Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

Abstract

Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), 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 differential analysis identified 122 upregulated genes and 90 downregulated 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 show that Hub genes were expressed at high levels in all immune cells. After performing Seurat 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 show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway.

A novel anoikis-related gene signature predicts prognosis in patients with sepsis and reveals immune infiltration

“…Several researched have elucidated that CFLAR is associated with immune regulation, cardiac remodeling, and acute liver injury 29 – 31 . In the context of sepsis, CFLAR has been identified on neutrophils 32 , and it has been shown to down-regulate lipopolysaccharide-induced NF-κB activation 33 .…”

Peripheral immune cell death in sepsis based on bulk RNA and single-cell RNA sequencing

“…Linfeng Tao et el. Found new co-diagnostic genes for sepsis and metabolic syndrome through single-cell data analysis and machine learning algorithms [ 33 ]. Recently, Wenfeng Xie also found SPI1-mediated autophagy of peripheral blood monocytes during sepsis based on single-cell RNA sequencing [ 34 ].…”

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

“…Previous studies have demonstrated the diagnostic value of STAT3 in sepsis-induced myocardial disease and sepsis-related respiratory distress syndrome ( Zhang et al, 2019 ; Song et al, 2023 ). Metabolism-related genes ( She et al, 2022 ), necrosis-related genes ( She et al, 2023 ), immune-related genes ( Zheng et al, 2022 ) common DEGs related to sepsis and metabolic syndrome ( Tao et al, 2023 ), apoptosis-related genes ( Wang et al, 2022 ), inflammation response signature genes ( Jiang et al, 2022 ), and signature genes for septic shock in children ( Fan et al, 2022 ) have all shown some efficacy in the diagnosis and prognostic assessment of sepsis. In addition, metabolites based on metabolomics analysis ( Li Y. et al, 2023 ), m6A regulatory factors ( Li F. et al, 2023 ), and non-coding RNAs such as miR-147b ( Trung et al, 2021 ), lncRNA THAP9-AS1 , and TSPOAP1-AS1 ( Wu et al, 2020 ) have all been reported as potential diagnostic or prognostic indicators for sepsis or septic shock.…”