Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Area of Science:

• Genomics
• Immunology
• Pharmacology

Background:

• The COVID-19 pandemic has caused significant mortality, with no definitive antiviral treatments available.
• Identifying key molecular players in COVID-19 is crucial for developing effective therapies and biomarkers.

Purpose of the Study:

• To identify key gene modules and hub targets in COVID-19 pathogenesis.
• To explore potential therapeutic strategies through drug repurposing.

Main Methods:

• Gene coexpression analysis of SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data.
• Identification of differentially expressed genes (DEGs), hub genes, and enriched pathways (e.g., IL-17 signaling).
• Analysis of regulatory networks and drug-gene interactions for potential therapeutic targets.

Main Results:

• Four key gene modules and a hub gene signature were identified, with DEGs enriched in immune and inflammatory responses.
• Hub genes demonstrated high classification performance (>0.90 accuracy), indicating biomarker potential.
• Top-scored repurposed drugs including amsacrine, palbociclib, and teniposide were suggested.

Conclusions:

• The identified hub genes and pathways represent potential therapeutic targets for COVID-19.
• The study provides a foundation for developing novel biomarkers and drug repurposing strategies for coronavirus disease-2019.
• Further validation of these findings could lead to improved clinical management of COVID-19.