Discussion
bacterial infection, sepsis, Machine learning, septic
REGRESSION, SEPSIS, BACTERIAL INFECTION
In this study, metabolomics analysis identified a total of 532 differentially expressed metabolites (DEMs). These DEMs were primarily involved in various metabolic pathways, including amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine, and threonine metabolism, as well as arginine and proline metabolism. Machine learning algorithms SVM and RF were utilized to identify four metabolic biomarkers (3,4-Dihydrocoumarin, Phenol, Benzaladehyde, and DL-phenylalanine) that are associated with the diagnosis of sepsis. Additionally, an analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine-metabolism-related gene MAOA in the sepsis group. Inhibition of MAOA with the inhibitor RS-8359 resulted in improved mean arterial pressure and survival rate in septic rats. Finally, univariate and multivariate Cox regression analysis identified six DEMs (FA (22:5), L-Thyronic, Cer/AS (d17:3/16:2), Diethylphophase, Hydroxyco-tinine, and Syringol) that are associated with the prognosis of sepsis.Machine learning algorithms have played a crucial role in the mining of metabolomic data [Previous studies have shown that bacterial infection can lead to an imbalance of amino acid metabolism [This study utilized metabolomics, machine learning algorithms, and prognosis analysis to screen potential metabolic biomarkers associated with the diagnosis and prognosis of sepsis. Moreover, it specifically examined the alterations in phenylalanine metabolism following sepsis and subsequently analyzed the regulatory mechanism of MAOA. However, since this study is considered exploratory and based on a small sample size of very new biomarkers, we opted to use completely healthy individuals as the control group. This decision could potentially lead to an overestimation of the sensitivity and specificity of the biomarkers. In future large sample studies, we will select case-controls as the control group to authenticate the diagnostic efficacy of these biomarkers.