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A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

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Benchmarking Sepsis Gene Expression Diagnostics Using Public Data.

Timothy E Sweeney¹, Purvesh Khatri

¹1Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA. 2Division of Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA.

Critical Care Medicine

|September 30, 2016

Summary

This summary is machine-generated.

Three sepsis gene expression diagnostics showed similar performance in distinguishing sepsis from noninfectious inflammation. Publicly available gene expression data are valuable for evaluating sepsis diagnostic tools.

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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Biomedical diagnostics
Genomics
Infectious disease research

Background:

Sepsis diagnostics require improvement.
Several new gene expression classifiers, including Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab, have been developed.

Purpose of the Study:

To systematically evaluate the performance of three sepsis gene expression classifiers using publicly available data.
To establish a public repository for sepsis gene expression data to facilitate future research.

Main Methods:

Searched Gene Expression Omnibus and ArrayExpress for human whole blood gene expression datasets related to sepsis and acute infections.
Included 39 datasets (3,241 samples, 2,604 patients) comparing sepsis/infection to healthy or noninfectious inflammation.
Renormalized raw data and applied consistent methods for analysis.

Main Results:

The Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab showed comparable performance in differentiating sepsis from noninfectious inflammation (AUCs 0.82, 0.78, 0.73).
Performance varied across datasets, with some showing low accuracy (AUC < 0.7) for FAIM3-to-PLAC8 and Septicyte Lab.
Septicyte Lab performed significantly worse in distinguishing infections from healthy controls (AUC 0.71).

Conclusions:

The evaluated gene expression diagnostics do not exhibit significant differences in distinguishing sepsis from noninfectious systemic inflammatory response syndrome.
Publicly available gene expression data serve as a valuable resource for benchmarking diagnostic tools.
Further validation and refinement of sepsis gene expression classifiers are warranted.