Diagnostic value of oxidative stress and ferroptosis in sepsis based on bioinformatics analysis
Objective To explore the diagnostic value of genes related to oxidative stress and ferroptosis in sepsis.Methods The differentially expressed genes(DEG)of the sepsis training set were filtered using R language and intersected with genes related to oxidative stress and iron death for gene ontology/Kyoto Encyclogedia of Genes and Genomes(GO/KEGG)pathway analysis.Diagnostic genes were identified through LASSO regression and SVM-RFE machine learning to develop the diagnostic model.Validation involved analyzing blood samples from three recently diagnosed sepsis patients(sepsis group)admitted to the Second Hospital of Lanzhou University in February 2024,along with blood samples from three healthy individuals(control group).Results Following the analysis of 876 DEG,a set of ten candidate genes were identified,along with significant enrichment in 557 GO entries and 32 KE GG pathway.After machine learning,TXN and TIMP1 were screened out and a diagnostic model was established,the area value of the model under the curve of the training set and verification set were both＞0.9.The expression levels of TXN and TIMP1 mRNA in blood samples of sepsis group were higher than those in control group(P＜0.05).After machine learning,TXN and TIMP1 were screened out and a diagnostic model was established,the area value of the model under the curve of the training set and verification set were both＞0.9.The expression levels of TXN and TIMP1 mRNA in blood samples of sepsis group were higher than those in control group(P＜0.05).Conclusion The TXN and TIMP1 gene model can effectively diagnose sepsis in its early stages,making them potential new biomarkers for sepsis.
BioinformaticsOxidative stressFerroptosisBiomarkersDiagnostic model
万方数据
维普
