Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

ABSTRACT: This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed genes were identified using the R 4.3.2 ( https://www.r-project.org/ ), followed by gene set enrichment analysis. Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. The inflammatory and immune status of pediatric septic shock was evaluated through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The correlation between diagnostic markers and infiltrating immune

SUBMITTER: Lyu P 

PROVIDER: S-EPMC11947139 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature