guest ::
| Home > Publications database > Unraveling glioblastoma diversity: Insights into methylation subtypes and spatial relationships. |
| Home > Publications database > Unraveling glioblastoma diversity: Insights into methylation subtypes and spatial relationships. |
| Journal Article | DKFZ-2024-01496 |
; ; ; ; ; ; ; ; ;
2024
Oxford University Press
Oxford
This record in other databases: 
Please use a persistent id in citations: doi:10.1093/noajnl/vdae112
Abstract: The purpose of this study was to elucidate the relationship between distinct brain regions and molecular subtypes in glioblastoma (GB), focusing on integrating modern statistical tools and molecular profiling to better understand the heterogeneity of Isocitrate Dehydrogenase wild-type (IDH-wt) gliomas.This retrospective study comprised 441 patients diagnosed with new IDH-wt glioma between 2009 and 2020 at Heidelberg University Hospital. The diagnostic process included preoperative magnetic resonance imaging and molecular characterization, encompassing IDH-status determination and subclassification, through DNA-methylation profiling. To discern and map distinct brain regions associated with specific methylation subtypes, a support-vector regression-based lesion-symptom mapping (SVR-LSM) was employed. Lesion maps were adjusted to 2 mm³ resolution. Significance was assessed with beta maps, using a threshold of P < .005, with 10 000 permutations and a cluster size minimum of 100 voxels.Of 441 initially screened glioma patients, 423 (95.9%) met the inclusion criteria. Following DNA-methylation profiling, patients were classified into RTK II (40.7%), MES (33.8%), RTK I (18%), and other methylation subclasses (7.6%). Between molecular subtypes, there was no difference in tumor volume. Using SVR-LSM, distinct brain regions correlated with each subclass were identified: MES subtypes were associated with left-hemispheric regions involving the superior temporal gyrus and insula cortex, RTK I with right frontal regions, and RTK II with 3 clusters in the left hemisphere.This study linked molecular diversity and spatial features in glioblastomas using SVR-LSM. Future studies should validate these findings in larger, independent cohorts to confirm the observed patterns.
Keyword(s): DNA methylation subtypes ; MRI ; glioblastoma ; support vector regression-based lesion-symptom mapping
; 
; 
; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Web of Science Core Collection
|
The record appears in these collections: |
PDF
PDF (PDFA)