Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.

ABSTRACT:

Background

Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.

Methods

Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein inter

SUBMITTER: Lin G 

PROVIDER: S-EPMC11192024 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature