Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.

ABSTRACT:

Background

Sepsis frequently induces acute kidney injury (AKI), and the complex interplay between these two conditions worsens prognosis, prolongs hospitalization, and increases mortality. Despite therapeutic options such as antibiotics and supportive care, early diagnosis and treatment remain a challenge. Understanding the underlying molecular mechanisms linking sepsis and AKI is critical for the development of effective diagnostic tools and therapeutic strategies.

Methods

We used two sepsis (GSE57065 and GSE28750) and three AKI (GSE30718, GSE139061, and GSE67401) datasets from the NCBI Gene Expression Omnibus (GEO) for model development and validation, and performed batch effect mitigation, differential gene, and functional enrichment analysis using R software packages. We

SUBMITTER: Lin C 

PROVIDER: S-EPMC12321556 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature