Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

ABSTRACT:

Background

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).

Methods

We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME)

SUBMITTER: Zhang P 

PROVIDER: S-EPMC10229769 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature