A Bioinformatics Approach to Identifying Druggable Targets in Glioblastoma Multiforme and Drug Discovery.

Glioblastoma multiforme (GBM) tumors are very aggressive and lethal. Most GBM patients upon being diagnosed with GBM, do not live longer than 15 months. Currently, GBM treatment includes surgery, Temozolomide (TMZ) chemotherapy, and adjuvant radiation therapy. However, this therapy fails to cure patients completely. Tumor stem cells and the heterogeneity of GBM tumors are some of the reasons that make tumors insensitive to TMZ, resulting in a shortened survival of the patients.
An aberrant cell cycle in GBM facilitates its rapid progression by avoiding senescence and apoptosis through a high expression of crucial regulatory proteins such as cyclin-dependent kinases. This study discusses the identification and validation of an oncogenic signature consisting of CDK1, PBK, and CHEK1 that contributes to therapy resistance in glioblastoma through extensive bioinformatics analysis. We unraveled the predictive biomarker properties of CDK1, PBK, and CHEK1 and discovered that their high expression are important players in advancing a dysregulated cell cycle and tumor progression.
Furthermore, we estimated poor patient survival resulting from high expression of these genes using Kaplan-Meier. Using in silico molecular docking, we explored the repurposing of Dapagliflozin to target CDK1, PBK, and CHEK1. This study revealed Dapagliflozin’s strong binding affinity on CDK1, PBK, and CHEK1, resulting in effective cell cycle arrest with a comparable effect to Abemaciclib.