Evidence map›Paper›PMID 41246346›Full record

ArticleFrontiers in immunology2025

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

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

Erratum issuedAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Zhechen LiBeijing Luhe Hospital, Capital Medical University, Beijing, China.
Yusong DengPediatric Cardiac Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Fei XiaoDepartment of Anesthesiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Jiashu SunPediatric Cardiac Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Qixu ZhaoPediatric Cardiac Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Zetong ZhengBeijing Luhe Hospital, Capital Medical University, Beijing, China.
Gang LiPediatric Cardiac Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Aortic DissectionLipid MetabolismMachine LearningSingle-Cell AnalysisAnimalsBiomarkersDisease Models, AnimalGene Expression ProfilingHumansMaleMiceMice, Inbred C57BLPerilipin-2Protein Interaction MapsSequence Analysis, RNABiomarkersPerilipin-2aortic dissectionlipid metabolismmacrophagePLIN2single-cellRNA sequencing

Identifiers

PMID41246346
PMCPMC12611683

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.