Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

ABSTRACT: Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE me

SUBMITTER: Tan Y 

PROVIDER: S-EPMC11584728 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature