Identification of amino acid metabolism‑related genes as diagnostic and prognostic biomarkers in sepsis through machine learning.

ABSTRACT: Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed. Based on weighted gene co-expression network analysis and machine learning algorithms, hub AAMGs were identified in the GSE65682 database. Subsequently, hub AAMGs were evaluated for their expression levels and diagnostic and prognostic significance in sepsis, as well as their interactions with regulatory pathways and role in immune cell infiltration. Additionally, trends in AAMG expression w

SUBMITTER: Wen Y 

PROVIDER: S-EPMC11705229 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature