Data Sheet 1_Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.zip
收藏NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Data_Sheet_1_Identification_of_novel_lipid_metabolism-related_biomarkers_of_aortic_dissection_by_integrating_single-cell_RNA_sequencing_analysis_and_machine_learning_algorithms_zip/30667010
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IntroductionAortic 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.
MethodsThis 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.
ResultsThis 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.
DiscussionThese 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.
创建时间:
2025-11-20



