Minimalistic Transcriptomic Signatures Permit Accurate Early Prediction of COVID-19 Mortality

Area of Science:

• Genomics and Transcriptomics
• Infectious Disease Epidemiology
• Clinical Prognostics

Background:

• Predicting COVID-19 mortality remains a significant clinical challenge.
• Accurate prognostic assays are crucial for timely patient management.
• This study addresses the unmet need for reliable early mortality prediction in COVID-19.

Purpose of the Study:

• To identify and validate parsimonious transcriptomic signatures for predicting COVID-19 mortality.
• To develop accessible prognostic tools for early risk stratification.
• To assess the performance of these signatures in both unvaccinated and vaccinated patients.

Main Methods:

• Analysis of host gene expression in 894 hospitalized COVID-19 patients using RNA sequencing.
• Development of prognostic classifiers using LASSO regression incorporating gene expression, age, and viral load.
• External validation in an independent cohort of 137 COVID-19 patients, including vaccinated individuals.

Main Results:

• Fatal COVID-19 showed distinct peripheral blood gene expression patterns, differing from sepsis.
• A three-gene peripheral blood classifier (CD83, ATP1B2, DAAM2) achieved an AUC of 0.88.
• The OLAH gene alone demonstrated strong predictive performance (AUC 0.86), and classifiers were validated in vaccinated patients.

Conclusions:

• A three-gene peripheral blood signature and OLAH gene expression accurately predict COVID-19 mortality early in hospitalization.
• These classifiers show promise as accessible prognostic tools for guiding clinical decisions.
• Further research is warranted to implement these signatures for triage and early therapeutic interventions.