Revealing radiotherapy- and chemoradiation-induced pathway dynamics in glioblastoma by analyzing multiple differential networks

The progression of glioblastoma (GBM) is driven by dynamic alterations in the activity and connectivity of gene pathways. Revealing these dynamic events is necessary in order to understand the pathological mechanisms of, and develop effective treatments for, GBM. The present study aimed to investigate dynamic alterations in pathway activity and connectivity across radiotherapy and chemoradiation conditions in GBM, and to give system-level insights into molecular mechanisms for GBM therapy. A total of two differential co-expression networks (DCNs) were constructed using Pearson correlation coefficient analysis and one sided t-tests, based on gene expression profiles and protein-protein interaction networks, one for each condition. Subsequently, shared differential modules across DCNs were detected via significance analysis for candidate modules, which were obtained according to seed selection, module search by seed expansion and refinement of searched modules. As condition-specific differential modules mediate differential biological processes, the module connectivity dynamic score (MCDS) was implemented to explore dynamic alterations among them. Based on DCNs with 287 nodes and 1,052 edges, a total of 28 seed genes and seven candidate modules were identified. Following significance analysis, five shared differential modules were identified in total. Dynamic alterations among these differential modules were identified using the MCDS, and one module with significant dynamic alterations was identified, termed the dynamic module. The present study revealed the dynamic alterations of shared differential modules, identified one dynamic module between the radiotherapy and chemoradiation conditions, and demonstrated that pathway dynamics may applied to the study of the pathogenesis and therapy of GBM.