Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma

Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma

Abstract The mortality rates have been increasing for glioma in adolescents and young adults (AYAs, aged 15–39 years). However, current biomarkers for clinical assessment in AYAs glioma are limited, prompting the urgent need for identifying ideal prognostic signature. Extracellular matrix is involve...

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Bibliographic Details
Main Authors: Pancheng Wu, Yi Zheng, Wei Wu, Beichen Zhang, Yichang Wang, Mingjing Zhou, Ziyi Liu, Zhao Wang, Maode Wang, Jia Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Adolescents and young adults
Glioma
Machine learning
Prognosis
Immunotherapy
Online Access:https://doi.org/10.1038/s41598-025-13547-6
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https://doi.org/10.1038/s41598-025-13547-6

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