Comprehensive analysis of atherosclerotic plaques reveals crucial genes and molecular mechanisms associated with plaque progression and rupture

Atherosclerosis (AS) and atherosclerotic cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. AS is a chronic inflammatory disease mainly characterized by the deposition of lipids and fibrous material in the subintima of arterial vessels. Continued local accumulation of lipids and lipid-engorged cells leads to the formation and progression of atherosclerotic plaques (APs), which are composed of a protective fibrous cap and nidus of a lipid-rich or necrotic core. Not all APs are the same, with some plaques remaining stable for years, whereas others progress and become unstable and vulnerable. Advanced APs can invade the arterial lumen, hinder blood flow, lead to tissue ischemia, and are more likely to rupture and cause thromboembolism, the most common cause of myocardial infarction. Intraplaque hemorrhage (IPH), caused by fibrous cap rupture or leakage due to neovascularization, is a typical feature of plaque instability and is common in high rupture risk coronary plaques. Given the slow progression of AS and often asymptomatic course, reliable tools to identify high-risk APs or predict rupture risk are crucial for preventing acute myocardial infarction (AMI). High-throughput gene profiling has enhanced understanding of plaque pathogenesis and progression, identifying genes and pathways involved. This study integrated GEO datasets on advanced, high-risk, and ruptured plaques, using Differential Expression Genes (DEGs) analysis and Weighted Gene Co-Expression Network Analysis (WGCNA) to define high rupture risk plaque-specific genes, and assessed their expression in blood of patients with plaque rupture to evaluate potential biomarkers and therapeutic targets.

The paper references prior histopathological and transcriptomic studies that identified genes and pathways implicated in AS and plaque progression, the clinical importance of IPH as a marker of plaque vulnerability, and roles of inflammation, neutrophils, and neutrophil extracellular traps (NETs) in atherogenesis and plaque destabilization. It also cites previous findings linking PLAU/PLAUR to advanced plaques and rupture-prone areas, and highlights gaps in understanding of cell origins and mechanisms affecting plaque stability in advanced lesions.

Study design and datasets: Gene expression profiles related to advanced plaque, high rupture risk plaque (with IPH), or ruptured plaque were downloaded from GEO. Discovery cohort: GSE28829 (13 early, 16 advanced human carotid plaque samples) and GSE163154 (16 low-risk [absence of IPH] and 27 high-risk [presence of IPH] carotid lesion segments). Validation cohort: GSE163154 (IPH vs non-IPH) and GSE41571 (5 ruptured vs 6 stable plaques). External validation for plasma/AMI: GSE66360 (49 AMI patients, 50 healthy controls). WGCNA: Performed using the R package WGCNA on the top 25% most variable genes. Outlier samples were excluded via hierarchical clustering (Hclust). Soft-threshold powers were selected using pickSoftThreshold (β=28 for GSE28829, β=12 for GSE163154) to construct weighted adjacency matrices and topological overlap matrices (TOM). Modules correlated with advanced plaques (GSE28829) or IPH (GSE163154) were identified; brown module (1,334 genes) in GSE28829 and turquoise module (887 genes) in GSE163154 were selected. Functional enrichment: Overlap between the brown (GSE28829) and turquoise (GSE163154) modules defined SET-1 (356 genes). Metascape was used for GO/Pathway enrichment of SET-1. DEGs analysis (validation cohort): Using limma with thresholds |log2FC|>1 and P<0.05, DEGs were identified for GSE163154 (IPH vs non-IPH) and GSE41571 (ruptured vs stable). UMAP was used to visualize separation between groups. Overlapping DEGs defined SET-2 (222 genes: 109 up, 113 down). Metascape was used for enrichment of SET-2. Hub gene identification: Intersection of SET-1 and SET-2 (67 genes) was analyzed in Metascape for PPI (STRING and BioGRID) and clustering via MCODE, yielding 16 hub genes in three densely connected clusters. Functional enrichment of hub genes was performed using ClueGO in Cytoscape. miRNA–mRNA network: miRTarBase and ENCORI were queried for experimentally supported or predicted miRNAs targeting hub genes; only overlapping miRNAs were retained. PLAU and SIRPA shared common miRNAs (e.g., hsa-miR-512-3p, hsa-miR-665). Networks were visualized in Cytoscape. Clinical cohort and OCT-based rupture identification: 29 patients undergoing coronary angiography (Mar–Oct 2021) at Shanghai Tongji Hospital were enrolled: 19 AMI patients with plaque rupture (identified by OCT) and 10 patients without AMI (controls). OCT criteria for rupture: fibrous cap discontinuity with cavity formation. Baseline clinical parameters were recorded. Sample collection and PBMC isolation: Peripheral blood obtained within 6 h of symptom onset before heparin/contrast. PBMCs isolated using Ficoll-Paque within 2 h. RNA-seq: Total RNA extracted (MagNA Pure Compact). Libraries prepared (VAHTS Total RNA-Seq H/M/R) and sequenced on Illumina HiSeq. DEGs identified using limma with |log2FC|≥0.5 and P<0.05. ROC analysis: Diagnostic performance of hub genes to distinguish plaque rupture vs controls assessed using pROC and ggplot2 (AUC). External ROC validation for AMI conducted on GSE66360. Foam cell validation: THP-1 monocytes differentiated to macrophages with PMA (100 ng/mL), then treated with ox-LDL (25 or 50 µg/mL) for 24 h to induce foam cells. Lipid accumulation assessed by Oil Red O staining and ImageJ quantification. Gene expression measured by RT-qPCR (2^-ΔΔCt) for PLAUR, FCER1G, PLAU, ITGB2, SLC2A5.

