基于突变的聚类和分类分析揭示了胶质瘤的独特年龄组和与年龄相关的生物标志物。
Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.
机构信息
Human-Centered AI Lab (Holzinger Group), Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria.
Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria.
出版信息
BMC Med Inform Decis Mak. 2021 Feb 27;21(1):77. doi: 10.1186/s12911-021-01420-1.
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.
背景
恶性脑肿瘤疾病在分子特征上因患者年龄而异。
方法
在这项工作中，我们使用公共资源中的基因突变数据来探索脑肿瘤的年龄特异性。我们同时使用可解释的聚类和分类方法来发现和解释脑肿瘤疾病中的年龄差异。我们估计年龄聚类并关联年龄特异性生物标志物。
结果
年龄组分类显示了已知的年龄特异性，但也指出了几个迄今为止与神经胶质瘤分类无关的基因。
结论
我们强调了突变基因是某些年龄组的特征，并提出了新的基于年龄的生物标志物和靶标。
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