Diagnostic and predictive values of pyroptosis-related genes in sepsis

Abstract

BackgroundSepsis 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.AimsThis 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.MethodsPediatric 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.ResultsEight 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).ConclusionThe pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis

“…We also looked at the connection between this diagnostic model and immune cell infiltration and discovered that the two were closely related. This is because cellular immune regulation involving immune cells plays There is an undeniable similarity between our study and the earlier ones [14][15][16][17] in that both used various highthroughput sequencing data from pediatric sepsis, both used machine learning to find diagnostic genes and finally screened for two common genes (CD177 and MMP8) that can accurately diagnose pediatric sepsis. However, our study still has several strengths.…”

Mitigation of Sepsis-Induced Acute Lung Injury by BMSC-Derived Exosomal miR-125b-5p Through STAT3-Mediated Suppression of Macrophage Pyroptosis

“… 45 , 46 Notably, recent research has increasingly focused on the influence of STAT3 on the progression of different diseases through the regulation of cell death, including sepsis and lung injury. 47 , 48 Additionally, certain studies have demonstrated that p-STAT3 facilitates the acetylation of histone H3 and H4 on the NLRP3 promoter, as well as the activation of the NLRP3 inflammasome, which may be directly regulated by p-STAT3. 48 Furthermore, several studies have demonstrated the impact of STAT3 on pyroptosis through its influence on other targets, including AIM2 and GSDME.…”

Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning

“…Despite the utilization of diverse high-throughput sequencing data from pediatric sepsis and the application of machine learning techniques to identify characteristic genes, our study presents distinct advantages compared to previous investigations. 17,23,24 Firstly, our diagnostic model exhibits enhanced diagnostic efficacy, as evidenced by its successful application to clinical samples and subsequent validation using three publicly available datasets. Secondly, our developed diagnostic model demonstrates effectiveness in distinguishing between bacterial and non-bacterial etiologies of pediatric sepsis (AUC=0.82).…”