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
Introduction<p>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...
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2025
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| author | Zhechen Li (15456929) |
| author2 | Yusong Deng (3841942) Fei Xiao (216199) Jiashu Sun (1340271) Qixu Zhao (9653241) Zetong Zheng (22069658) Gang Li (34549) |
| author2_role | author author author author author author |
| author_facet | Zhechen Li (15456929) Yusong Deng (3841942) Fei Xiao (216199) Jiashu Sun (1340271) Qixu Zhao (9653241) Zetong Zheng (22069658) Gang Li (34549) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhechen Li (15456929) Yusong Deng (3841942) Fei Xiao (216199) Jiashu Sun (1340271) Qixu Zhao (9653241) Zetong Zheng (22069658) Gang Li (34549) |
| dc.date.none.fl_str_mv | 2025-11-20T15:13:20Z |
| dc.identifier.none.fl_str_mv | 10.3389/fimmu.2025.1681989.s001 |
| dc.relation.none.fl_str_mv | 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 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetic Immunology macrophage lipid metabolism aortic dissection PLIN2 single-cellRNA sequencing |
| dc.title.none.fl_str_mv | 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 |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Introduction<p>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. </p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_b47b122f50a0892fa25bf4e9cbcff841 |
| identifier_str_mv | 10.3389/fimmu.2025.1681989.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30667010 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.zipZhechen Li (15456929)Yusong Deng (3841942)Fei Xiao (216199)Jiashu Sun (1340271)Qixu Zhao (9653241)Zetong Zheng (22069658)Gang Li (34549)Genetic Immunologymacrophagelipid metabolismaortic dissectionPLIN2single-cellRNA sequencingIntroduction<p>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. </p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p>2025-11-20T15:13:20ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fimmu.2025.1681989.s001https://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/30667010CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306670102025-11-20T15:13:20Z |
| spellingShingle | 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 Zhechen Li (15456929) Genetic Immunology macrophage lipid metabolism aortic dissection PLIN2 single-cellRNA sequencing |
| status_str | publishedVersion |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Genetic Immunology macrophage lipid metabolism aortic dissection PLIN2 single-cellRNA sequencing |