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. 2021 Feb 27;21(1):77.
doi: 10.1186/s12911-021-01420-1.

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

Claire Jean-Quartier #  1 , Fleur Jeanquartier #  2   3 , Aydin Ridvan  4 , Matthias Kargl  4 , Tica Mirza  4 , Tobias Stangl  4 , Robi Markaĉ  4 , Mauro Jurada  4 , Andreas Holzinger  4
Affiliations

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

Claire Jean-Quartier et al. BMC Med Inform Decis Mak. .

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: Age clusters; Glioma classification; IDH1; K-Means; Random Forest; XAI; explainable artificial intelligence; pediatric cancer.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Graphical abstract
Fig. 2
Fig. 2
Glioma sample data distribution
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Fig. 3
Top 10 mutated genes distribution
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Fig. 4
Performance functions for different number of clusters
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Fig. 5
Results from gene-based clustering of age groups with different number of clusters n
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Fig. 6
Comparison of classifier confusion matrices, showing classifier performance (darker means better prediction)
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Fig. 7
Comparison of classifier features (classes sorted by performance)
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Fig. 8
Impact of the top 20 features for each of the classes 0–18, 19–70, 70+
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Fig. 9
Impact of the top 20 features for each of the classes 0–22, 23–48, 48+
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Fig. 10
LGG and HGG specific sample data distribution per age
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Fig. 11
Comparison of Classifier feature importance for age classes 0–18 (blue), 19–70 (orange), 70+ (green); classes sorted by performance
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Fig. 12
Comparison of updated classifier features for age classes 0–22 (blue), 23–48 (orange), 48+ (green), classes sorted by performance
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Fig. 13
Comparison of updated classifier features for age classes 0–9 (green), 10–26 (red), 27–50 (orange), 50+ (blue); classes sorted by performance
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Fig. 14
Distribution of top mutated genes within various age groups: examples from top 20 mutated genes among selected age groups of children, young adults and elderly patients suffering from glioma
See this image and copyright information in PMC

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