Diagnostic value of cuproptosis-related genes in sepsis: A novel model based on bioinformatics screening

Abstract

Abstract Background: Sepsis is a life-threatening functional disorder of the organs resulting from a dysregulated host immune response to infection and is a leading cause of death and critical illness worldwide. Genetic diagnosis combined with big data analysis of existing biomarkers has great potential in the diagnosis and prognosis prediction of sepsis, and there is an urgent need to construct prognostic models that will improve the effectiveness of treatment decisions. Methods: We used data from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database to perform a comprehensive analysis of differential gene expression profiles associated with cuproptosis in sepsis. Combining the sepsis datasets (GSE131761 and GSE54514) as test sets, a total of 208 sepsis samples and 69 normal samples were used for the analysis of cuproptosis-related differentially expressed genes (CRDEGs), weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The gene sets from the Molecular Signatures Database (MSigDB) and were used to perform GSVA (Gene Set Variation Analysis) and GSEA (Gene-set Enrichment Analysis). The prognostic performance of the hub genes in the CRDEGs diagnostic model was examined in the validation set (GSE25504 and GSE26378), and receiver operating characteristic curves (ROC) were plotted. We constructed a Cox regression model and drew a nomogram based on the final screened CRDEGs. The prognostic Calibration and decision curve analysis were used to evaluate the model. Finally, we constructed a protein-protein interaction network (PPI network) and performed ceRNA network analysis and immune cell infiltration abundance correlation analysis. Results: We obtained two sepsis disease subtype groups based on clustering analysis of differentially expressed cuproptosis hub genes (LIPT1, PDHB, MTF1, GLS, SLC31A1). GO and KEGG analyses indicated that sepsis-related cuproptosis alterations were primarily enriched in cellular copper ion homeostasis, regulation of respiratory gaseous exchange by neurological system process, suckling behavior, protein-cofactor linkage. WGCNA yielded six cuproptosis-related gene co-expression modules and 202 CRDEGs between subgroups of sepsis. A total of 32 CRDEGs were extracted additionally based on LASSO analysis calculations, of which 23 CRDEGs were included in the optimized diagnostic gene labels used for constructing Cox regression models and plotting nomogram. Finally, in the immune infiltration analysis, there was a statistically significant relationship between the abundance of immune infiltration of 16 immune cells and the expression of CRDEGs. Conclusions: The diagnostic model we constructed on CRDEGs has promising predictive power, paving the way for further exploration of the mechanisms related to cuproptosis in sepsis and providing new ideas for discovering potential biomarkers and diagnostic patterns for sepsis.