This study explored the effect of Lactate Metabolism-Related Genes (LMRGs) in the diagnosis and prognosis of Sepsis. 599 LMRGs were gained via the Molecular Signatures and GeneCards databases. Next, the authorssifted out Differently Expressed LMRGs (DE-S-LMRGs) through differential expression analysis, Protein-Protein Interactions (PPI) networks, and Weighted Gene Co-expression Network Analysis (WGCNA). Then, DE-S-LMRGs were subjected to Least-Absolute Shrinkage and Selection Operator (LASSO), gene expression patterns, Receiver Operating Characteristic (ROC), and Kaplan-Meier (K-M) survival analyses for obtaining diagnostic genes. Subsequently, the authorsyielded independent factors via univariate and multivariate COX analyses. The diagnostic genes expression levels, immune cell infiltration, immune checkpoint, Human Leukocyte Antigen (HLA) molecules, and K-M survival curves between the clusters were compared. Finally, the authorsapplied quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) for verifying the expression of diagnostic genes in the light of normal and Sepsis samples. Four diagnostic genes (APRT, ARG1, UMPS, and LDHB) were identified, which were mainly concentrated in energy metabolism-related functions and immune-related pathways. In addition, age and ARG1 were selected as independent prognostic factors. The two clusters of GSE65682 datasets show significant differences in the expression, immune cells, immune checkpoints, HLA molecules, and survivability of the four diagnostic genes. qRT-PCR revealed that the expression levels of four diagnostic genes were congruent with the results of bioinformatics analysis. APRT, ARG1, UMPS, and LDHB might be new ideas for studies related to the diagnosis and treatment of Sepsis.
Pan et al. (2025) studied this question.