Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis

Abstract

Early identification of the death risk of sepsis may improve short-term prognosis. The objective of this study was to identify urinary proteomic biomarkers and create a model to predict short-term outcomes in sepsis patients. A total of 46 sepsis patients selected from the intensive care unit of a comprehensive tertiary hospital were enrolled in this study. We used data-independent acquisition (DIA) proteomics to detect proteins in the urinary of death patients (n = 14) and survivals (n = 32). KEGG and GO analyses were conducted to investigate the possible functions of these proteins. Feature variables were selected from the differentially expressed proteins using the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithms and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Nomogram model and ROC curves were constructed to evaluate the predictive efficacy of these identified protein biomarkers. In total, 2570 proteins were identified in urine. Statistical analysis revealed that 255 proteins exhibited differential expression, with 146 being upregulated and 109 downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses highlighted the involvement of key genes in processes such as the negative regulation of hemostasis, organization of the cortical actin cytoskeleton, the Rap1 signaling pathway, and cytoskeletal dynamics in muscle cells. Utilizing LASSO regression, random forest analysis, and a receiver operating characteristic (ROC) curve with an area under the curve (AUC) greater than 0.8, we identified potential protein biomarkers for predicting sepsis prognosis. Additionally, a nomogram incorporating biomarkers Solute Carrier Family 25 Member 24 (SLC25A24), Ubiquilin-1 (UBQLN1), and Cyclic AMP-responsive element-binding protein 3-like protein 3 (CREB3L3) demonstrated superior predictive accuracy for assessing the risk of sepsis-related mortality. This study has identified several novel proteomic biomarkers and has developed a practical prediction nomogram utilizing SLC25A24, UBQLN1, and CREB3L3 for the individualized prediction of sepsis mortality risk. This nomogram serves as a valuable tool in facilitating personalized treatment strategies.

Keywords: Biomarkers; Prediction model; Proteomics; Sepsis; Urine.