Differential Gene Expression in MRI-classified Glioblastoma

Area of Science:

• Neuro-oncology
• Genomics
• Medical Imaging

Background:

• Glioblastoma (GBM) exhibits molecular heterogeneity, with genomic and transcriptomic alterations identified.
• Existing resources like The Cancer Genome Atlas (TCGA) and The Cancer Imaging Atlas (TCIA) offer molecular and MRI data, but lack integrated analysis.
• The link between molecular signatures and MRI-classified GBM remains undeciphered.

Purpose of the Study:

• To computationally integrate TCIA and TCGA datasets for GBM.
• To identify common and distinct molecular signatures across GBM patients and tumor locations.
• To explore the relationship between molecular profiles and imaging data for precision neuro-oncology.

Main Methods:

• Computational integration of Glioblastoma (GBM) molecular data from The Cancer Genome Atlas (TCGA) and imaging data from The Cancer Imaging Atlas (TCIA).
• Analysis to uncover common and distinct molecular signatures related to tumor location.
• Identification of key genes associated with GBM heterogeneity and commonalities.

Main Results:

• Identified common and distinct molecular signatures in Glioblastoma (GBM) patients, varying by tumor location.
• Top 12 genes highlighted dysregulation in neurotransmitter receptors/transporters and synaptic activity common across GBM.
• Integrated molecular and imaging data revealed disrupted neurocircuitry, suggesting imbalanced excitation and inhibition.

Conclusions:

• Coherent molecular and imaging data stratification aids precision neuro-oncology and treatment selection for Glioblastoma (GBM).
• Findings identify molecular targets within the disrupted neurocircuit of GBM.
• Results support viewing Glioblastoma (GBM) within the context of neurological disorders, beyond solely a cancerous disease.