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Updated: May 5, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
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Radiomic Features of MRI Subcompartments Associate with Angiogenic and Inflammatory Transcriptomic Programs in

Daniele Piccolo1, Marco Vindigni1

  • 1Unit of Neurosurgery, Department of Head-Neck and Neuroscience, Azienda Sanitaria Universitaria Friuli Centrale, Presidio Ospedaliero Universitario Santa Maria della Misericordia, Piazzale Santa Maria della Misericordia, 15, 33100 Udine, Italy.

Cancers
|May 4, 2026
PubMed
Summary

Glioblastoma heterogeneity is linked to therapy resistance. This study found that magnetic resonance imaging (MRI) radiomic features can predict regional transcriptomic differences, specifically the Inflammatory Response pathway, in glioblastoma tumors.

Keywords:
glioblastomainflammatory responseradiomicstranscriptomicstumor heterogeneity

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Area of Science:

  • Neuro-oncology
  • Radiomics
  • Transcriptomics
  • Cancer Heterogeneity

Background:

  • Glioblastoma (GBM) displays significant intratumoral heterogeneity, with distinct zones exhibiting unique molecular profiles that contribute to treatment resistance.
  • The potential of magnetic resonance imaging (MRI)-derived radiomic features to reflect these regional transcriptomic variations in GBM is not well understood.

Purpose of the Study:

  • To investigate the association between subcompartment-level radiomic features and transcriptomic pathway enrichment scores derived from approximate tumor zones in glioblastoma.
  • To determine if radiomics can serve as a non-invasive tool to capture regional molecular differences in GBM.

Main Methods:

  • Utilized matched RNA-seq and radiomics data from 28 glioblastoma patients.
  • Computed single-sample gene set enrichment analysis (ssGSEA) pathway scores for 24 gene sets.
  • Employed nested leave-one-patient-out cross-validation (LOPO-CV) with Elastic Net for prediction and linear mixed-effects models (LMM) for association analysis, using a biologically motivated zone-to-subcompartment mapping.

Main Results:

  • Only the Inflammatory Response pathway showed significant association with radiomic features across both predictive (R2cv = 0.185, p=0.008) and exploratory (ΔR2 = 0.214, p=0.004) analyses.
  • Angiogenesis pathway reached predictive significance (R2cv = 0.209, p=0.006) but lacked corroboration in the exploratory analysis, suggesting a tentative signal.
  • T2-derived texture features were consistently selected for both pathways, highlighting their potential relevance.

Conclusions:

  • The Inflammatory Response pathway is significantly associated with radiomic features in glioblastoma, suggesting MRI can capture regional transcriptomic differences.
  • The Angiogenesis pathway association requires further validation in larger cohorts.
  • The study was underpowered for detecting all potential associations due to small sample size and methodological constraints; larger studies with precise spatial co-registration are essential for robust validation.