首页|Ministry of Education Reports Findings in Malaria (Classification and clinical significance of immunogenic cell death-related genes in Plasmodium falciparum infection determined by integrated bioinformatics analysis and machine learning)
Ministry of Education Reports Findings in Malaria (Classification and clinical significance of immunogenic cell death-related genes in Plasmodium falciparum infection determined by integrated bioinformatics analysis and machine learning)
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Springer Nature
New research on Mosquito-Borne Diseases - Malaria is the subject of a report. According to news originating from Fuzhou, People’s Republic of China, by NewsRx correspondents, research stated, “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.” Financial support for this research came from Natural Science Foundation of Fujian Province,China. Our news journalists obtained a quote from the research from the Ministry of Education, “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. 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 clinical manifestation of P. falciparum infection were observed. This study reveals the existence of two ICD subtypes in the human immune response against P. falciparum infection. Two ICD-associated candidate hub genes were identified, and a nomogram was constructed for the classification of high parasitaemia.”
FuzhouPeople’s Republic of ChinaAsiaBioinformaticsBiotechnologyCyborgsEmerging TechnologiesGeneticsHealth and MedicineHuman ParasitesInformation TechnologyMachine LearningMalariaMosquito-Borne DiseasesParasitemiaParasitic Diseases and ConditionsPlasmodium falciparumProtozoan ParasitesTropical Disease
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Springer Nature
