBM
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2018 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 18, article id 167Article in journal (Refereed) Published
Abstract [en]

Background: Tumour cells have a high demand for arginine. However, a subset of glioblastomas has a defect in the arginine biosynthetic pathway due to epigenetic silencing of the rate limiting enzyme argininosuccinate synthetase (ASS1). These tumours are auxotrophic for arginine and susceptible to the arginine degrading enzyme, pegylated arginine deiminase (ADI-PEG20). Moreover, ASS1 deficient GBM have a worse prognosis compared to ASS1 positive tumours. Since altered tumour metabolism is one of the hallmarks of cancer we were interested to determine if these two subtypes exhibited different metabolic profiles that could allow for their non-invasive detection as well as unveil additional novel therapeutic opportunities.

Methods: We looked for basal metabolic differences using one and two-dimensional gas chromatography-time-of-flight mass spectrometry (1D/2DGC-TOFMS) followed by targeted analysis of 29 amino acids using liquid chromatography-time-of-flight mass spectrometry (LC-TOFMS). We also looked for differences upon arginine deprivation in a single ASS1 negative and positive cell line (SNB19 and U87 respectively). The acquired data was evaluated by chemometric based bioinformatic methods.

Results: Orthogonal partial least squares-discriminant analysis (OPLS-DA) of both the 1D and 2D GC-TOFMS data revealed significant systematic difference in metabolites between the two subgroups with ASS1 positive cells generally exhibiting an overall elevation of identified metabolites, including those involved in the arginine biosynthetic pathway. Pathway and network analysis of the metabolite profile show that ASS1 negative cells have altered arginine and citrulline metabolism as well as altered amino acid metabolism. As expected, we observed significant metabolite perturbations in ASS negative cells in response to ADI-PEG20 treatment.

Conclusions: This study has highlighted significant differences in the metabolome of ASS1 negative and positive GBM which warrants further study to determine their diagnostic and therapeutic potential for the treatment of this devastating disease.

Place, publisher, year, edition, pages
BioMed Central, 2018
Keywords
Glioblastoma, Epigenetics, ASS1, Arginine, ADI-PEG20, Metabolomics, Chemometrics
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-145374 (URN)10.1186/s12885-018-4040-3 (DOI)000424776100003 ()29422017 (PubMedID)
Note

Errata: Mörén L., Perryman R., Crook T., Langer J. K., Oneill K., Syed N., Antti H. Correction to: Metabolomic profilingidentifies distinct phenotypes for ASS1positive and negative GBM. BMC Cancer. 2018;18:268. DOI: 10.1186/s12885-018-4128-9

Available from: 2018-03-09 Created: 2018-03-09 Last updated: 2018-06-09Bibliographically approved
Mörén, L., Wibom, C., Bergström, P., Johansson, M., Antti, H. & Bergenheim, A. T. (2016). Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas. Radiation Oncology, 11, Article ID 51.
Open this publication in new window or tab >>Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas
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2016 (English)In: Radiation Oncology, ISSN 1748-717X, E-ISSN 1748-717X, Vol. 11, article id 51Article in journal (Refereed) Published
Abstract [en]

