Integrative multi-omics reveals energy metabolism-related prognostic signatures and immunogenetic landscapes in lung adenocarcinoma.

ABSTRACT:

Background

Energy metabolism (EM) is critically involved in driving tumor development, therapeutic resistance, and modulation of the immune response. However, its genetic basis and prognostic value in lung adenocarcinoma (LUAD) remain unclear. This study integrates multi-omics approaches to develop an EM-related prognostic model for assessing LUAD prognosis and uncovering relevant immunogenetic pathways.

Methods

Differential analysis combined with Mendelian randomization was used to identify EM-related genes (EMRGs) with a causal link to LUAD, which were then used to build a prognostic model via machine learning. Nomogram integrating clinical features and risk model was developed to enhance prognostic accuracy. Subsequent analyses, including immune invasion, enrichment analy

SUBMITTER: Xie L 

PROVIDER: S-EPMC12558868 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature