Accurately predicting the molecular subtype of cancer is crucial for personalized diagnosis and treatment. Furthermore, finding reliable biomarkers is essential for achieving personalized medicine and improving patient outcomes overall. This paper presents a novel approach for multi-omics biomarker discovery in Glioblastoma using explainable artificial intelligence. The proposed method integrated data from the TCGA cohort and MOGONET's deep learning model for subtyping problems and then used the Integrated Gradients algorithm to discover important features in the multi-omics data. The identified biomarkers were subsequently validated through two approaches: first, by comparing them with established ground truth data from reference databases, and second, performance evaluation using classical machine learning models.