- WGCNA identified modules associated with advanced plaques and IPH: GSE28829 brown module (1,334 genes) correlated with advanced APs (r=0.775, P=8.1×10^-7); GSE163154 turquoise module (887 genes) correlated with IPH (r=0.799, P=1.4×10^-10). Additional positively correlated modules in GSE28829: pink (r=0.691, P=3.4×10^-5) and black (r=0.678, P=5.2×10^-5). - Overlap of key modules yielded SET-1 (356 genes). Metascape enrichment of SET-1 highlighted: Neutrophil degranulation (top), regulation of cell activation, inflammatory response, positive regulation of immune response, TYROBP causal network in microglia. - DEGs in validation datasets: GSE163154 (IPH vs non-IPH) had 499 DEGs (270 up, 229 down). GSE41571 (ruptured vs stable) had 1,795 DEGs (636 up, 1,159 down). Overlap defined SET-2 with 222 DEGs (109 up, 113 down). Enriched pathways for SET-2 included NABA Core Matrisome, actin cytoskeleton organization, regulation of cell adhesion, TYROBP causal network in microglia, and Neutrophil degranulation. - Intersection of SET-1 and SET-2 (67 genes) and PPI/MCODE analysis identified 16 hub genes grouped into three clusters. Neutrophil degranulation was the most significant term in the first cluster; hub genes overall enriched in Neutrophil degranulation, Complement and coagulation cascades, and Exocytosis of tertiary granule membrane proteins. - miRNA–mRNA network: hsa-miR-665 and hsa-miR-512-3p targeted both PLAU and SIRPA, suggesting regulation of the neutrophil degranulation pathway via these genes. - Clinical PBMC RNA-seq (plaque rupture vs control) identified 1,215 DEGs (399 up, 816 down); enrichment again emphasized Neutrophil degranulation. Five hub genes were significantly upregulated: PLAU (log2FC=1.833836), PLAUR (0.703704), ITGB2 (1.070705), FCER1G (0.570931), SLC2A5 (0.710641). - Foam cell assays showed increased expression of PLAUR, FCER1G, PLAU, ITGB2, and SLC2A5 in ox-LDL–induced macrophage foam cells, with dose-dependent increases paralleling lipid accumulation. - ROC analyses for distinguishing plaque rupture from controls in the clinical cohort: PLAUR AUC=0.868; FCER1G AUC=0.879; PLAU AUC=0.963; ITGB2 AUC=0.858; SLC2A5 AUC=0.637. External AMI validation (GSE66360): PLAUR AUC=0.868; FCER1G AUC=0.885; PLAU AUC=0.827; ITGB2 AUC=0.509; SLC2A5 AUC=0.578. - Final diagnostic hub genes prioritized: PLAUR, FCER1G, and PLAU, with consistent upregulation and diagnostic performance across datasets.

The study addresses the need to identify molecular mechanisms and biomarkers underlying plaque progression to instability and rupture. Integrative analyses across advanced plaques, IPH-associated high-risk plaques, and ruptured plaques converge on neutrophil degranulation as a central pathway driving progression and instability. Neutrophils, through degranulation and NET formation, can amplify inflammation, degrade extracellular matrix, and promote thrombogenicity, thereby contributing to plaque vulnerability and rupture. The identification of 16 hub genes within interconnected PPI clusters further supports roles of innate immune activation, complement/coagulation, and exocytosis processes. miRNA targeting analyses implicate hsa-miR-665 and hsa-miR-512-3p in regulation of PLAU and SIRPA, potentially modulating neutrophil degranulation and cell activation. Experimental validation demonstrated that key hub genes (PLAU, PLAUR, FCER1G, ITGB2, SLC2A5) are upregulated in PBMCs from patients with OCT-confirmed plaque rupture and in ox-LDL–induced foam cells, linking plaque-level signatures to peripheral blood signals and cellular phenotypes relevant to atherogenesis. Diagnostic testing shows that PLAUR, FCER1G, and PLAU can discriminate plaque rupture and AMI cases from controls with acceptable-to-excellent AUCs, indicating translational promise as biomarkers. These findings refine understanding of the transition from stable to rupture-prone plaques, highlighting neutrophil-driven inflammation and the PLAU–PLAUR system as potential therapeutic targets.

Neutrophil degranulation is closely associated with atherosclerotic plaque progression, instability, and rupture. Integrative transcriptomic analyses identified 16 hub genes, among which PLAUR, FCER1G, and PLAU emerged as robust candidates for early diagnosis of plaque rupture and as potential therapeutic targets. Their upregulation in patient PBMCs and diagnostic performance in both internal and external datasets underscore their clinical relevance.

The study is limited by a modest clinical sample size for the plaque rupture cohort, necessitating validation in larger, prospective populations. While external validation for AMI was performed, plasma samples from patients with high-risk rupture-prone (but unruptured) plaques were not analyzed; comparative studies among high-risk, ruptured, and healthy cohorts are needed to assess predictive value. Mechanistic roles of identified genes, including FCER1G, require further experimental elucidation. Generalizability may be affected by cohort heterogeneity and dataset integration constraints.