Background: Glioblastomas progress rapidly making response evaluation using MRI insufficient since treatment effects are not detectable until months after initiation of treatment. Thus, there is a strong need for supplementary biomarkers that could provide reliable and early assessment of treatment efficacy. Analysis of alterations in the metabolome may be a source for identification of new biomarker patterns harboring predictive information. Ideally, the biomarkers should be found within an easily accessible compartment such as the blood. Method: Using gas-chromatographic-time-of-flight-mass spectroscopy we have analyzed serum samples from 11 patients with glioblastoma during the initial phase of radiotherapy. Fasting serum samples were collected at admittance, on the same day as, but before first treatment and in the morning after the second and fifth dose of radiation. The acquired data was analyzed and evaluated by chemometrics based bioinformatics methods. Our findings were compared and discussed in relation to previous data from microdialysis in tumor tissue, i.e. the extracellular compartment, from the same patients. Results: We found a significant change in metabolite pattern in serum comparing samples taken before radiotherapy to samples taken during early radiotherapy. In all, 68 metabolites were lowered in concentration following treatment while 16 metabolites were elevated in concentration. All detected and identified amino acids and fatty acids together with myo-inositol, creatinine, and urea were among the metabolites that decreased in concentration during treatment, while citric acid was among the metabolites that increased in concentration. Furthermore, when comparing results from the serum analysis with findings in tumor extracellular fluid we found a common change in metabolite patterns in both compartments on an individual patient level. On an individual metabolite level similar changes in ornithine, tyrosine and urea were detected. However, in serum, glutamine and glutamate were lowered after treatment while being elevated in the tumor extracellular fluid. Conclusion: Cross-validated multivariate statistical models verified that the serum metabolome was significantly changed in relation to radiation in a similar pattern to earlier findings in tumor tissue. However, all individual changes in tissue did not translate into changes in serum. Our study indicates that serum metabolomics could be of value to investigate as a potential marker for assessing early response to radiotherapy in malignant glioma.

Place, publisher, year, edition, pages
BioMed Central, 2016
Keywords
Glioblastoma, Radiation therapy, Treatment response, Metabolomics, Chemometrics
National Category
Cancer and Oncology Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-119631 (URN)10.1186/s13014-016-0626-6 (DOI)000373185900001 ()27039175 (PubMedID)
Available from: 2016-05-20 Created: 2016-04-25 Last updated: 2018-06-07Bibliographically approved
Björkblom, B., Wibom, C., Jonsson, P., Mörén, L., Andersson, U., Johannesen, T. B., . . . Melin, B. (2016). Metabolomic screening of pre-diagnostic serum samples identifies association between alpha- and gamma-tocopherols and glioblastoma risk. OncoTarget, 7(24), 37043-37053
Open this publication in new window or tab >>Metabolomic screening of pre-diagnostic serum samples identifies association between alpha- and gamma-tocopherols and glioblastoma risk
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2016 (English)In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 7, no 24, p. 37043-37053Article in journal (Refereed) Published
Abstract [en]

Glioblastoma is associated with poor prognosis with a median survival of one year. High doses of ionizing radiation is the only established exogenous risk factor. To explore new potential biological risk factors for glioblastoma, we investigated alterations in metabolite concentrations in pre-diagnosed serum samples from glioblastoma patients diagnosed up to 22 years after sample collection, and undiseased controls. The study points out a latent biomarker for future glioblastoma consisting of nine metabolites (gamma-tocopherol, alpha-tocopherol, erythritol, erythronic acid, myo-inositol, cystine, 2-keto-L-gluconic acid, hypoxanthine and xanthine) involved in antioxidant metabolism. We detected significantly higher serum concentrations of alpha-tocopherol (p=0.0018) and gamma-tocopherol (p=0.0009) in future glioblastoma cases. Compared to their matched controls, the cases showed a significant average fold increase of alpha- and gamma-tocopherol levels: 1.2 for alpha-T (p=0.018) and 1.6 for gamma-T (p=0.003). These tocopherol levels were associated with a glioblastoma odds ratio of 1.7 (alpha-T, 95% CI: 1.0-3.0) and 2.1 (gamma-T, 95% CI: 1.2-3.8). Our exploratory metabolomics study detected elevated serum levels of a panel of molecules with antioxidant properties as well as oxidative stress generated compounds. Additional studies are necessary to confirm the association between the observed serum metabolite pattern and future glioblastoma development.

Keywords
population-based, serum metabolite, vitamin E, antioxidants, brain tumor
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-123973 (URN)10.18632/oncotarget.9242 (DOI)000377756800119 ()
Available from: 2016-11-15 Created: 2016-07-07 Last updated: 2018-06-09Bibliographically approved
Sjöberg, R. L., Bergenheim, T., Mörén, L., Antti, H., Lindgren, C., Naredi, S. & Lindvall, P. (2015). Blood Metabolomic Predictors of 1-Year Outcome in Subarachnoid Hemorrhage. Neurocritical Care, 23(2), 225-232
Open this publication in new window or tab >>Blood Metabolomic Predictors of 1-Year Outcome in Subarachnoid Hemorrhage
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2015 (English)In: Neurocritical Care, ISSN 1541-6933, E-ISSN 1556-0961, Vol. 23, no 2, p. 225-232Article in journal (Refereed) Published
Abstract [en]

