Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma.

ABSTRACT: Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one-class logistic regression (OCLR) machine-learning method to score MB samples; we then obtained two stemness indices, a gene expression-based stemness index (mRNAsi) and a DNA methylation-based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi

SUBMITTER: Lian H 

PROVIDER: S-EPMC6763787 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature