Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Ge Zhang; Xiaolin Cui; Zhen Qin; Zeyu Wang; Yongzheng Lu; Yanyan Xu; Shuai Xu; Laiyi Tang; Li Zhang; Gangqiong Liu; Xiaofang Wang; Jinying Zhang; Junnan Tang

doi:10.1016/j.isci.2023.107587

Article · 2023 Deep GEO

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

iScience

Full Authors

Ge Zhang
Xiaolin Cui
Zhen Qin
Zeyu Wang
Yongzheng Lu
Yanyan Xu
Shuai Xu
Laiyi Tang
Li Zhang
Gangqiong Liu
Xiaofang Wang
Jinying Zhang
Junnan Tang

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Official Abstract

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

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Plain citation

Ge Zhang, Xiaolin Cui, Zhen Qin, Zeyu Wang, Yongzheng Lu, Yanyan Xu, Shuai Xu, Laiyi Tang, Li Zhang, Gangqiong Liu, Xiaofang Wang, Jinying Zhang, Junnan Tang. Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability. iScience. 2023;26(9):107587. https://doi.org/10.1016/j.isci.2023.107587

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Research & review context

Research fields: Atherosclerosis research
Computational cardiovascular medicine
Review topics: Molecular assessment of plaque vulnerability
Transcriptomic characterization of coronary disease
Scientific role: Transcriptomic machine-learning framework connecting plaque-vulnerability signatures with bulk and single-cell biological interpretation.
Evidence position: Retrospective multicohort human expression analysis; a research framework, not an approved diagnostic test or demonstrated treatment benefit.
Related concepts: Molecular plaque stratification
Bulk-to-single-cell interpretation
Possible citation contexts: Methods: computational approaches to molecular plaque assessment.
Evidence synthesis: plaque-vulnerability expression signatures and their cellular context, without replacing coronary imaging or clinical assessment.

Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.

Questions & evidence · Evidence limits · Original article & citation downloads

Evidence Scale

APVS / APVSLevel: machine-learning and single-cell quantification of atherosclerotic plaque vulnerability

A multicohort transcriptomic and single-cell framework that derives a 14-gene machine-learning signature and APVSLevel system for classification, quantification, and biological interpretation of atherosclerotic plaque vulnerability.

bulk transcriptomic cohorts: 22
bulk samples reported in Summary: 2,235
single-cell cohorts: 2
single-cell samples reported in Summary: 14
GSE159677 single-cell scale: 43,964 cells from 3 atherosclerotic cores and 3 adjacent normal tissues
GSE184073 single-cell scale: 2,237 cells from plaques of 3 STEMI and 4 CCS patients

Counting note

The publication reports 2,235 samples from 22 bulk cohorts and 14 samples from two single-cell cohorts in the Summary, while detailed single-cell analyses describe 3 atherosclerotic cores plus 3 adjacent normal tissues and plaques from 3 STEMI plus 4 CCS patients. These modality-specific counts are preserved as reported and are not combined into a derived unique-participant total.

Research Question

Can an integrative machine-learning and transcriptomic framework identify a robust molecular signature of atherosclerotic plaque vulnerability and quantify the transition from stable to unstable disease across bulk and single-cell resolution?

Author Evidence Summary

APVS is a transcriptome-based molecular signature developed to characterize atherosclerotic plaque vulnerability across multiple stages of coronary artery disease. The study integrated 22 bulk transcriptomic cohorts with two single-cell RNA-sequencing cohorts and used a nine-learner machine-learning framework to reduce 96 dysregulated co-expression pattern genes to a 14-gene atherosclerotic plaque vulnerability signature. The APVS classifier discriminated clinically and pathologically distinct states, including STEMI versus chronic coronary syndrome, advanced versus early atherosclerotic plaque, and ruptured versus stable plaque, across multiple independent datasets. To extend classification toward biological and clinical interpretation, the study developed APVSLevel, a continuous quantification system associated with coronary stenosis, major adverse cardiovascular events, plaque inflammatory activity, and cellular remodeling. Single-cell analyses linked higher APVSLevel to vulnerable plaque states, inflammatory macrophage programs, and altered macrophage-myofibroblast communication. These results support APVS/APVSLevel as a research framework for molecular characterization and risk stratification of plaque vulnerability; prospective clinical validation remains necessary before routine clinical use.

Key Findings

KF1 The integrative machine-learning program reduced 96 dysregulated co-expression pattern genes to a 14-gene APVS signature.

APVS generation and model selection across nine classical machine-learning learners

Evidence for KF1
Measure	Value
candidate DCPGs	96
machine-learning learners	9
selected APVS genes	14
optimal learners	random forest and backpropagation neural network
learner evaluation	10-fold cross-validation with 100 repetitions
feature selection	RF-RFE with 10-fold, 10-repeated cross-validation

Source locator: Results: Machine learning-based integrative program generates APVS; Fig. 2A-E; Methods: APVS generated from an integrative program

Permalink to KF1

KF2 The APVS classifier showed strong discrimination across independent datasets spanning clinical and pathological atherosclerotic states.

Independent external validation of STEMI/CCS, plaque-stage, plaque-rupture, and STEMI/healthy comparisons

Evidence for KF2
Measure	Value
GSE59867 STEMI vs CCS AUC	0.985
GSE62646 STEMI vs CCS AUC	0.997
GSE28829 advanced vs early plaque AUC	0.952
GSE41571 ruptured vs stable plaque AUC	0.972
GSE48060 STEMI vs healthy AUC	0.871
GSE60993 STEMI vs healthy AUC	0.916
GSE141512 STEMI vs healthy AUC	1.000

Source locator: Results: Robust performance of APVS-based classifier; Fig. S2B and Fig. 3A-B

Permalink to KF2

KF3 Higher APVSLevel was associated with worse cardiovascular prognosis and greater coronary atherosclerotic severity.

Clinical interpretability analyses across MACE and coronary stenosis datasets

Evidence for KF3
Measure	Value
MACE association	HR 3.819; p<0.01
high vs low APVSLevel MACE incidence	log-rank p<0.01
APVSLevel vs coronary stenosis degree	p<0.001
APVSLevel vs Duke CAD Index	p<0.01

Source locator: Results: The clinical interpretability underlying APVSLevel; Fig. 4A-D and Fig. 4J

Permalink to KF3

KF4 APVSLevel reproduced plaque-vulnerability differences at single-cell resolution.

Two single-cell RNA-sequencing cohorts comparing atherosclerotic core with adjacent normal tissue and vulnerable STEMI plaques with stable CCS plaques

Evidence for KF4
Measure	Value
GSE159677 cells	43,964
atherosclerotic core vs adjacent normal APVSLevel	higher in atherosclerotic core; p<0.0001
GSE184073 cells	2,237
vulnerable STEMI vs stable CCS plaque APVSLevel	higher in vulnerable STEMI plaques; p<0.0001

Source locator: Results: Single-cell resolution interpretation of the biological significance of APVSLevel; Fig. 7A-B and Fig. S13A

Permalink to KF4

KF5 High plaque vulnerability was linked to inflammatory macrophage states and loss of plaque-stabilizing fibrotic macrophage phenotypes.

Single-cell macrophage-state analysis in vulnerable STEMI versus stable CCS plaques

Evidence for KF5
Measure	Value
CXCL3+/IL1B+ inflammatory macrophages	higher APVSLevel in STEMI plaques
C1Q+ fibrotic macrophages	lower APVSLevel in STEMI plaques
APVSLevel-high atherosclerotic core	more monocyte/macrophage and endothelial cells; fewer myofibroblast and SMC populations

Source locator: Results: Single-cell resolution interpretation; Fig. 7C-E and Fig. S14

Permalink to KF5

KF6 APVSLevel-high plaques showed a more inflammatory, procoagulant, and matrix-destabilizing molecular state, whereas APVSLevel-low plaques were relatively stromal-rich and fibrotic.

