A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model.

ABSTRACT: Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteris

SUBMITTER: Li B 

PROVIDER: S-EPMC9843270 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature