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 involved in the development of tumors, while their prognostic significance in AYAs glioma remains unclear. By an integrated machine learning workflow and circuit training and validation procedure, we developed a machine learning-derived prognostic signature (MLDPS) based on 1,026 extracellular matrix-related genes and 3 AYAs glioma cohorts. MLDPS exhibited robust and consistent predictive performance in overall survival and could serve as an independent prognostic factor for AYAs glioma. Simultaneously, MLDPS o

SUBMITTER: Wu P 

PROVIDER: S-EPMC12331974 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature