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通过整合单细胞RNA测序分析和机器学习算法鉴定主动脉夹层新的脂质代谢相关生物标志物。

Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.

作者信息

Li Zhechen, Deng Yusong, Xiao Fei, Sun Jiashu, Zhao Qixu, Zheng Zetong, Li Gang

机构信息

Beijing Luhe Hospital, Capital Medical University, Beijing, China.

Pediatric Cardiac Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

出版信息

Front Immunol. 2025 Oct 30;16:1681989. doi: 10.3389/fimmu.2025.1681989. eCollection 2025.

Abstract

INTRODUCTION

Aortic dissection (AD) is a lethal disease with increasing incidence and limited preventive options, characterized by aortic media degeneration and inflammatory cell infiltration. Dysregulation of lipid metabolism is increasingly recognized as a pathological characteristic of AD; however, the exact molecular regulators and critical genetic determinants involved remain unclear.

METHODS

This study employed an integrative approach combining single-cell RNA sequencing and machine learning to identify novel lipid metabolism-related biomarkers in aortic dissection. Single-cell RNA sequencing data from aortic dissection and control samples were processed to analyze lipid metabolism activity and identify differentially expressed genes. Machine learning algorithms and protein-protein interaction networks were then used to prioritize biomarkers, which were further validated through bulk RNA-seq analysis and immune infiltration studies and experiments using an Ang II-induced aortic dissection mouse model.. Functional characterization included cell-cell communication analysis and pseudo-time trajectory reconstruction to elucidate the roles of candidate genes in aortic dissection pathogenesis.

RESULTS

This multi-modal strategy identified PLIN2 and PLIN3 as key regulators of lipid metabolism in aortic dissection. Analysis revealed significant up-regulation of lipid metabolism in aortic dissection, with PLIN2 and PLIN3 emerging as central regulators. Single-cell profiling showed these genes were highly expressed in monocytic cells, correlating with enhanced inflammatory signaling (e.g., SPP1, GALECTIN). Machine learning and bulk RNA-seq validation confirmed their diagnostic potential. Pseudo-time analysis linked PLIN2 to early monocyte differentiation, while cell-cell communication studies implicated it in pro-inflammatory crosstalk with smooth muscle cells. The upregulation of PLIN2 and its specific expression in macrophages were further confirmed in an Ang II-induced aortic dissection mouse model. Molecular docking screened for potential therapeutic compounds that may target PLIN2, among which ketoconazole was identified.

DISCUSSION

These findings suggest that PLIN2/PLIN3 could be key mediators of metabolic dysregulation and immune activation in aortic dissection, highlighting their potential as diagnostic markers and therapeutic targets.

摘要

引言

主动脉夹层(AD)是一种发病率不断上升且预防手段有限的致命疾病,其特征为主动脉中膜退变和炎性细胞浸润。脂质代谢失调日益被认为是AD的一种病理特征;然而,所涉及的确切分子调节因子和关键遗传决定因素仍不清楚。

方法

本研究采用单细胞RNA测序和机器学习相结合的综合方法,以鉴定主动脉夹层中与脂质代谢相关的新型生物标志物。对来自主动脉夹层和对照样本的单细胞RNA测序数据进行处理,以分析脂质代谢活性并鉴定差异表达基因。然后使用机器学习算法和蛋白质-蛋白质相互作用网络对生物标志物进行排序,并通过批量RNA测序分析、免疫浸润研究以及使用血管紧张素II诱导的主动脉夹层小鼠模型进行的实验进一步验证。功能表征包括细胞-细胞通讯分析和伪时间轨迹重建,以阐明候选基因在主动脉夹层发病机制中的作用。

结果

这种多模态策略确定PLIN2和PLIN3是主动脉夹层中脂质代谢的关键调节因子。分析显示主动脉夹层中脂质代谢显著上调,PLIN2和PLIN3成为核心调节因子。单细胞分析表明这些基因在单核细胞中高度表达,与增强的炎症信号传导(如SPP1、半乳糖凝集素)相关。机器学习和批量RNA测序验证证实了它们的诊断潜力。伪时间分析将PLIN2与早期单核细胞分化联系起来,而细胞-细胞通讯研究表明它参与了与平滑肌细胞的促炎串扰。在血管紧张素II诱导的主动脉夹层小鼠模型中进一步证实了PLIN2的上调及其在巨噬细胞中的特异性表达。分子对接筛选出可能靶向PLIN2的潜在治疗化合物,其中鉴定出酮康唑。

讨论

这些发现表明PLIN2/PLIN3可能是主动脉夹层中代谢失调和免疫激活的关键介质,突出了它们作为诊断标志物和治疗靶点的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28e7/12611683/aa0642ea03ef/fimmu-16-1681989-g001.jpg

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