ArticleFrontiers in immunology2025
Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Bioinformatics Analysis of Ferroptosis-Related Driver Genes in Stanford Type A Aortic Dissection.Current issues in molecular biology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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