Background: Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.
Methods: In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.
Results: Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification.
Conclusions: We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.
Keywords: Age clusters; Glioma classification; IDH1; K-Means; Random Forest; XAI; explainable artificial intelligence; pediatric cancer.