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LINK . SPRINGER . COM {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Link.springer.com Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. External Links
  11. Analytics And Tracking
  12. Libraries
  13. CDN Services

We are analyzing https://link.springer.com/article/10.1007/s00401-012-1016-2.

Title:
MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status | Acta Neuropathologica
Description:
The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

  • Education
  • Science
  • Social Networks

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 7,626,182 visitors per month in the current month.

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How Does Link.springer.com Make Money? {💸}

We see no obvious way the site makes money.

The purpose of some websites isn't monetary gain; they're meant to inform, educate, or foster collaboration. Everyone has unique reasons for building websites. This could be an example. Link.springer.com could be secretly minting cash, but we can't detect the process.

Keywords {🔍}

methylation, mgmt, glioblastoma, model, probes, article, promoter, google, scholar, hmk, cpg, gene, classification, status, methylated, fig, prediction, supplementary, pubmed, cimp, mgmtstp, cas, glioma, patients, data, cancer, outcome, datasets, expression, samples, dna, analysis, based, grade, msp, cpgs, clinical, anaplastic, unmethylated, survival, island, iii, temozolomide, information, treated, dataset, values, predictive, cohort, tumors,

Topics {✒️}

edema/cellular invasion mri-phenotypes o6-methylguanine-dna methyltransferase t-glioma-ii/iii datasets basically real-time methylation-specific pcr dna damage o6-methylguanine t-glioma-ii/iii datasets o6-methylguanine methyltransferase classic methylation-specific pcr external data-sets suggests dna-repair gene mgmt methylation-specific pyrosequencing-based vb-glioma-iii samples analyzed jocelyne bloch chemo-radiotherapy-treated glioblastoma patients van den bent hm-27k dna methylation t-glioma-ii/iii t-glioma ii/iii parametric smirnov–kolmogorov test log-likelihood ratio test mgmt-stp27-based outcome prediction vb-glioma-iii data pairwise quantile-quantile representation expression-based glioblastoma subtypes hm-450k beadarray platform logit-transformed response fitted wald-based confidence intervals intriguingly distinct behavior article download pdf location-specific cpg methylation vb-glioma-iii dataset vb-glioma-iii datasets grade ii/iii glioma msp-based methylation classification hm-450k methylation data standard chemo-radiotherapy comprised hm-27k data dna methylation analysis hm-27k information consisted pierre-yves dietrich hm-27k data revealed cimp-negative patient subpopulation cimp-negative tumors exhibited roger stupp & monika dna methylome data ms-mlpa methylation calls methylation-specific pcr respective glioblastoma-derived spheres ms-pseq-based prediction methylation-specific pyrosequencing

Questions {❓}

  • Weller M, Stupp R, Reifenberger G et al (2010) MGMT promoter methylation in malignant gliomas: ready for personalized medicine?

Schema {🗺️}

WebPage:
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         headline:MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status
         description:The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
         datePublished:2012-07-19T00:00:00Z
         dateModified:2012-07-19T00:00:00Z
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         pageEnd:560
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            MGMT
            DNA methylation
            MSP
            Infinium methylation platform
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            Pathology
            Neurosciences
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                        name:Department of Neurology, University Hospital Zurich, Zurich, Switzerland
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                        type:PostalAddress
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ScholarlyArticle:
      headline:MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status
      description:The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
      datePublished:2012-07-19T00:00:00Z
      dateModified:2012-07-19T00:00:00Z
      pageStart:547
      pageEnd:560
      license:https://creativecommons.org/licenses/by/2.0
      sameAs:https://doi.org/10.1007/s00401-012-1016-2
      keywords:
         MGMT
         DNA methylation
         MSP
         Infinium methylation platform
         Prediction model
         Pathology
         Neurosciences
      image:
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            name:Pierre Bady
            affiliation:
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                  address:
                     name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
                  name:Swiss Institute for Bioinformatics
                  address:
                     name:Bioinformatics Core Facility, Swiss Institute for Bioinformatics, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
                  name:Lausanne University Hospital
                  address:
                     name:Département de Formation et de recherche, Lausanne University Hospital, Lausanne, Switzerland
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                  type:Organization
            type:Person
            name:Davide Sciuscio
            affiliation:
                  name:Lausanne University Hospital
                  address:
                     name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Annie-Claire Diserens
            affiliation:
                  name:Lausanne University Hospital
                  address:
                     name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Jocelyne Bloch
            affiliation:
                  name:Lausanne University Hospital
                  address:
                     name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Martin J. van den Bent
            affiliation:
                  name:Erasmus Medical Center
                  address:
                     name:Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
                     type:PostalAddress
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            name:Christine Marosi
            affiliation:
                  name:Medical University of Vienna
                  address:
                     name:Medical University of Vienna, Vienna, Austria
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Pierre-Yves Dietrich
            affiliation:
                  name:University Hospital Geneva
                  address:
                     name:University Hospital Geneva, Geneva, Switzerland
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Michael Weller
            affiliation:
                  name:University of Tübingen
                  address:
                     name:Department of Neurology, University of Tübingen, Tübingen, Germany
                     type:PostalAddress
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                  address:
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            name:Luigi Mariani
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                  name:Inselspital Berne
                  address:
                     name:Department of Neurosurgery, Inselspital Berne, Berne, Switzerland
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            name:Denis Lacombe
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                  address:
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            name:Mauro Delorenzi
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                  address:
                     name:Bioinformatics Core Facility, Swiss Institute for Bioinformatics, Lausanne, Switzerland
                     type:PostalAddress
                  type:Organization
                  name:Lausanne University Hospital
                  address:
                     name:Département de Formation et de recherche, Lausanne University Hospital, Lausanne, Switzerland
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                  name:ISREC-SV-EPFL
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                     name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
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            name:Monika E. Hegi
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      name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
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      name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
      name:Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
      name:National Center of Competence in Research Molecular Oncology, ISREC-SV-EPFL, Lausanne, Switzerland
      name:Department of Neurosurgery, Laboratory of Brain Tumor Biology and Genetics, Centre Hospitalier Universitaire Vaudois (CHUV BH19-110), Lausanne, Switzerland

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