Integrated Transcriptomic Analysis Reveals Distinct Immune Response Signatures and Prognostic Biomarkers in SARS-CoV-2 Infection

The COVID-19 pandemic continues to challenge global health systems, with persistent knowledge gaps regarding host immune responses to SARS-CoV-2 infection. Comprehensive transcriptomic profiling of peripheral blood mononuclear cells provides critical insights into molecular mechanisms underlying COVID-19 pathogenesis and may identify diagnostic biomarkers. This study performed integrative bioinformatics analysis of RNA-sequencing data from COVID-19 patients infected with the Omicron variant to elucidate immune dysregulation patterns and identify molecular signatures. We analyzed peripheral blood mononuclear cell transcriptomes from 47 COVID-19 patients and 8 healthy controls using the GSE201530 dataset generated on the Illumina NovaSeq 6000 platform. Differential expression analysis using DESeq2 identified 2,253 significantly dysregulated genes (FDR < 0.05, |log2FC| > 1), comprising 1,573 upregulated and 680 downregulated genes. Functional enrichment revealed activation of interferon signaling, viral response pathways, and NOD-like receptor signaling. The most upregulated genes included interferon-stimulated genes IFI27, SIGLEC1, LY6E, IFI44L, and OAS1. Immune cell deconvolution demonstrated significant increases in M2 macrophages and decreases in dendritic cells, eosinophils, mast cells, and CD4 + T cells. Protein-protein interaction network analysis identified ISG15, RSAD2, DHX58, and IRF7 as critical hub genes. Machine learning models achieved excellent discrimination between COVID-19 patients and controls, with Random Forest attaining 100% test accuracy and AUC of 1.0. External validation on an independent cohort (GSE163151, n = 176) confirmed robust generalizability, with 67–81% accuracy and AUC of 0.73–0.88 depending on class balance. These findings reveal a characteristic interferon-dominated transcriptomic signature in Omicron-infected patients with validated diagnostic potential. The identified gene signatures and hub genes represent promising biomarker candidates for COVID-19 detection and monitoring, warranting further clinical validation.