Toward precision medicine: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

Background The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS). Methods Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016–2017. 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 a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software. Results 277 patients were included (141 with sepsis and 136 with NISIRS). After performing RFE, 30 proteins (SERPINA4, ITIH1, ITIH3, SERPINA3, F12, FN1, SERPINA6, APOE, GSN, C3, SERPINF1, C5, LBP, CD14, FCN3, C6, C1RL, PRDX2, APOB, PPBP, SAA1, VWF, LRG1, AFM, BTD, ORM1, RBP4, LUM, COL1A1, CA1) demonstrated an association with sepsis compared to patients with NISIRS with an accuracy of 0.49 ± 0.035, precision of 0.967 ± 0.037, specificity of 0.910 ± 0.103, sensitivity of 0.964 ± 0.035 and an area under the curve (AUC) of 0.937. Of these PPBP, V1RL, C5, vWF and SERPINA4 have a greater association with Sepsis compared to NISIRS. Conclusion There are proteomic patterns associated with sepsis compared to NISIRS with different strength of association. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutic targets in the future.