Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.

ABSTRACT:

Background

Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate the influence of lipid metabolism-related genes (LMRGs) on the diagnosis and treatment of LUAD and to identify significant biomarkers.

Methods

DESeq2 and robust rank aggregation (RRA) analyses were employed to determine the differential expression of LMRGs from TCGA-LUAD and five GEO datasets. Mendelian randomization (MR) was conducted utilizing protein quantitative trait loci (pQTLs) in the deCODE, prot-a, and UKB-PPP Study to estimate causal relationships between plasma proteins and LUAD within the ieu-a-984, ieu-a-965, and FinnGen R10 cohorts as potential drug targets of LUAD. Subsequently, an optimal machine learning model fo

SUBMITTER: Wei S 

PROVIDER: S-EPMC11922676 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature