Transcriptomic bioinformatics analysis proposes a novel BCL2-MAPK14-TXN oxidative stress diagnostic model of sepsis and identifies TXN as an oxidative stress-related signature gene in sepsis.

BACKGROUND

Oxidative stress was one of key factors driving the septic development by the uncontrolled accumulation of free radicals, thus oxidative stress-related biomarkers provide a novel diagnostic option. This study focused on screening oxidative stress-related genes and validating their diagnostic utility.

METHODS

We obtained microarray datasets (GSE65682, GSE95233, GSE131761, and GSE33118) from the NCBI Gene Expression Omnibus (GEO) database and assigned them to the training, test, and validation cohorts. In the training cohort, differential expression genes (DEGs) were screened and intersected with oxidative stress-related genes for oxidative stress-related DEGs (OSDEGs). Machine learning algorithms were applied when selecting hub-OSDEGs, and examinations of their septic change and diagnostic value were replicated in test and validation cohorts. Immune infiltration analyses by CIBERSORT and ssGSEA were conducted, and the single-cell RNA sequencing dataset (GSE175453) was also analysed. Experimental validation proceeded to seek the reliability of bioinformatical results.

RESULTS

BCL2, MAPK14, and TXN were determined by machine learning algorithms. One diagnostic nomogram was established and validated triply in silico, showing excellent diagnostic efficacy in distinguishing septic status from control status. TXN significantly correlated with the abundance of most immunocytes, and its role in septic oxidative stress was initially confirmed experimentally.

CONCLUSIONS

One novel BCL2-MAPK14-TXN predictive model of sepsis was proposed, and the role of TXN in regulating oxidative stress in sepsis was initially explored. Further steps should be taken in promoting the application.