Identification of Key Inflammation-related Genes as Potential Diagnostic Biomarkers of Sepsis.

CONTEXT

Sepsis is one of the leading causes of mortality for patients with severe infections who had been admitted to intensive care units (ICUs). Early diagnosis, accurate treatment, and management of sepsis remain extremely difficult in clinical settings, due to a lack of early biomarkers and diverse clinical manifestations.

OBJECTIVE

The study intended to identify the key genes and pathways associated with inflammation in sepsis-using microarray technology combined with bioinformatics and key inflammation-related genes (IRGs)-to perform an enrichment analysis and evaluate the value of those genes for the diagnosis and evaluation of prognosis for patients with sepsis.

DESIGN

The research team performed a genetic analysis.

SETTING

The study took place at the Center for Emergency and Critical Medicine at Jinshan Hospital of Fudan University in Jinshan District, Shanghai, China.

GROUPS

The research team created two groups, the sepsis group, individuals with sepsis, and the control group, individuals without sepsis, using data for those groups from five microarray datasets obtained from the Gene Expression Omnibus (GEO) database.

OUTCOME MEASURES

The research team: (1) downloaded the GSE57065, GSE28750, GSE9692, GSE13904, and GSE54514 datasets from the Gene Expression Omnibus (GEO) database for analysis; (2) analyzed the GSE57065, GSE28750, and GSE9692 datasets to detect the differentially expressed genes (DEGs) in the sepsis and control groups; (3) used Venn diagrams to obtain the intersection of DEGs and inflammation-related genes (IRGs); (4) mapped the protein-protein interaction (PPI) network using the Search Tool for Retrieval of Interacting Genes (STRING) database; (5) detected the hub genes using Cytoscape and cytoHubba; (6) performed an enrichment analysis of hub IRGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG); (7) validated the expression of hub IRGs in sepsis using the GSE13904 dataset; and (8) performed a survival analysis in sepsis using the GSE54514 dataset to explore the prognostic value of the hub IRGs.

RESULTS

The research team: (1) identified 104 upregulated DEGs and 4 downregulated DEGs; (2) after defining the intersection of DEGs and IRGs, detected nine differentially expressed IRGs (DEIRGs); and (3) identified five IRGs- haptoglobin (HP), high affinity immunoglobulin gamma Fc receptor I (FCGR1A), cluster of differentiation 163 (CD163), complement C3a receptor 1 human (C3AR1), C-type lectin domain containing 5A (CLEC5A)-that overlapped DEIRGs. The GO and KEGG pathway analyses showed that the hub IRGs became enriched during acute-phase response, acute inflammatory response, specific granule, specific granule membrane, endocytic vesicle membrane, tertiary granule, immunoglobulin G (IgG) binding, complement receptor activity, Ig binding, scavenger receptor activity, and scaffold protein binding. The DEGs also played a significant role in Staphylococcus aureus (S. aureus) infection. The ROC curves showed that HP (AUC: 0.956, 95% CI: 0.924-0.988); FCGR1A (AUC: 0.895,95% CI: 0.827-0.963); CD163 (AUC: 0.838, 95% CI: 0.774-0.901); C3AR1 (AUC: 0.953, 95% CI: 0.913-0.993); and CLEC5A (AUC: 0.951, 95% CI: 0 920-0 981) had meaningful diagnostic value for sepsis. Survival analysis showed that the sepsis and control groups had significant differences in HP (P = .043) and CLEC5A (P < .001).

CONCLUSIONS

HP, FCGR1A, CD163, C3AR1, and CLEC5A have value for clinical application. Clinicians can use them as diagnostic biomarkers, and they provide research direction for treatment targets for sepsis.