Glioblastoma (GBM) is an aggressive brain cancer with poor prognosis, making the identification of reliable molecular biomarkers vital for early detection and improving treatment strategies. This study introduces a two-phase framework for discovering and validating GBM subtyping biomarkers. In the first phase, we employed the Simple, Fast, and Efficient (SFE) [1] feature selection algorithm to high-dimensional multi-omics data from The Cancer Genome Atlas (TCGA) GBM cohort to identify potential biomarkers. In the second phase, we assessed the explainability of these biomarkers through two approaches. First, by comparing them with reference data from established databases. Second, by evaluating their performance using classical machine learning models. This two-phase framework is versatile and potentially applicable to other cancer datasets, offering a promising approach to biomarker discovery for improving cancer treatment.