Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease. Results: We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease. Conclusions: The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Original languageEnglish (US)
Article number319
JournalBMC Genomics
Volume24
Issue number1
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

Keywords

  • COVID-19
  • Integrative analysis
  • Multi-omics
  • Pathway analysis

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status'. Together they form a unique fingerprint.

Cite this