| Sumario: | Simple Summary: This study has provided new insights into the role of E2F transcription factors, a group of proteins, in glioblastoma (GBM), a type of brain cancer. These proteins play a part in various processes that can lead to cell death, growth, and the formation of new blood vessels, all of which are crucial in the development of GBM. To understand how these proteins impact a specific type of GBM that responds to the drug Bevacizumab (BVZ), researchers utilized advanced computer algorithms on datasets from multiple large databases. By doing so, they were able to predict the potential lifespan of GBM patients based on the levels of these proteins found in their tumors. The study revealed that an increase in a protein called E2F8 and its regulatory networks following BVZ treatment might be associated with the recurrence of cancer. This suggests that E2F8 could be a key factor in the reemergence of GBM after treatment. Furthermore, the study indicated that BVZ could be detrimental to GBM patients whose tumors do not respond to the drug, potentially worsening the disease. These findings emphasize the significance of E2F proteins in the aggressiveness of GBM and its response to BVZ, suggesting their potential as markers for predicting patient outcomes and as targets for more personalized treatment approaches. The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment. Using machine learning algorithms, including TreeBagger and deep neural networks, we systematically predicted and validated GBM patient survival terms based on the expression profiles of E2Fs across BVZ-responsive GBM subtypes. Our bioinformatics analyses suggested that a significant increase in E2F8 post-BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, implicating this factor as a candidate mechanism of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. These insights underscore that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers, therapeutic targets, and recommending precision BVZ treatment to individual GBM patients.
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