# Molecular Signatures and Network Alterations Underlying GBM Progression and Recurrence

## TL;DR

This study identifies gene-expression patterns and molecular networks linked to glioblastoma progression and recurrence, offering insights into potential therapeutic targets.

## Contribution

The study introduces composite Tumor and Recurrence Scores to classify GBM states and reveals novel gene-expression signatures associated with tumor recurrence.

## Key findings

- Distinct molecular signatures were identified in primary and recurrent GBM samples.
- Transcriptomic analysis revealed four tumor states: normal-like, proliferative, transitional, and recurrence-adapted.
- Dysregulated genes were linked to cellular growth, proliferation, and movement pathways.

## Abstract

Background and Objectives: Glioblastoma (GBM) is the most aggressive form of primary brain tumor, characterised by high recurrence rates and poor patient prognosis. This study aimed to identify gene-expression signatures and molecular networks associated with primary and recurrent GBM to better understand the biological mechanisms underlying tumor progression. Materials and Methods: Gene expression analysis of TCGA data was conducted to identify differentially expressed genes across tumor, recurrent, and normal brain tissues. Analysis of overlapping differentially expressed gene sets revealed both common and specific gene-expression profiles across the groups, highlighting genes potentially involved in GBM recurrence. Gene network and canonical pathway analyses were performed using Ingenuity Pathway Analysis (IPA) to identify key pathways and cellular functions altered in GBM. Results: Our data identified distinct molecular signatures in tumor, recurrent, and normal brain samples, highlighting dysregulated genes associated with cellular growth, proliferation, and movement. Transcriptomic stratification revealed progressive tumor- and recurrence-adapted states, with composite Tumor Scores (TS) and Recurrence Scores (RS) classifying samples into four classes: normal-like, proliferative, transitional, and recurrence-adapted tumor states. Conclusions: These findings provide insights into the signaling networks and biological mechanisms underlying GBM recurrence and may guide the identification of potential therapeutic targets to improve the management of this malignancy.