Classification and clinical significance of immunogenic cell death-related genes in Plasmodium falciparum infection determined by integrated bioinformatics analysis and machine learning

• Published in: Malaria Journal
• Main Authors: Yan-hui Zhang, Li-hua Xie, Jian Li, Yan-wei Qi, Jia-jian Shi
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2024
• Subjects: Immunogenic cell death (ICD); Plasmodium falciparum; Machine learning; CD3E; FCGR1A; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/s12936-024-04877-3; https://doaj.org/article/c79f033473c04c84ab5a1b6b7db207fe

Abstract Background Immunogenic cell death (ICD) is a type of regulated cell death that plays a crucial role in activating the immune system in response to various stressors, including cancer cells and pathogens. However, the involvement of ICD in the human immune response against malaria remains to be defined. Methods In this study, data from Plasmodium falciparum infection cohorts, derived from cross-sectional studies, were analysed to identify ICD subtypes and their correlation with parasitaemia and immune responses. Using consensus clustering, ICD subtypes were identified, and their association with the immune landscape was assessed by employing ssGSEA. Differentially expressed genes (DEGs) analysis, functional enrichment, protein-protein interaction networks, and machine learning (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify ICD-associated hub genes linked with high parasitaemia. A nomogram visualizing these genes' correlation with parasitaemia levels was developed, and its performance was evaluated using receiver operating characteristic (ROC) curves. Results In the P. falciparum infection cohort, two ICD-associated subtypes were identified, with subtype 1 showing better adaptive immune responses and lower parasitaemia compared to subtype 2. DEGs analysis revealed upregulation of proliferative signalling pathways, T-cell receptor signalling pathways and T-cell activation and differentiation in subtype 1, while subtype 2 exhibited elevated cytokine signalling and inflammatory responses. PPI network construction and machine learning identified CD3E and FCGR1A as candidate hub genes. A constructed nomogram integrating these genes demonstrated significant classification performance of high parasitaemia, which was evidenced by AUC values ranging from 0.695 to 0.737 in the training set and 0.911 to 0.933 and 0.759 to 0.849 in two validation sets, respectively. Additionally, significant correlations between the expressions of these genes and the ...