Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

ABSTRACT: Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditional inflammatory markers facilitate the diagnosis of sepsis, the aspect of immune suppression remains poorly addressed. This study aimed to identify critical immune-related genes (IIRGs) associated with sepsis through genomic analysis and machine learning techniques, thereby enhancing diagnostic and treatment response predictions. Analyses of two extensive datasets were conducted, identifying significant immune genes using the ESTIMATE algorithm, Weighted Gene Correlation Network Analysis (WGCNA), and five machine learning methods. Prediction models were constructed and validated using six machine learning algorithms, achieving high accuracy (A

SUBMITTER: Xiong W 

PROVIDER: S-EPMC11894075 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature