Identification of pyroptosis-related gene S100A12 as a potential diagnostic biomarker for sepsis through bioinformatics analysis and machine learning

Abstract

Sepsis is a non-discriminatory inflammatory reaction that can result in a diverse array of organ dysfunctions, which can be fatal. Pyroptosis is a programmed mechanism of cell death that is distinguishable from apoptosis and other forms of cellular demise. However, the role of pyroptosis in sepsis remains to be further explored. In this study, by employing a combination of the difference analysis, WGCNA, Friends' analysis, and machine learning, the central gene S100A12 was successfully identified. S100A12 demonstrated superb diagnostic capabilities in both the integrated and external validation datasets. Furthermore, significant disparities were observed in the levels of monocytes, eosinophils, and neutrophils between sepsis patients and the control group, as per the findings of immune infiltration analysis. The aforementioned immune infiltrating cells exhibited an increase in expression levels among patients diagnosed with sepsis and were found to be significantly and positively associated with S100A12 expression. The results of the single-cell analysis indicated a significant expression of S100A12 in both neutrophils and monocytes, which was in complete alignment with the outcomes of immune infiltration. In summary, the pyroptosis-related gene S100A12 represents a potential biomarker for the diagnosis and treatment of sepsis.