Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers.

ABSTRACT:

Background

The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers.

Methods

In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and tr

SUBMITTER: Li CI 

PROVIDER: S-EPMC9990231 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature