Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.

ABSTRACT:

Background

Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.

Results

Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate m

SUBMITTER: Lee G 

PROVIDER: S-EPMC11290261 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature