Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in bloodstream infection by integrative bioinformatics analysis.

() is an opportunistic pathogen that could cause life-threatening bloodstream infections. The objective of this study was to identify potential diagnostic biomarkers of bloodstream infection. Gene expression dataset GSE33341 was optimized as the discovery dataset, which contained samples from human and mice. GSE65088 dataset was utilized as a validation dataset. First, after overlapping the differentially expressed genes (DEGs) in infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. Additionally, the receiver operating characteristic curve was leveraged to verify the efficacy of the hub genes. DRAM1 (AUC=1), PSTPIP2 (AUC=1), and UPP1 (AUC=1) were investigated and demonstrated significant expression differences (all P < 0.05) and diagnostic efficacy in the training and validation datasets. Furthermore, the relationship between the diagnostic markers and the abundance of immune cells was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). These three diagnostic indicators also correlated with multiple immune cells to varying degrees. The expression of DRAM1 was significantly positively correlated with B cell naive and mast cell activation, and negatively correlated with NK cells and CD4/CD8 T cells. The expression of PSTPIP2 was significantly positively correlated with macrophage M0, macrophage M1, B cell naive, and dendritic cell activation, while the expression of PSTPIP2 was negatively correlated with NK cells and CD4/CD8 T cells. Significant negative correlations between UPP1 expression and T cell CD4 memory rest and neutrophils were also observed. Finally, we established a mouse model of bloodstream infection and collected the blood samples for RNA-Seq analysis and RT-qPCR experiments. The analysis results in RNA-Seq and RT-qPCR experiments further confirmed the significant expression differences (all P < 0.05) of these three genes. Overall, three candidate hub genes (DRAM1, PSTPIP2, and UPP1) were identified initially for bloodstream infection diagnosis. Our study could provide potential diagnostic biomarkers for bloodstream infection patients.