Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms Article Swipe
Zhechen Li
,
Yong‐Qiang Deng
,
Fei Xiao
,
Jiashu Sun
,
Qiang Zhao
,
Zetong Zheng
,
Gang Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1681989
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1681989
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2025.1681989
- https://public-pages-files-2025.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1681989/pdf
- OA Status
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- References
- 52
- OpenAlex ID
- https://openalex.org/W4415684378
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4415684378Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2025.1681989Digital Object Identifier
- Title
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Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-30Full publication date if available
- Authors
-
Zhechen Li, Yong‐Qiang Deng, Fei Xiao, Jiashu Sun, Qiang Zhao, Zetong Zheng, Gang LiList of authors in order
- Landing page
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https://doi.org/10.3389/fimmu.2025.1681989Publisher landing page
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https://public-pages-files-2025.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1681989/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://public-pages-files-2025.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1681989/pdfDirect OA link when available
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