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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-13547-6 |
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