Bulk and single-cell pathway-level biological interpretation

Evidence for KF6
Measure	Value
APVSLevel-high pathways	coagulation cascade, collagen degradation, Notch signaling, TLR signaling
APVSLevel-high cell programs	inflammatory response, granulocyte activation, macrophage M1 polarization
APVSLevel-low phenotype	stromal-rich, fibroblast migration, fibrous content, relative immune suppression
single-cell high-vulnerability programs	coagulation, hypoxia, TNF signaling, heme metabolism, oxidative stress, senescence, DNA damage

Source locator: Results: The biological implications underlying APVSLevel; Fig. 5, Fig. S6-S8, Fig. 7E, and Fig. S14D-E

Permalink to KF6

KF7 Plaque vulnerability was accompanied by remodeling of macrophage-myofibroblast communication networks.

CellChat and NicheNet analyses of APVSLevel-high versus APVSLevel-low atherosclerotic core states

Evidence for KF7
Measure	Value
pro-inflammatory signaling	MIF and VCAM strengthened in APVSLevel-high states
profibrogenic signaling	SPP1 and FN1 reduced in APVSLevel-high states
Mono/Mac-directed ligand-target axes	IL34/CTSK; IL6/SOCS3; AGT/SPP1; APOE/SPP1
myofibroblast-directed ligand-target axes	CXCL2/CDKN1A; ADM/CCL2; NRG1/FOS; ITGB2/CCL2

Source locator: Results: Intercellular crosstalk within the atherosclerotic core was remodeled by APVS; Fig. 7F-K and Fig. S15-S16

Permalink to KF7

Study Design & Model Development

Study profile

Study Design: Retrospective multicohort computational systems biology study
Evidence Type: Bulk transcriptomics, single-cell RNA sequencing, machine learning, and clinical association analyses
Population: Atherosclerotic and coronary artery disease samples spanning stable and unstable clinical and pathological states
Primary Endpoint: Discrimination of atherosclerotic and coronary pathological states using the APVS classifier
Secondary Endpoint: Clinical and biological quantification of plaque vulnerability using APVSLevel
External dataset evaluation: Yes

Data modalities

bulk transcriptomics
single-cell RNA sequencing
clinical outcomes
coronary stenosis phenotypes

Model development

Candidate Dcpgs: 96
Algorithms: 9
Learner Evaluation: 10-fold cross-validation with 100 repetitions
Optimal Learners: random forest, backpropagation neural network
Feature selection: Random-forest recursive feature elimination with Gini-based importance and 10-fold, 10-repeated cross-validation
Signature Size: 14
Classifier: Backpropagation neural network
Quantification System: APVSLevel
Quantification Basis: APVS expression profile combined with principal-coordinate information

What This Study Adds

A molecular plaque-vulnerability framework derived from transcriptomic disease states rather than relying only on conventional epidemiological risk factors.
Cross-condition validation across STEMI versus CCS, advanced versus early plaque, ruptured versus stable plaque, and STEMI versus healthy comparisons.
A transition from binary classification with APVS to continuous biological and clinical quantification with APVSLevel.
Bulk-to-single-cell interpretation linking the same vulnerability framework to plaque microenvironment and cellular-state remodeling.
Mechanistic hypothesis generation through pathway, immune-landscape, and cell-cell communication analyses.

Evidence Scope

Supports

APVS discriminates several studied coronary and plaque pathological states in retrospective transcriptomic datasets.
APVSLevel is associated with plaque vulnerability, coronary stenosis severity, and MACE risk in the studied cohorts.
APVSLevel-high states exhibit stronger inflammatory, immune, procoagulant, and plaque-destabilizing molecular programs.
Single-cell datasets support APVSLevel as a molecular descriptor of vulnerable plaque biology and cellular-state remodeling.
APVS/APVSLevel can serve as a research framework for molecular stratification and mechanistic exploration of atherosclerotic plaque vulnerability.

Does Not Establish

That APVS is a clinically approved diagnostic test for plaque vulnerability.
That the 14 APVS genes are causal drivers of plaque rupture.
That APVSLevel-guided treatment reduces myocardial infarction, mortality, or other clinical events.
That transcriptomic APVS/APVSLevel can replace coronary imaging, cardiac biomarkers, or standard clinical assessment.
That retrospective public-dataset validation is equivalent to prospective multicenter clinical validation.
That drug-response or signature-reversal analyses constitute therapeutic recommendations.

Limitations

The study was based predominantly on retrospective public high-throughput datasets.
Prospective multicenter studies are required to confirm the biological and clinical interpretability of APVSLevel.
The APVS classifier requires further direct comparison with established clinical biomarkers.
The functions of many APVS-related molecules remain incompletely established and require additional in vivo and in vitro validation.
Incomplete clinical and molecular traits in source datasets may obscure associations between APVS and some relevant factors.

When This Study Is Useful to Cite

CU1: What transcriptomic machine-learning signatures have been developed to characterize atherosclerotic plaque vulnerability?
APVS is a 14-gene signature derived from a 96-gene candidate set using a nine-learner machine-learning framework.
Evidence: KF1
CU2: Can APVS distinguish different clinical and pathological atherosclerotic states across independent datasets?
The classifier was evaluated for STEMI versus CCS, advanced versus early plaque, ruptured versus stable plaque, and STEMI versus healthy comparisons.
Evidence: KF2
CU3: Is APVSLevel associated with cardiovascular prognosis and coronary atherosclerotic severity?
Higher APVSLevel was associated with MACE risk, coronary stenosis and Duke CAD Index in the analyzed retrospective datasets.
Evidence: KF3
CU4: Which single-cell states are associated with higher molecular plaque vulnerability?
Single-cell analyses associated higher APVSLevel with vulnerable plaque states and inflammatory macrophage programs.
Evidence: KF4, KF5
CU5: How does plaque vulnerability relate to macrophage-myofibroblast communication?
APVSLevel-high and APVSLevel-low states showed differing inflammatory, profibrogenic and ligand-target communication patterns.
Evidence: KF7

What This Study Should Not Be Cited to Claim

An approved clinical diagnostic test for plaque vulnerability.
Causal roles for the 14 APVS genes.
Prospective multicenter clinical validation.
APVSLevel-guided treatment reduces MI, mortality or other events.
Transcriptomic APVS replaces plaque imaging, biomarkers or standard clinical assessment.
Signature-reversal or drug-response analyses constitute treatment recommendations.

Evidence Matrix

Row ID	Component	Context	Finding	Evidence Level	Scope	Source Locator	Evidence refs
evidence-apvs-development	APVS signature development	APVS generation and model selection across nine classical machine-learning learners	The integrative machine-learning program reduced 96 dysregulated co-expression pattern genes to a 14-gene APVS signature. candidate DCPGs: 96; machine-learning learners: 9; selected APVS genes: 14; optimal learners: random forest and backpropagation neural network; learner evaluation: 10-fold cross-validation with 100 repetitions; feature selection: RF-RFE with 10-fold, 10-repeated cross-validation.	Retrospective multicohort machine-learning development	Molecular signature derivation, not clinical diagnostic approval.	Results: Machine learning-based integrative program generates APVS; Fig. 2A-E; Methods: APVS generated from an integrative program	KF1
evidence-multicohort-discrimination	Multicohort discrimination	Independent external validation of STEMI/CCS, plaque-stage, plaque-rupture, and STEMI/healthy comparisons	The APVS classifier showed strong discrimination across independent datasets spanning clinical and pathological atherosclerotic states. GSE59867 STEMI vs CCS AUC: 0.985; GSE62646 STEMI vs CCS AUC: 0.997; GSE28829 advanced vs early plaque AUC: 0.952; GSE41571 ruptured vs stable plaque AUC: 0.972; GSE48060 STEMI vs healthy AUC: 0.871; GSE60993 STEMI vs healthy AUC: 0.916; GSE141512 STEMI vs healthy AUC: 1.000.	Independent retrospective dataset classification	Dataset discrimination, not prospective clinical validation.	Results: Robust performance of APVS-based classifier; Fig. S2B and Fig. 3A-B	KF2
evidence-clinical-association	Clinical association	Clinical interpretability analyses across MACE and coronary stenosis datasets	Higher APVSLevel was associated with worse cardiovascular prognosis and greater coronary atherosclerotic severity. MACE association: HR 3.819; p<0.01; high vs low APVSLevel MACE incidence: log-rank p<0.01; APVSLevel vs coronary stenosis degree: p<0.001; APVSLevel vs Duke CAD Index: p<0.01.	Retrospective clinical association	Association, not evidence that APVSLevel-guided treatment improves outcomes.	Results: The clinical interpretability underlying APVSLevel; Fig. 4A-D and Fig. 4J	KF3
evidence-single-cell-validation	Single-cell validation	Two single-cell RNA-sequencing cohorts comparing atherosclerotic core with adjacent normal tissue and vulnerable STEMI plaques with stable CCS plaques	APVSLevel reproduced plaque-vulnerability differences at single-cell resolution. GSE159677 cells: 43,964; atherosclerotic core vs adjacent normal APVSLevel: higher in atherosclerotic core; p<0.0001; GSE184073 cells: 2,237; vulnerable STEMI vs stable CCS plaque APVSLevel: higher in vulnerable STEMI plaques; p<0.0001.	Single-cell observational association	Single-cell plaque-state contrast, not causal validation.	Results: Single-cell resolution interpretation of the biological significance of APVSLevel; Fig. 7A-B and Fig. S13A	KF4
evidence-macrophage-state	Inflammatory macrophage state	Single-cell macrophage-state analysis in vulnerable STEMI versus stable CCS plaques	High plaque vulnerability was linked to inflammatory macrophage states and loss of plaque-stabilizing fibrotic macrophage phenotypes. CXCL3+/IL1B+ inflammatory macrophages: higher APVSLevel in STEMI plaques; C1Q+ fibrotic macrophages: lower APVSLevel in STEMI plaques; APVSLevel-high atherosclerotic core: more monocyte/macrophage and endothelial cells; fewer myofibroblast and SMC populations.	Single-cell cell-state association	Macrophage-state association, not a proven therapeutic mechanism.	Results: Single-cell resolution interpretation; Fig. 7C-E and Fig. S14	KF5
evidence-biological-state	Biological plaque state	Bulk and single-cell pathway-level biological interpretation	APVSLevel-high plaques showed a more inflammatory, procoagulant, and matrix-destabilizing molecular state, whereas APVSLevel-low plaques were relatively stromal-rich and fibrotic. APVSLevel-high pathways: coagulation cascade, collagen degradation, Notch signaling, TLR signaling; APVSLevel-high cell programs: inflammatory response, granulocyte activation, macrophage M1 polarization; APVSLevel-low phenotype: stromal-rich, fibroblast migration, fibrous content, relative immune suppression; single-cell high-vulnerability programs: coagulation, hypoxia, TNF signaling, heme metabolism, oxidative stress, senescence, DNA damage.	Bulk and single-cell pathway association	Biological interpretation, not treatment recommendation.	Results: The biological implications underlying APVSLevel; Fig. 5, Fig. S6-S8, Fig. 7E, and Fig. S14D-E	KF6
evidence-cell-communication	Macrophage-myofibroblast communication	CellChat and NicheNet analyses of APVSLevel-high versus APVSLevel-low atherosclerotic core states	Plaque vulnerability was accompanied by remodeling of macrophage-myofibroblast communication networks. pro-inflammatory signaling: MIF and VCAM strengthened in APVSLevel-high states; profibrogenic signaling: SPP1 and FN1 reduced in APVSLevel-high states; Mono/Mac-directed ligand-target axes: IL34/CTSK; IL6/SOCS3; AGT/SPP1; APOE/SPP1; myofibroblast-directed ligand-target axes: CXCL2/CDKN1A; ADM/CCL2; NRG1/FOS; ITGB2/CCL2.	Computational ligand-receptor inference	Inferred communication differences, not clinical intervention efficacy.	Results: Intercellular crosstalk within the atherosclerotic core was remodeled by APVS; Fig. 7F-K and Fig. S15-S16	KF7

Q&A

What is APVS?

APVS is a 14-gene atherosclerotic plaque vulnerability signature derived from 96 dysregulated co-expression pattern genes using an integrative nine-learner machine-learning framework.

Evidence: KF1

Can APVS distinguish STEMI from chronic coronary syndrome?

Yes. The APVS classifier achieved AUCs of 0.985 in GSE59867 and 0.997 in GSE62646 for STEMI versus chronic coronary syndrome classification.

Evidence: KF2

Can APVS distinguish ruptured from stable atherosclerotic plaques?

Yes. In GSE41571, the APVS classifier achieved an AUC of 0.972 for ruptured versus stable plaque classification.

Evidence: KF2

Can APVS distinguish advanced from early atherosclerotic plaques?

Yes. In GSE28829, the APVS classifier achieved an AUC of 0.952 for advanced versus early-stage plaque classification.

Evidence: KF2

What is APVSLevel?

APVSLevel is a continuous molecular quantification system derived from the APVS expression profile and principal-coordinate information to represent atherosclerosis severity and plaque vulnerability.

Evidence: KF3, KF4

Is APVSLevel associated with cardiovascular outcomes and disease severity?

Yes. Higher APVSLevel was associated with increased MACE risk (HR 3.819, p<0.01), greater coronary stenosis (p<0.001), and a higher Duke Coronary Artery Disease Index (p<0.01).

Evidence: KF3

What single-cell states are associated with high atherosclerotic plaque vulnerability?

Higher APVSLevel was associated with vulnerable plaque microenvironments enriched in inflammatory monocyte/macrophage programs, including CXCL3+/IL1B+ macrophage states, while C1Q+ fibrotic plaque-stabilizing macrophage phenotypes showed lower APVSLevel in STEMI plaques.

Evidence: KF4, KF5

Is APVS or APVSLevel ready to replace clinical plaque assessment?

No. The evidence is primarily retrospective and transcriptomics-based. Prospective multicenter validation, direct comparison with established clinical biomarkers, and additional experimental validation are required before clinical translation.

Concepts & Entities

Conditions

atherosclerosiscoronary artery diseaseacute myocardial infarctionST-elevation myocardial infarctionchronic coronary syndromeacute coronary syndrome

Plaque Phenotypes

atherosclerotic plaque vulnerabilitystable plaqueruptured plaqueearly-stage plaqueadvanced plaqueatherosclerotic corefibrous plaque

Outcomes

major adverse cardiovascular eventscoronary stenosisplaque rupturerestenosis after PCI

Methods

machine learningrandom forestbackpropagation neural networkrecursive feature eliminationweighted gene co-expression network analysisprincipal coordinates analysissingle-cell RNA sequencinggene set enrichment analysisgene set variation analysisDIRACVissExCellCellChatNicheNetSubclass Mapping

Models & Tools

APVSAPVS classifierAPVSLevel

Cell Types

monocyte/macrophagevascular smooth muscle cellmyofibroblastendothelial cellB cellplasma cellmast cellneutrophilfoam cell

Pathways Processes

inflammationcoagulationextracellular matrix degradationfibrosisoxidative stresscellular senescenceDNA damageNotch signalingTLR signalingTNF signalingheme metabolismcholesterol homeostasis

Mechanistic Entities

CXCL3IL1BC1QIL34CTSKIL6SOCS3AGTSPP1APOECXCL2CDKN1AADMCCL2NRG1FOSITGB2FN1MIFVCAM

Datasets & Cohorts

GSE59867GSE62646GSE28829GSE41571GSE48060GSE60993GSE141512GSE21545GSE159677GSE184073

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Publication & Provenance

DOI: https://doi.org/10.1016/j.isci.2023.107587
Publisher: https://www.cell.com/iscience/fulltext/S2589-0042(23)01664-4
PubMed: https://pubmed.ncbi.nlm.nih.gov/37664595/
PMCID: PMC10470306
Code: https://github.com/DrZoggg/APVS
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