Bioinformatics-based screening of sepsis biomarkers

Abstract

Abstract Purpose RNA-seq sequencing and bioinformatics methods were combined to identify differentially expressed genes,and Investigated new biomarkers for sepsis diagnosis and treatment. Methods Blood samples from 30 patients with sepsis, 10 normal volunteers, and 15 patients with systemic inflammatory response syndrome (systemic inflammatory response group) were collected in the Affiliated Hospital of Southwest Medical University for RNA-seq sequencing(TRN:ChiCTR1900021261,Date:2019.02.04).After differentiating the data, the Venn plot intersection, GO enrichment analysis, and protein interaction analysis were performed.Using the public dataset, a survival curve was constructed for the differential genes. The expression of different groups was verified as statistically significant, and then the ROC curve was constructed with sequencing data.Finally, with the help of single-cell transcriptome sequencing, the localization cell line of the core gene was identified. Results Comparing with the normal group, sepsis serum samples were screened for 365 differentially expressed genes: 85 were downregulated and 280 were upregulated. Compared with the systemic inflammatory response group, in the serum samples of patients with sepsis, 484 differential genes were identified.By intersection, 98 differentially expressed genes were identified, of which 184 were down-regulated and 300 were up-regulated. Among these differential genes GO function is enriched in specific granules, tertiary granules and specific granule cavities. CEBPE, IL1R2, CYSTM1, S100A9, FCER1A, MCEMP1, NELL2, SERPINB10 were found in the center of the protein interaction network analysis.Based on RNA-sequencing data, CEBPE was highly expressed in the sepsis group and NELL2 was low in the group.The survival curve showed that the lower the CEBPE expression in patients with sepsis, the higher the NELL2 expression and the higher the survival rate. Based on the ROC curves, CEBPE had an AUC of 0.920 (normal), 0.882 (systemic inflammatory response group), while NELL2 had an AUC of 0.960(normal), 0.844 (systemic inflammatory response group).In single-cell sequencing, CEBPE was mainly found in macrophage cells and NELL2 was found in T cells. Conclusion CEBPE expression in macrophages is positively correlated with sepsis mortality. NELL2 expression in T cell lines is positively correlated with survival rates in sepsis patients.Both have good diagnostic value, or they can be used as new research targets.