Background: Sepsis is an organ dysfunction syndrome caused by the body's dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.
Aims: This study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.
Methods: Pediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.
Results: Eight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all P<0.05). SIRT1, STAT3, and TLR2 were identified by the DGIdb database as potentially regulated by multiple drugs. Finally, 7 genes were verified to have significantly different expressions between the sepsis group and the control group (P<0.05).
Conclusion: The pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.
Keywords: MALT1 gene; immune landscape; machine learning (ML); pyroptosis; sepsis.
Copyright © 2023 Wang, Guo, Wang, Liao, Wang, Chen and Wang.