Immunity, inflammation and disease
Identification of SORT1 as a Key Regulator of Antigen Presentation in CD16+ Monocytes During Septic Shock via Multi-Omics and In Silico Gene Knockout Analysis
By Wu S, Zhang J, Guo S and 4 others.
Abstract
Objective
This study aimed to screen and identify hub genes correlated with septic shock through integrative analyses of bulk transcriptomics, machine learning, weighted gene co-expression network analysis (WGCNA), single-cell transcriptomics, and in silico gene knockout, and further dissect their underlying immunoregulatory mechanisms.
Methods
Multiple bulk transcriptomic datasets of septic shock were integrated and processed via batch correction and differential expression analysis, followed by WGCNA. Candidate genes were screened by intersecting disease-related differentially expressed genes, module genes derived from WGCNA, and predicted drug/compound targets. A diagnostic classifier was established by integrating 113 machine learning algorithms, with its predictive performance assessed using a training cohort and an independent external validation cohort. SHAP analysis was implemented to quantify and interpret the feature importance of model biomarkers. Single-cell transcriptomic data from the dataset GSE175453 were subsequently used to characterize the cell-type localization of hub genes, and scTenifoldKnk was adopted to conduct in silico gene knockout experiments on pivotal genes. Finally, gene set enrichment analysis (GSEA) based on Z-score ranking was performed to dissect the underlying regulatory signaling pathways of these hub genes.