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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
463
Identifying effective diagnostic biomarkers in sepsis by bioinformatics analysis
Mingxin Han1, Hong Zheng1, Donghao Wang1
1Department of Critical Care Medicine, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Computer Methods in Biomechanics and Biomedical Engineering
|December 1, 2025
Summary
This study identifies 14 hub genes as potential biomarkers for sepsis diagnosis and treatment. These key genes were discovered using bioinformatics analysis of gene expression data.
Area of Science:
- Bioinformatics
- Genomics
- Immunology
Background:
- Sepsis diagnosis and treatment require reliable biomarkers.
- Identifying novel biomarkers is crucial for improving patient outcomes.
Purpose of the Study:
- To identify potential sepsis biomarkers using bioinformatics analysis.
- To explore mechanisms and therapeutic agents for sepsis.
Main Methods:
- Downloaded sepsis expression profile data from the Gene Expression Omnibus (GEO) database.
- Screened for hub genes and performed functional analysis.
- Verified findings using external datasets.
Main Results:
- Identified 14 hub genes: PRF1, CD247, IL7R, CD27, CCR7, IL2RB, GZMB, KLRK1, GZMK, CD160, FCGR1A, RUNX3, HLA-DRB1, and PRKCQ.
- These genes show potential as biomarkers for sepsis diagnosis and treatment.
- Mechanisms and potential therapeutic agents were also identified.
Conclusions:
- The 14 identified hub genes may serve as valuable biomarkers for sepsis.
- Further research can validate these genes for clinical application in sepsis management.