Delayed neurological deficit (DND) is the most important cause of morbidity and mortality in patients with subarachnoid hemorrhage (SAH) whose aneurysms have been secured. However, the methods currently used to predict the development of DND, such as trans-cranial Doppler or levels biochemical markers in blood and cerebrospinal fluid are not very accurate. Venous blood was drawn from 50 patients with SAH, admitted to the neurosurgical department UmeAyen University Hospital, at day 1-3 and day 7 after the bleed. The clinical status of the patients was followed up approximately 1 year after this episode and classified according to the Glasgow Outcome Score (GOS). Results showed considerable differences in blood metabolomic patterns between day 1-3 and 7 after the hemorrhage. Fifty-six out of 98 metabolites could be identified from our in-house library and 17 of these metabolites changed significantly from day 1-3 to 7 after the bleed. One of these, myo-inositol, was predictive of clinical outcome even after correction for multiple testing. An estimation of the diagnostic accuracy of high levels of this substance in predicting good outcome (GOS 4-5) yielded a sensitivity of .763 and a specificity of .5 at the optimal cut off point. SAH is an event with a profound effect on blood metabolomics profiles. Myo-inositol might be an interesting compound for future study to focus on in the search for metabolic markers in venous blood of delayed neurological deterioration in SAH patients.

Place, publisher, year, edition, pages
Springer, 2015
Keywords
Delayed neurological deficit, Myo-inositol, Metabolomics, Subarachnoid hemorrhage, Vasospasm, nous blood
National Category
Neurosciences
Identifiers
urn:nbn:se:umu:diva-109362 (URN)10.1007/s12028-014-0089-2 (DOI)000360700700012 ()25667130 (PubMedID)
Available from: 2015-09-30 Created: 2015-09-25 Last updated: 2018-06-07Bibliographically approved
Mörén, L., Bergenheim, A. T., Ghasimi, S., Brännström, T., Johansson, M. & Antti, H. (2015). Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites, 5(3), 502-520
Open this publication in new window or tab >>Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information
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2015 (English)In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 5, no 3, p. 502-520Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2015
Keywords
glioma, diagnosis, prognosis, blood, tumor, metabolomics, chemometrics, latent biomarkers
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-111307 (URN)10.3390/metabo5030502 (DOI)000363208200007 ()26389964 (PubMedID)
Available from: 2015-11-13 Created: 2015-11-13 Last updated: 2018-06-07Bibliographically approved
Mörén, L. (2015). Metabolomics and proteomics studies of brain tumors: a chemometric bioinformatics approach. (Doctoral dissertation). Umeå: Umeå Universitet
Open this publication in new window or tab >>Metabolomics and proteomics studies of brain tumors: a chemometric bioinformatics approach
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The WHO classification of brain tumors is based on histological features and the aggressiveness of the tumor is classified from grade I to IV, where grade IV is the most aggressive. Today, the correlation between prognosis and tumor grade is the most important component in tumor classification. High grade gliomas, glioblastomas, are associated with poor prognosis and a median survival of 14 months including all available treatments. Low grade meningiomas, usually benign grade I tumors, are in most cases cured by surgical resection. However despite their benign appearance grade I meningiomas can, without any histopathological signs, in some cases develop bone invasive growth and become lethal. Thus, it is necessary to improve conventional treatment modalities, develop new treatment strategies and improve the knowledge regarding the basic pathophysiology in the classification and treatment of brain tumors.

In this thesis, both proteomics and metabolomics have been applied in the search for biomarkers or biomarker patterns in two different types of brain tumors, gliomas and meningiomas. Proteomic studies were carried out mainly by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS). In one of the studies, isobaric tags for relative and absolute quantitation (iTRAQ) labeling in combination with high-performance liquid chromatography (HPLC) was used for protein detection and identification. For metabolomics, gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) has been the main platform used throughout this work for generation of robust global metabolite profiles in tissue, blood and cell cultures. To deal with the complexity of the generated data, and to be able to extract relevant biomarker patters or latent biomarkers, for interpretation, prediction and prognosis, bioinformatic strategies based on chemometrics were applied throughout the studies of the thesis.

