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Development, validation, and clinical utility of a novel methylation classifier for recurrence risk prediction in meningiomas [dataset 1]

ABSTRACT: Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence but has limitations as a prognostic tool. Recent work has shown the value of DNA methylation, transcriptomic, and copy number data for identifying groups of tumors which have distinct biological signatures and predicting recurrence risk. Here, we describe development, clinical validation, and implementation of a methylation classifier based on k-means clustering for prognostic stratification of meningiomas. Previously published work has validated the concept of meningioma prognostic stratification through DNA methylation data, but our system is unique in that it is the first clinically validated classifier which identifies risk groups based exclusively on DNA methylation signatures. This work has the potential to improve diagnostic workup, recurrence risk prediction, and clinical management of meningiomas.

ORGANISM(S): Homo sapiens

PROVIDER: GSE292325 | GEO | 2026/01/12

REPOSITORIES: GEO

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Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates

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Development, validation, and clinical utility of a novel methylation classifier for recurrence risk prediction in meningiomas [dataset 1]

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