Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS), addressing the need for early sepsis diagnosis.

Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016-2018. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination (RFE) classification and cross-validation with logistic regression, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software.

275 patients were included (139 with sepsis and 136 with NISIRS. Plasma proteins were analyzed using mass spectrometry and evaluated through recursive feature elimination and cross-validation with a vector classifier. Twenty-five proteins showed statistically significant differences, with high diagnostic performance (sensitivity: 0.973, specificity: 0.920, accuracy: 0.960, AUC: 0.985). Fourteen proteins (VWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) were more associated with sepsis, while eleven (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) were linked to NISIRS. The study found upregulation of several proteins in sepsis (C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP) and downregulation of others (ITIH3, SERPINA4, AHSG).

These findings highlight distinct proteomic patterns between sepsis and NISIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers in the future.