In summary, we detected differentiating protein profiles between invasive and non-invasive meningiomas, in both fibrous and meningothelial tumors. Furthermore, in a different study we discovered treatment induce protein pattern changes in a rat glioma model treated with an angiogenesis inhibitor. We identified a cluster of proteins linked to angiogenesis. One of those proteins, HSP90, was found elevated in relation to treatment in tumors, following ELISA validation. An interesting observation in a separate study was that it was possible to detect metabolite pattern changes in the serum metabolome, as an effect of treatment with radiotherapy, and that these pattern changes differed between different patients, highlighting a possibility for monitoring individual treatment response.  In the fourth study of this work, we investigated tissue and serum from glioma patients that revealed differences in the metabolome between glioblastoma and oligodendroglioma, as well as between oligodendroglioma grade II and grade III. In addition, we discovered metabolite patterns associated to survival in both glioblastoma and oligodendroglioma. In our final work, we identified metabolite pattern differences between cell lines from a subgroup of glioblastomas lacking argininosuccinate synthetase (ASS1) expression, (ASS1 negative glioblastomas), making them auxotrophic for arginine, a metabolite required for tumor growth and proliferation, as compared to glioblastomas with normal ASS1 expression (ASS1 positive). From the identified metabolite pattern differences we could verify the hypothesized alterations in the arginine biosynthetic pathway. We also identified additional interesting metabolites that may provide clues for future diagnostics and treatments. Finally, we were able to verify the specific treatment effect of ASS1 negative cells by means of arginine deprivation on a metabolic level.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2015. p. 60
Keywords
glioblastoma, glioma, meningioma, metabolomics, proteomics, mass-spectrometry
National Category
Other Basic Medicine
Research subject
biological chemistry
Identifiers
urn:nbn:se:umu:diva-111309 (URN)978-91-7601-354-0 (ISBN)
Public defence
2015-12-11, KBC-huset, KB3B1, Umeå universitet, Umeå, 13:00 (Swedish)
Opponent
Supervisors
Available from: 2015-11-20 Created: 2015-11-13 Last updated: 2018-06-07Bibliographically approved
Mörén, L., Johansson, M., Bergenheim, T. & Antti, H. (2012). Metabolomic profiling of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Paper presented at 17th Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO), NOV 15-18, 2012, Washington, DC. Neuro-Oncology, 14(Suppl. 6), 96-96, Article ID OM-24.
Open this publication in new window or tab >>Metabolomic profiling of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information
2012 (English)In: Neuro-Oncology, ISSN 1522-8517, E-ISSN 1523-5866, Vol. 14, no Suppl. 6, p. 96-96, article id OM-24Article in journal, Meeting abstract (Other academic) Published
Abstract [en]

High-grade glioma is the most common brain tumor in adults, and the prognosis for patients diagnosed with this type of cancer is still poor. The biological behavior of the tumors is correlated to the classification and the World Health Organization (WHO) grading system, in which the grading reflects the increased aggressiveness. The classification system has been developed and improved over the years, but there are still problems with possible clinical implications. The histological features are not always easy to interpret, and diagnosis relies partly on personal experience of the neuropathologist. The most important component in the classification is the correlation between tumor grade and prognosis; however, the clinical reality shows a large variation in the survival of patients with glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers to obtain a more reliable classification of glioma tumors and also prognostic markers. We have performed a metabolomic profiling study of 81 tissue samples and 96 corresponding serum samples from patients with different glioma diagnoses (glioblastoma or oligodendroglioma) and grades (WHO grades II, IIIs and IV). The samples were analyzed by a global screening strategy using gas chromatography/time of flight mass spectrometry (GC/TOFMS). The acquired data were analyzed and evaluated by chemometrics-based bioinformatics methods in search for metabolite patterns of clinical relevance. We found metabolite patterns in both tissue and serum that distinguished glioblastomas from oligodendrogliomas and oligodendroglioma grade II from oligodendroglioma grade III. Interestingly, we also found metabolites elevated (eg, glycerol-3-phoshate, myo-inositol, ribitol, and fructose) and decreased (eg, octadecanoic acid and maltose) in glioblastoma patients that were associated with long survival (>3 years). Metabolite patterns associated with survival were also found in patients diagnosed with oligodendroglioma. These findings indicate that metabolomic profiling of glioma tissue and serum may be a valuable tool in future characterization of malignant glioma.

