From Sleep Deprivation to Severe COVID-19: A Comprehensive Analysis of Shared Differentially Expressed Genes and Potential Diagnostic Biomarkers

Abstract

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+ T cell exhaustion and the biological pathway of ribosome biosynthesis. Conclusions: This research suggests that biomarkers CD160, QPCT, SIGLEC17P, and SLC22A4 could serve as potential diagnostic tools for SD-related severe COVID-19. The substantial association of CD160 with both CD8+ T cell exhaustion and ribosomal biogenesis highlights its potential pivotal role in the pathogenesis and progression of COVID-19.

Keywords

• sleep deprivation
• covid-19
• bioinformatics
• machine learning
• immune cell infiltration
• biomarkers
• cd160