Glioblastoma multiforme (GBM) is one of the most lethal malignancies of the central nervous system characterized by high mortality rate. The complexity of GBM pathogenesis, progression, and prognosis is not fully understood yet. GBM-derived extracellular vesicles (EVs) carry several oncogenic elements that facilitate GBM progression. The purpose of this study was to identify systems level molecular signatures from GBM-derived EVs using integrative analysis of publicly available transcriptomic data generated from plasma and serum samples. The dataset contained 19 samples in total, of which 15 samples were from plasma (11 GBM patients and 4 healthy samples) and 4 samples were from serum (2 GBM and 2 healthy samples). We carried out statistical analysis to identify differentially expressed genes (DEGs), functional enrichment analysis of the DEGs, protein-protein interaction networks, module analysis, transcription factors and target gene regulatory networks analysis, and identification of hub genes. The differential expression of the identified hub genes were validated with the independent TCGA-GBM dataset. We have identified a few crucial genes and pathways associated with GBM prognosis and therapy resistance. The DEGs identified from plasma were associated with inflammatory processes and viral infection. On the other hand, the hub genes identified from the serum samples were significantly associated with protein ubiquitinylation processes and cytokine signaling regulation. The findings indicate that GBM-derived plasma and serum DEGs may be associated with distinct cellular processes and pathways which facilitate GBM progression. The findings will provide better understanding of the molecular mechanisms of GBM pathogenesis and progression. These results can further be utilized for developing and validating minimally invasive diagnostic and therapeutic molecular biomarkers for GBM.
Keywords: Differentially expressed genes; Extracellular vesicles; Glioblastoma multiforme; Protein–protein interactions; Systems biology; Transcriptomic analysis.