Place, publisher, year, edition, pages
Oxford University Press, 2012
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-63037 (URN)10.1093/neuonc/nos231 (DOI)000310971300383 ()
Conference
17th Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO), NOV 15-18, 2012, Washington, DC
Note

LAB–OMICS AND PROGNOSTIC MARKERS

Available from: 2012-12-28 Created: 2012-12-27 Last updated: 2018-06-08Bibliographically approved
Wibom, C., Surowiec, I., Mörén, L., Bergström, P., Johansson, M., Antti, H. & Bergenheim, A. T. (2010). Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study. Journal of Proteome Research, 9(6), 2909-2919
Open this publication in new window or tab >>Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study
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2010 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 9, no 6, p. 2909-2919Article in journal (Refereed) Published
Abstract [en]

We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2010
Keywords
chemometrics, gas chromatography-mass spectrometry, glioblastoma, metabolomics, predictive metabolomics, radiotherapy, treatment response
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-25668 (URN)10.1021/pr901088r (DOI)000278243300011 ()20302353 (PubMedID)
Available from: 2009-08-27 Created: 2009-08-27 Last updated: 2018-06-08Bibliographically approved
Wibom, C., Mörén, L., Aarhus, M., Knappskog, P., Lund-Johansen, M., Antti, H. & Bergenheim, A. T. (2009). Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype. Journal of Neuro-Oncology, 94(3), 321-331
Open this publication in new window or tab >>Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype
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2009 (English)In: Journal of Neuro-Oncology, ISSN 0167-594X, E-ISSN 1573-7373, Vol. 94, no 3, p. 321-331Article in journal (Refereed) Published
Abstract [en]

Meningiomas of WHO grade I can usually be cured by surgical resection. However, some tumors may, despite their benign appearance, display invasive growth behavior. These tumors constitute a difficult clinical problem to handle. By histology alone, bone invasive meningiomas may be indistinguishable from their noninvasive counterparts. In this study we have examined the protein spectra in a series of meningiomas in search of protein expression patterns that may distinguish between bone invasive and noninvasive meningiomas. Tumor tissue from 13 patients with fibrous (6 invasive and 7 noninvasive) and 29 with meningothelial (10 invasive and 19 noninvasive) grade I meningiomas were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI). Multivariate statistical methods were applied for data analyses. Comparing the protein spectra from invasive and noninvasive fibrous meningioma we found 22 peaks whose intensities were significantly different between the two groups (P < 0.001). Based on the expression pattern of these peaks we were able to perfectly separate the two entities (area under ROC curve = 1.0). In meningothelial meningioma the same comparison yielded six significantly differentially expressed peaks (P < 0.001), which to a large degree separated the invasive from noninvasive tissue (area under ROC curve = 0.873). By analyzing the protein spectra in benign meningiomas we could differentiate between invasive and noninvasive growth behavior in both fibrous and meningothelial meningiomas of grade I. A possibility for early identification of invasive grade I meningiomas may have a strong influence on the follow-up policy and the issue of early or late radiotherapy.

Place, publisher, year, edition, pages
Springer Netherlands, 2009
Keywords
fibrous, invasive, marker, meningioma, meningothelial, multivariate statistical analysis, proteomics
National Category
Neurosciences
Identifiers
urn:nbn:se:umu:diva-22131 (URN)10.1007/s11060-009-9865-9 (DOI)19350207 (PubMedID)
Available from: 2009-04-24 Created: 2009-04-24 Last updated: 2018-06-08Bibliographically approved
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