Sepsis has a high fatality rate, and as such, is a major global public health problem. During the COVID-19 pandemic, some seriously ill individuals suffer from sepsis, including multiple organ dysfunction[18]. With a complicated pathophysiology that includes an imbalanced inflammatory response, immunological dysfunction, coagulation breakdown, and other mechanisms, sepsis causes functional alterations in several organs. Imbalance in the inflammatory response is a crucial factor in the pathogenesis of sepsis[19]. The number of relevant immune effector cells is reduced due to sepsis-induced immune cell death, which also has an immunosuppressive effect[20]. Cuproptosis, a recently discovered type of copper-dependent cell death characterized by the development of lipoylated mitochondrial enzymes, has been closely linked to the development of illness [21]. However, the processes of cuproptosis and its regulatory functions in sepsis have not yet been studied. Therefore, we attempted to clarify the precise function of cuproptosis-related genes in sepsis phenotyping and in the immune microenvironment. Furthermore, sepsis subtypes were predicted using gene signatures associated with cuproptosis.
We investigated how CRGs in sepsis patients and healthy people were differentially expressed. CRGs were much more highly expressed in sepsis patients than in healthy groups, indicating a key role for CRGs in the development of sepsis. To better understand the relationship between cuproptosis regulators and sepsis, we analyzed the correlation between CRGs. As shown by the presence of CRG interactions in sepsis patients, various cuproptosis modulators had notable synergistic or antagonistic effects. Sepsis is currently thought to be a disordered systemic inflammatory and immunological response [22]. We discovered that there were differences between the control participants and sepsis patients in the proportion of immune cells (neutrophils, monocytes, macrophages, M0, etc.). Neutrophils rise by 30–50% in the blood circulation when sepsis begins [23]. In the hyperinflammatory stage of sepsis, monocytes and macrophages are crucial for preventing infection [24]. Additionally, based on the expression landscapes of CRGs, we used unsupervised cluster analysis to demonstrate the various cuproptosis regulation patterns in patients with sepsis, and two different cuproptosis-related clusters were identified. According to cluster-specific DEGs, Cluster1 was largely enriched in phenylalanine metabolism, alpha-linolenic acid metabolism, and melanogenesis, whereas Cluster2 was identified by basal transcription factors, cell cycles, and P53 signaling metabolism. In the current study, we compared the performance of four different machine learning classifiers (RF, SVM, GLM, and XGB) based on the expression profiles of cluster-specific DEGs and developed an RF-based prediction model that demonstrated the highest predictive efficacy in the testing cohort (AUC = 0.983), indicating that RF-based machine learning has sufficient performance in predicting the subtypes of sepsis. Furthermore, to build a 5-gene-based RF model, we chose five key factors (HMGB2, SAR1B, TMEM165, JAK2, and CD58). It has been suggested that the concentration of high-mobility group box 1 (HMGB1) is a significant biological marker of various inflammation-related illnesses. These results suggest that individuals recovering from septic shock may develop proteolytic antibodies against HMGB1 [25]. JAK2 is essential for cytokine and growth factor signaling. Numerous inflammatory disorders and cancers have been associated with mutations in this gene. This study provides evidence that the JAK2-STAT pathway plays a critical role in the epigenetic regulation of tolerized genes' early responses to gram-negative bacterial endotoxins, as well as a pharmaceutical target to prevent exacerbations of these responses [26]. CD58 is a member of the immunoglobulin superfamily [27] and mediates the adhesion and activation of T lymphocytes [28]. It is involved in host defense against viral infections such as hepatitis B [29] and fungal germination at the level of the phagosome [30]. SAR1B encodes a small GTPase that functions as a homodimer. It is currently unknown how sepsis and SAR1B are associated. A putative transmembrane protein with a perinuclear Golgi-like distribution in fibroblasts is encoded by the gene TMEM165. The recently discovered congenital disorders of glycosylation subtype is connected to TMEM165 mutations [31].
The ability of the RF model to accurately predict sepsis in the external validation datasets offers new knowledge on the classification of sepsis. More significantly, we used HMGB2, SAR1B, TMEM165, JAK2, and CD58 to create a nomogram model for the diagnosis of the sepsis subgroups. We discovered that this prediction model demonstrated remarkable predictive efficacy, demonstrating its value in clinical applications. Additionally, in the US, people over 65 years of age account for more than half of all admissions to the intensive care unit, and sepsis has been shown to be especially dangerous for elderly patients [32]. Therefore, using sepsis samples from the GSE57065 and GSE65682 datasets, we conducted correlation analysis between these five predictor genes and age. The findings revealed that, in GSE57065, SAR1B was negatively correlated with age.
It's important to note several drawbacks on this study. First, further clinical or experimental assessments are required to validate the selected CRGs. Second, Our current study was based on a thorough bioinformatic analysis. Furthermore, to confirm the effectiveness of the prediction model, more thorough clinical characteristics are needed. Additionally, more sepsis samples are required to determine the accuracy of clusters associated with cuproptosis, and more research is needed to determine whether immune responses and CRGs are correlated. To validate the model, we also needed to recruit more sepsis patients.