Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed genes were identified using the R 4.3.2 ( https://www.r-project.org/ ), followed by gene set enrichment analysis. Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. The inflammatory and immune status of pediatric septic shock was evaluated through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The correlation between diagnostic markers and infiltrating immune cells was further examined. Overall, we detected 12 differentially expressed genes. CD177, MCEMP1, MMP8, and OLAH were examined as diagnostic indicators for pediatric septic shock, revealing statistically significant differences (P < 0.01) and diagnostic efficacy in the validation cohort. The immune cell infiltration analysis suggests that various immune cells may contribute to the onset of pediatric septic shock. Furthermore, all diagnostic characteristics may exhibit varying degrees of correlation with immune cells. This study identifies four potential biomarkers-CD177, MCEMP1, MMP8, and OLAH-that provide diagnostic value and novel insights into immune dysregulation in pediatric septic shock. Through the integration of bioinformatics and machine learning methodologies, we offer a novel perspective on the immune mechanisms involved in pediatric septic shock, potentially facilitating more targeted and personalized therapies for individual patients.