Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

Area of Science:

• Genomics
• Critical Care Medicine
• Translational Research

Background:

• High mortality rates in intensive care units (ICUs) necessitate improved prognostic tools.
• Current prognostic systems for postsurgical shock lack reliability in predicting patient outcomes.
• Need for accurate biomarkers to stratify mortality risk in postsurgical shock patients.

Purpose of the Study:

• To develop a gene expression signature for distinguishing low and high mortality risk in postsurgical shock patients.
• To identify novel transcriptional biomarkers for improved patient classification and risk stratification.

Main Methods:

• Microarray analysis on a discovery cohort to identify differentially expressed genes between survivors and non-survivors.
• Validation of selected gene expression using quantitative real-time polymerase chain reaction (qPCR) in a separate cohort.
• Receiver-operating characteristic (ROC) analysis to assess the predictive accuracy of gene expression levels compared to APACHE and SOFA scores.

Main Results:

• Four genes (IL1R2, CD177, RETN, OLFM4) were found to be upregulated in non-survivors.
• The identified gene signature demonstrated validated predictive power in the validation cohort.
• The gene expression-based risk classification showed higher accuracy than established scoring systems (APACHE, SOFA).

Conclusions:

• A novel gene expression signature comprising IL1R2, CD177, RETN, and OLFM4 can accurately classify postsurgical shock patients by mortality risk.
• These transcriptional biomarkers offer a more precise tool for risk stratification compared to existing clinical scores.
• The findings provide a foundation for developing targeted interventions and improving patient management in critical care settings.