Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis.

Abstract

AIM:

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

METHODS:

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

RESULTS:

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

CONCLUSION:

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

• Authors: Yinhui Yao, Jingyi Zhao, Junhui Hu, Hong Song, Sizhu Wang, Ying Wang
• Journal: BioMed research international (Biomed Res Int) Vol. 2022 Pg. 5217885 ( 2022) ISSN: 2314-6141 [Electronic] United States
• PMID: 35198634 (Publication Type: Journal Article, Meta-Analysis)
• Copyright: Copyright © 2022 Yinhui Yao et al.

Chemical References

• Biomarkers

Topics

• Biomarkers (analysis)
• Child
• Computational Biology
• Databases, Genetic
• Early Diagnosis
• Gene Expression Profiling
• Gene Regulatory Networks
• Humans
• Sepsis (diagnosis, genetics)