Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms - Tao, Linfeng; Zhu, Yue; Liu, Jun (Frontiers in Genetics, 2023)
Sepsis, a serious inflammatory reaction that can be fatal, has a poorly understood pathophysiology. The metabolic syndrome, however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for sepsis, PBMC single cell RNA sequencing data for sepsis and microarray data for MetS were downloaded from the GEO database. Limma difference analysis identified 122 up-regulated genes and 90 down-regulated genes in sepsis and MetS. WGCNA identified brown co-expression modules as sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in sepsis and MetS. The immune infiltration results showed that Hub genes were expressed at high levels in all immune cells. After performing Seurat flow analysis on PBMC from normal and sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results showed that CFLAR plays an important role in the glycolytic pathway. Our study identified 7 hub genes that serve as co-diagnostic markers for sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolism pathway.