Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning.

ABSTRACT:

Background

Lower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

Methods

Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The

SUBMITTER: Liu XP 

PROVIDER: S-EPMC9825346 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature