  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.
