, and serve as potential molecular biomarkers for sepsis based on bioinformatics analysis.

PURPOSE

Sepsis is an unusual systemic reaction to what is sometimes an otherwise ordinary infection, and it probably represents a pattern of response by the immune system to injury. However, the relationship between biomarkers and sepsis remains unclear. This study aimed to find potential molecular biomarkers, which could do some help to patients with sepsis.

METHODS

The sepsis dataset GSE28750, GSE57065 was downloaded from the GEO database, and ten patients with or without sepsis from our hospital were admitted for RNA-seq and the differentially expressed genes (DEGs) were screened. The Metascape database was used for functional enrichment analysis and was used to found the differential gene list. Protein-protein interaction network was used and further analyzed by using Cytoscape and STRING. Logistic regression and Correlation analysis were used to find the potential molecular biomarkers.

RESULTS

Taking the intersection of the three datasets yielded 287 differential genes. The enrichment results included Neutrophil degranulation, leukocyte activation, immune effectors process, positive regulation of immune response, regulation of leukocyte activation. The top 10 key genes of PPI connectivity were screened using cytoHubba plugin, which were , , , , , , , , , and . All of the hub genes are higher expressed in health group of different databases. Logistic regression showed that , and proteins were analyzed and all of them were statistically significant. Correlation analysis showed that there was a statistically significant correlation between , and .

CONCLUSION

, , , , , , , , , are key genes in sepsis, which associated with the development of sepsis. However, , and may serve as the attractively potential molecular biomarkers for sepsis.