Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

ABSTRACT:

Background

Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis.

Methods

Fourteen PCD patterns were analyzed for model construction. Seven transcriptome datasets and a single cell sequencing dataset were collected from the Gene Expression Omnibus database.

Results

A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. The area under curve of the dia

SUBMITTER: Liu M 

PROVIDER: S-EPMC11871900 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature