Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

BioData Mining

Research

Roberta Coletti¹  Sofia Martins²  Marta B. Lopes³ 

[1] Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, 2829-516, Caparica, Portugal;NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516, Caparica, Portugal;NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516, Caparica, Portugal;Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, 2829-516, Caparica, Portugal;NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), NOVA School of Science and Technology, 2829-516, Caparica, Portugal;UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, 2829-516, Caparica, Portugal;

关键词: Glioma;    Transcriptomics;    Biomarkers;    Sparse networks;    Joint graphical lasso;    Robust sparse K-means clustering;   

DOI  :  10.1186/s13040-023-00341-1

 received in 2023-03-21, accepted in 2023-08-13,  发布年份 2023

来源: Springer

【 摘 要 】

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups’ separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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