Identifying effective diagnostic biomarkers in sepsis by bioinformatics analysis
Identifying appropriate biomarkers for sepsis diagnosis and treatment holds great significance. This study aims to explore potential sepsis biomarkers through bioinformatics analysis. The expression profile data of sepsis were downloaded from Gene Expression Omnibus (GEO) database. Hub genes were screened and performed functional analysis, therewith verified using external datasets. The mechanisms and potential therapeutic agents were also identified. A total of 14 Hub genes were identified in this study including PRF1, CD247, IL7R, CD27, CCR7, IL2RB, GZMB, KLRK1, GZMK, CD160, FCGR1A, RUNX3, HLA-DRB1 and PRKCQ, which may serve as potential biomarkers for diagnosis and treatment of sepsis.
Authors
- Mingxin Han
- Donghao Wang (ORCID: https://orcid.org/0000-0002-7219-2676)
- Yang Lyu (ORCID: https://orcid.org/0000-0002-3560-4061)
- Hong Zheng (ORCID: https://orcid.org/0000-0002-8884-8211)
Institutions
- Tianjin Medical University Cancer Institute and Hospital (CN)
Publication Details
- Journal
- Computer Methods in Biomechanics & Biomedical Engineering
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1080/10255842.2025.2595138
- Primary Topic
- Inflammation biomarkers and pathways
- Type
- article
- Field-Weighted Citation Impact
- 0.00