Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response.

ABSTRACT: Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs)

SUBMITTER: Zhou H 

PROVIDER: S-EPMC10407594 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature