Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma

Abstract

Abstract 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

• Glioma classification
• pediatric cancer
• explainable artificial intelligence
• XAI
• Age clusters
• K-Means