Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
Malar J. 2024 Feb 15;23(1):48. doi: 10.1186/s12936-024-04877-3.
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.
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. This study can deepen the understanding of the human immune response to P. falciparum infection and provide new targets for the prevention and control of malaria.
免疫原性细胞死亡（ICD）是一种受调控的细胞死亡方式，在应对各种应激源（包括癌细胞和病原体）时对激活免疫系统起着至关重要的作用。然而，ICD 如何参与人体对疟疾的免疫反应仍有待明确。
本研究对来自于横断面研究的恶性疟原虫感染队列的数据进行了分析，以确定 ICD 亚型及其与寄生虫血症和免疫反应的相关性。采用共识聚类的方法确定 ICD 亚型，并通过 ssGSEA 评估其与免疫景观的相关性。采用差异表达基因（DEGs）分析、功能富集、蛋白质-蛋白质相互作用网络以及机器学习（最小绝对收缩和选择算子（LASSO）回归和随机森林）等方法，鉴定与高寄生虫血症相关的 ICD 相关枢纽基因。构建了一个可视化这些基因与寄生虫血症水平相关性的列线图，并通过接受者操作特征（ROC）曲线评估其性能。
在恶性疟原虫感染队列中，鉴定出两种与 ICD 相关的亚型，与亚型 2 相比，亚型 1 表现出更好的适应性免疫反应和更低的寄生虫血症。DEGs 分析显示，在亚型 1 中，增殖信号通路、T 细胞受体信号通路和 T 细胞激活和分化呈上调趋势，而在亚型 2 中，细胞因子信号和炎症反应呈上调趋势。通过构建蛋白质-蛋白质相互作用网络和机器学习，鉴定出 CD3E 和 FCGR1A 作为候选枢纽基因。整合这些基因构建的列线图在训练集中表现出显著的高寄生虫血症分类性能，其 AUC 值范围为 0.695 至 0.737，在两个验证集中分别为 0.911 至 0.933 和 0.759 至 0.849。此外，还观察到这些基因的表达与恶性疟原虫感染的临床表现之间存在显著相关性。
本研究揭示了人类对恶性疟原虫感染的免疫反应中存在两种 ICD 亚型。鉴定出两种与 ICD 相关的候选枢纽基因，并构建了一个用于高寄生虫血症分类的列线图。本研究可以加深对人体对恶性疟原虫感染的免疫反应的理解，并为疟疾的预防和控制提供新的靶点。
