Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

ABSTRACT:

Background

Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.

Methods

Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via "rms" package.

Result

SUBMITTER: Shu Q 

PROVIDER: S-EPMC10203506 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature