PDF(Pubmed)
Abstract：
UNASSIGNED: Age-related Macular Degeneration (AMD) poses a growing global health concern as the leading cause of central vision loss in elderly people.
UNASSIGNED: This study focuses on unraveling the intricate involvement of Natural Killer (NK) cells in AMD, shedding light on their immune responses and cytokine regulatory roles.
UNASSIGNED: Transcriptomic data from the Gene Expression Omnibus database were utilized, employing single-cell RNA-seq analysis. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and single-cell regulatory network inference and clustering (SCENIC) analysis were applied to reveal the regulatory mechanisms of NK cells in early-stage AMD patients. Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD.
UNASSIGNED: Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. The integration of hub genes and TFs identified CREM, FOXP1, IRF1, NFKB2, and USF2 as potential predictors for AMD through machine learning.
UNASSIGNED: This comprehensive approach enhances our understanding of NK cell dynamics, signaling alterations, and potential predictive models for AMD. The identified TFs provide new avenues for molecular interventions and highlight the intricate relationship between NK cells and AMD pathogenesis. Overall, this study contributes valuable insights for advancing our understanding and management of AMD.
UNASSIGNED: This study focuses on unraveling the intricate involvement of Natural Killer (NK) cells in AMD, shedding light on their immune responses and cytokine regulatory roles.
UNASSIGNED: Transcriptomic data from the Gene Expression Omnibus database were utilized, employing single-cell RNA-seq analysis. High-dimensional weighted gene co-expression network analysis (hdWGCNA) and single-cell regulatory network inference and clustering (SCENIC) analysis were applied to reveal the regulatory mechanisms of NK cells in early-stage AMD patients. Machine learning models, such as random forests and decision trees, were employed to screen hub genes and key transcription factors (TFs) associated with AMD.
UNASSIGNED: Distinct cell clusters were identified in the present study, especially the T/NK cluster, with a notable increase in NK cell abundance observed in AMD. Cell-cell communication analyses revealed altered interactions, particularly in NK cells, indicating their potential role in AMD pathogenesis. HdWGCNA highlighted the turquoise module, enriched in inflammation-related pathways, as significantly associated with AMD in NK cells. The SCENIC analysis identified key TFs in NK cell regulatory networks. The integration of hub genes and TFs identified CREM, FOXP1, IRF1, NFKB2, and USF2 as potential predictors for AMD through machine learning.
UNASSIGNED: This comprehensive approach enhances our understanding of NK cell dynamics, signaling alterations, and potential predictive models for AMD. The identified TFs provide new avenues for molecular interventions and highlight the intricate relationship between NK cells and AMD pathogenesis. Overall, this study contributes valuable insights for advancing our understanding and management of AMD.
摘要：
与年龄相关的黄斑变性（AMD）作为老年人中央视力丧失的主要原因，引起了全球日益增长的健康问题。
■这项研究的重点是揭示自然杀伤（NK）细胞在AMD中的复杂参与，阐明它们的免疫反应和细胞因子调节作用。
■使用来自基因表达综合数据库的转录组数据，采用单细胞RNA-seq分析。应用高维加权基因共表达网络分析（hdWGCNA）和单细胞调控网络推断和聚类（SCENIC）分析揭示早期AMD患者NK细胞的调控机制。机器学习模型，如随机森林和决策树，用于筛选与AMD相关的hub基因和关键转录因子(TFs)。
■在本研究中确定了不同的细胞簇，特别是T/NK簇，在AMD中观察到NK细胞丰度显著增加。细胞-细胞通讯分析揭示了改变的相互作用，特别是在NK细胞中，表明它们在AMD发病机制中的潜在作用。HdWGCNA强调了绿松石模块，富含炎症相关途径，与NK细胞中的AMD显著相关。场景分析确定了NK细胞调控网络中的关键TF。集线器基因和TFs的整合确定了CREM，FOXP1、IRF1、NFKB2和USF2通过机器学习作为AMD的潜在预测因子。
■这种全面的方法增强了我们对NK细胞动力学的理解，信号改变，和AMD的潜在预测模型。鉴定的TF为分子干预提供了新的途径，并突出了NK细胞与AMD发病机理之间的复杂关系。总的来说,这项研究为推进我们对AMD的理解和管理提供了有价值的见解.
■这项研究的重点是揭示自然杀伤（NK）细胞在AMD中的复杂参与，阐明它们的免疫反应和细胞因子调节作用。
■使用来自基因表达综合数据库的转录组数据，采用单细胞RNA-seq分析。应用高维加权基因共表达网络分析（hdWGCNA）和单细胞调控网络推断和聚类（SCENIC）分析揭示早期AMD患者NK细胞的调控机制。机器学习模型，如随机森林和决策树，用于筛选与AMD相关的hub基因和关键转录因子(TFs)。
■在本研究中确定了不同的细胞簇，特别是T/NK簇，在AMD中观察到NK细胞丰度显著增加。细胞-细胞通讯分析揭示了改变的相互作用，特别是在NK细胞中，表明它们在AMD发病机制中的潜在作用。HdWGCNA强调了绿松石模块，富含炎症相关途径，与NK细胞中的AMD显著相关。场景分析确定了NK细胞调控网络中的关键TF。集线器基因和TFs的整合确定了CREM，FOXP1、IRF1、NFKB2和USF2通过机器学习作为AMD的潜在预测因子。
■这种全面的方法增强了我们对NK细胞动力学的理解，信号改变，和AMD的潜在预测模型。鉴定的TF为分子干预提供了新的途径，并突出了NK细胞与AMD发病机理之间的复杂关系。总的来说,这项研究为推进我们对AMD的理解和管理提供了有价值的见解.
