Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)

Artemisinin attenuates IgM xenoantibody production via inhibition of T cell-independent marginal zone B cell proliferation

Artemisinin (ART) has been shown to suppress B cell activation and plasma cell formation. However, its effect on splenic marginal zone (MZ) B cells is unknown. Splenic MZ B cells play a critical role in rapidly induced Ab production against blood-borne foreign Ags. Dysfunction of MZ B cells, due to inhibition of its proliferation or displacement of its homing, results in an attenuated adaptive humoral response. Here, we investigate the effect of ART on splenic MZ B (CD19+ CD21high CD23low ) and B10 (CD19+ CD1dhigh CD5+ ) B cells to explore the mechanisms of ART-induced immunosuppression in T cell-deficient nude mice challenged with hamster xenoantigens. In this study, we demonstrate that ART decreases T cell-independent xenogeneic IgM Ab production and, this is associated with a strong suppression of MZ B cell proliferation and a relative increase of CD21low CD23+ follicular and B10 B cells. In addition, this suppression impairs IL-10 production. Taken together, our data indicate that ART suppresses B cell immune responses through a distinctive effect on splenic MZ B and other B cells. This represents a new mechanism of ART-induced immunosuppression.status: publishe

Additional file 1 of Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning

Supplementary Material 1: Table S1. Baseline characteristics of the patients of GSE25504 (GPL6947 platform