Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis

Abstract

Background

Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis.

Methods

Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model’s ability to diagnose pediatric sepsis in 65 actual clinical samples.

Results

Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).

Conclusion

We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

Keywords:

• pediatric sepsis
• diagnostic model
• ROC curves
• WGCNA

Abbreviation

WGCNA, weighted gene co-expression network; GEO, gene expression omnibus; ROC, Receiver operating characteristic curve; DEGs, differentially expressed genes; GSEA, gene set enrichment analysis; TOM, topological overlap matrices; GS, genetic significance; MM, module membership; LASSO, Least absolute shrink and selection operator; RF, random forests; AUC, area under the curve; qRT-PCR, quantitative real-time PCR; PCT, procalcitonin; NEU%, neutrophil percentage; WBC, white blood cell; CRP, C-reactive protein; PBMCs, peripheral blood mononuclear cells.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was supported by the Ethics Committees of The First Affiliated Hospital of Zhengzhou University (2023-KY-0932-002). Written informed consent was obtained from parents or legal guardians of all patients and healthy controls. All methods were performed following the relevant guidelines and regulations. The manuscript is consistent with the Declaration of Helsinki.

Disclosure

All authors declare no conflict of interest.

Additional information

Funding

This study was supported by the Henan Provincial Health and Health Commission Joint Construction Project (LHGJ20230209).