A robust and generalizable immune-relatedsignature for sepsis diagnostics

TL;DR

This study introduces a new blood transcriptome-based diagnostic signature for sepsis, utilizing a novel Recurrent Logistic Regression method to identify five immune-related genes with high accuracy across multiple cohorts.

Contribution

The paper presents a novel method and a robust gene panel that significantly improve sepsis diagnosis accuracy over existing markers.

Findings

The gene panel achieves an average AUROC of 0.9959 across nine validation cohorts.

The identified biomarkers outperform existing diagnostic markers.

The method provides a foundation for clinical tests and mechanistic studies.

Abstract

High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.