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- Academic Editor
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†These authors contributed equally.
Background: This study aims to identify biomarkers through the analysis
of genomic data, with the goal of understanding the potential immune mechanisms
underpinning the association between sleep deprivation (SD) and the progression
of COVID-19. Methods: Datasets derived from the Gene Expression Omnibus
(GEO) were employed, in conjunction with a differential gene expression analysis,
and several machine learning methodologies, including models of Random Forest,
Support Vector Machine, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. The molecular underpinnings of the
identified biomarkers were further elucidated through Gene Set Enrichment
Analysis (GSEA) and AUCell scoring. Results: In the research, 41 shared
differentially expressed genes (DEGs) were identified, these were associated with
the severity of COVID-19 and SD. Utilizing LASSO and SVM-RFE, nine optimal
feature genes were selected, four of which demonstrated high diagnostic potential
for severe COVID-19. The gene CD160, exhibiting the highest diagnostic value, was
linked to CD8