Identification of necroptotic biomarkers associated with immune microenvironment in sepsis based on the protein-protein interaction network and machine learning.

BACKGROUND

Necroptosis is inflammatorily sparked and closely associated with sepsis, but the crosstalk between necroptosis and inflammation in sepsis has rarely been studied in depth. This study is designed to reveal the role of necroptosis in the pathogenesis and development of sepsis by screening and validating hub necroptotic septic genes.

METHOD

We obtained datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEGs) and intersected them with necroptotic genes from the literature. Intersected genes were organized into one protein-protein interaction network (PPIN) and determined by machine learning algorithms as hub necroptotic DEGs (hub-NRDEGs). All hub-NRDEGs were examined for their septic change and diagnostic value and connected with septic changes in the immune microenvironment for their inflammatory roles. Experiments verified these genes using septic murine and cellular models.

RESULTS

We obtained 4974 DEGs and concentrated on 336 necroptosis-related genes by the intersection. Experiencing the dual selection of PPIN and machine learning analysis, three hub-NRDEGs, CD40LG, TXN, and AIM2, were identified. Based on their transcriptomic profile, we classified septic samples into two groups, revealing their signalling discrepancy. They correlate with most immunocytes, and their abundance differentiates the immune infiltration widely. Experimental validation converged with the transcriptomic profile of three hub-NRDEGs in septic status.

CONCLUSIONS

One three-panel necroptotic signature for diagnosing sepsis was proposed. We initially screened the association between hub-NRDEGs and immunocytes, and we validated the expressional change of hub-NRDEGs in the experimental sepsis. Our necroptotic gene signature may contribute to the rapid diagnosis of sepsis.