Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis - Wang Y; Qiu X; Liu J; Liu X; Pan J; Cai J; Liu X; Qu S (Dove Medical Press, 2024)
Yuanfeng Wang,1,* Xu Qiu,1,* Jiao Liu,1,* Xuanyi Liu,1 Jialu Pan,2 Jiayi Cai,2 Xiaodong Liu,1,3 Shugen Qu1,3 1College of Public Health and Management, Zhejiang Provincial Key Laboratory of Watershed Science and Health, Wenzhou Medical University, Wenzhou, People's Republic of China; 2The First Clinical Medical College, Wenzhou Medical University, Wenzhou, People's Republic of China; 3South Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, People's Republic of China*These authors contributed equally to this work.These authors contributed equally to this workCorrespondence: Xiaodong Liu; Shugen Qu, Email forget45@wmu.edu.cn; shugenju@wmu.edu.cnIntroduction: Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets.Methods: The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples.Results: We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis.Discussion: Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.Keywords: machine learning, cuproptosis, immune infiltration, sepsis, diagnostic model