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Updated: Aug 28, 2025
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach
Lu Lu1, Le-Ping Liu1,2, Rong Gui1
1Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China.
Insights
Severe COVID-19 shares pathological similarities with sepsis, prompting a bioinformatics study. Researchers identified common genes and pathways, suggesting ITGAM as a biomarker and potential therapeutic agents for COVID-19 treatment.
Area of Science:
- Infectious Diseases
- Bioinformatics
- Systems Biology
Background:
- COVID-19 (Coronavirus Disease 2019) presents with sepsis-like symptoms, including organ dysfunction and coagulopathy.
- Severe COVID-19 shares pathological features with sepsis, such as cytokine storms and neutrophil dysfunction.
- Understanding molecular parallels is crucial for developing novel COVID-19 treatments.
Purpose of the Study:
- To analyze molecular mechanisms common to COVID-19 and sepsis using bioinformatics.
- To identify shared differentially expressed genes (DEGs) and pathways.
- To discover potential biomarkers and therapeutic targets for COVID-19.
Main Methods:
- Compared gene expression profiles of COVID-19 and sepsis patients from the GEO database.
- Performed enrichment analysis on common DEGs to identify related pathways (e.g., Cytokine-cytokine receptor interaction, NF-kappa B signaling).
- Constructed protein-protein interaction and gene regulatory networks; screened potential drugs using molecular docking.
Main Results:
- Identified common DEGs between COVID-19 and sepsis, highlighting inflammatory response pathways.
- ITGAM emerged as a potential key biomarker based on network analysis.
- Developed a COVID-19 diagnostic model and risk prediction nomogram.
- Screened progesterone and emetine as potential therapeutic agents.
Conclusions:
- Elucidating shared pathogenesis between COVID-19 and sepsis offers new treatment strategies.
- ITGAM may serve as a critical biomarker for COVID-19.
- Identified novel therapeutic candidates for COVID-19 management.
Abstract:
Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.
