INTEGRATIVE TRANSCRIPTOMIC, PROTEOMIC, AND MACHINE LEARNING ANALYSIS OF COVID-19 SEVERITY AND ITS IMPLICATIONS FOR POST-VIRAL OUTCOMESFor Post-Viral Outcomes
DOI:
https://doi.org/10.69980/w53n3j86Keywords:
Transcriptomics, Proteomics, Machine Learning, COVID-19 Severity, Multi-Omics Integration, Post-Viral OutcomesAbstract
COVID-19, caused by SARS-CoV-2, exhibits wide variability in clinical severity, ranging from asymptomatic infection to critical respiratory failure. Understanding the molecular determinants of this severity is essential for biomarker discovery, risk stratification, and improved clinical management. This study presents an integrative multi-omics framework combining transcriptomic analysis, proteomic enrichment profiling, and machine learning-based feature prioritization to identify molecular signatures associated with COVID-19 severity and their potential implications for post-viral outcomes.
Transcriptomic analysis of the publicly available whole-blood dataset GSE213313 (Agilent microarray) identified 1,544 significantly differentially expressed genes (FDR < 0.05) in critical versus healthy samples, with enrichment of innate immune response, leukocyte activation, antigen processing, T-cell differentiation, and cytokine-mediated signalling pathways. Proteomic analysis using a published peer-reviewed dataset comparing mild (n = 3) and severe (n = 5) COVID-19 cases identified 91 severity-associated proteins, with significant enrichment of acute-phase response, complement cascade activation, platelet degranulation, and coagulation-related processes.
Multi-omics integration identified 8 overlapping molecules between transcriptomic and proteomic datasets, with an inverse RNA–protein fold-change correlation (r = −0.58), indicative of post-transcriptional regulatory complexity. Machine learning analysis using a Random Forest classifier demonstrated perfect discrimination between mild and severe cases (AUC = 1.0; LOOCV accuracy = 100%), and prioritized proteins including LIPG, CLEC11A, KDR, FAIM3, and BST1 as key severity-associated features.
Collectively, this study demonstrates that severe COVID-19 is characterized by coordinated dysregulation of immune activation, inflammatory signalling, complement amplification, and coagulation mechanisms across multiple molecular layers. The identified molecular signatures provide a foundation for future biomarker validation and therapeutic targeting in COVID-19 and related post-viral conditions.
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