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

Claire Jean-Quartier, Fleur Jeanquartier*, Aydin Ridvan, Matthias Kargl, Tica Mirza, Tobias Stangl, Robi Markaĉ, Mauro Jurada, Andreas Holzinger

*Corresponding author for this work

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number77
JournalBMC Medical Informatics and Decision Making
Volume21
Issue number1
Early online date27 Feb 2021
DOIs
Publication statusE-pub ahead of print - 27 Feb 2021

Keywords

  • Biomarkers, Tumor/genetics
  • Cluster Analysis
  • Glioma/diagnosis
  • Humans
  • Isocitrate Dehydrogenase/genetics
  • Mutation
  • explainable artificial intelligence
  • XAI
  • Glioma classification
  • K-Means
  • IDH1
  • pediatric cancer
  • Random Forest
  • Age clusters

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

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