1. Introduction
Coronary atherosclerosis (CAD) is the primary pathological basis for coronary heart disease (CHD) and acute coronary syndrome (ACS), and it is one of the leading global causes of death and disease burden. Statistics show that CAD causes nearly 10 million deaths worldwide annually, accounting for the largest proportion of cardiovascular-related mortality. This chronic arterial disease is characterized by lipid deposition, chronic inflammation, and fibrous tissue proliferation, which progressively lead to arterial sclerosis, lumen narrowing, and even complete occlusion [1]. Although traditional risk assessment models and diagnostic tools (such as the Framingham Risk Score, electrocardiography, and coronary angiography) play critical roles in the diagnosis and treatment of mid-to-late-stage CAD, they are less effective in identifying the early, asymptomatic stages of the disease. With the advent of precision medicine, individualized molecular-level diagnostics have become a research focus, offering new hope for the early detection and risk stratification of CAD [2,3].
In recent years, the rapid development of genomics has provided novel perspectives and tools for CAD research. By studying the structure, function, and disease associations of genetic material, genomics has offered important insights into the pathogenesis and potential molecular pathways of CAD [4]. For instance, genome-wide association studies (GWASs) have become a core technology in CAD genetics research. By scanning genetic variations across large populations, GWASs have identified several key genetic loci related to CAD, such as JCAD/KIAA1462, GUCY1A3, and PCSK9 [5,6]. These genes are closely associated with vascular endothelial function regulation, lipid metabolism, and inflammatory responses, laying the foundation for developing diagnostic and therapeutic targets [7,8]. Furthermore, gene sequencing technologies (including second- and third-generation sequencing) have revealed critical mutations and genetic variations associated with CAD [9]. Notably, these discoveries have already yielded clinical applications, with PCSK9 inhibitors now serving as effective therapeutic agents for lowering cholesterol in high-risk CAD patients [10].
Transcriptomics and epigenomics, as extended fields of genomics, have deepened our understanding of the molecular mechanisms underlying CAD. Transcriptomic studies have discovered significantly abnormal mRNA expression levels of certain key genes in CAD-affected tissues, such as those involved in inflammation and oxidative stress. Additionally, epigenomics, through the study of DNA methylation, histone modification, and non-coding RNAs (e.g., miRNAs and lncRNAs), has highlighted the critical role of epigenetic changes in CAD pathogenesis [11]. For example, research shows that the DNA methylation status of specific gene regions is significantly altered in CAD patients, and the expression patterns of certain miRNAs are strongly correlated with disease severity, providing a basis for developing potential biomarkers [12].
Despite these advances in genomic research, several challenges impede clinical implementation. For example, CAD is a dynamically evolving process involving different genes and molecular mechanisms at various stages of the disease [13]. The expression levels and functions of genetic markers may change throughout disease progression, limiting the universal applicability of single markers in clinical diagnostics. Furthermore, the lack of standardized diagnostic criteria and guidelines poses a significant barrier, as different studies and medical institutions employ inconsistent methods and thresholds for genetic testing, hindering the broad application of genomics findings [14]. Additionally, integrating genomics with traditional diagnostic methods, such as imaging and biochemical markers, to develop multidimensional diagnostic strategies requires further exploration [15].
Against this backdrop, this review aims to systematically summarize the research progress of CAD-related genetic markers, exploring their applications in early diagnosis, risk stratification, and personalized treatment from multiple perspectives, including GWASs, gene sequencing, transcriptomics, and epigenomics. It also analyzes the challenges related to dynamic marker changes, standardized diagnostic methods, and technical integration, proposing future directions and strategies to promote the clinical translation of genomic diagnostics in CAD. Ultimately, these efforts aim to achieve precision medical management of the disease and improve patient outcomes [16].
2. Research Methodology
2.1. Literature Search Strategy
Authoritative databases such as PubMed, Web of Science, Wiley Online Library, and ScienceDirect were used to conduct the search until June 2024 to obtain the research progress and cutting-edge results in the field of coronary atherosclerosis genomics. Disease terms such as “coronary atherosclerosis”, “coronary heart disease”, “atherosclerosis”, “genomics”, “genetic markers”, and “genomics” were used. Using “gene marker” and other genomics terms and “JCAD/KIAA1462” and other key gene names, we constructed a multi-group search formula through logical operators, such as “(coronary atherosclerosis OR coronary heart disease OR atherosclerosis) AND (genomics OR gene markers OR genome-wide association study OR gene sequencing OR transcriptomics OR epigenomics) AND (JCAD/KIAA1462 OR GUCY1A3 OR PCSK9 OR SORT1)”, to conduct a comprehensive search.
2.2. Literature Screening Criteria
Studies were included if they directly investigated the genomic aspects of coronary atherosclerosis, including genetic markers for diagnosis and risk stratification, molecular pathogenesis, and applications of genomic technologies. Each study was required to provide a clear methodological description, well-documented results, and valid conclusions supported by data.
Studies were excluded if they examined cardiovascular diseases without a genomic analysis of coronary atherosclerosis or primarily focused on non-genomic factors (environmental, lifestyle) without a genetic mechanism investigation. Studies with an inadequate sample size, lack of appropriate controls, or unsound methodology were also excluded. For multiple publications using the same dataset, only the most recent or comprehensive study was included.
3. Pathogenesis of Coronary Atherosclerosis
The pathogenesis of coronary atherosclerosis, a complex and serious threat to human cardiovascular health, involves the interaction and dynamic evolution of multiple factors (Figure 1).
3.1. Early Vascular Functional Alterations and Inflammation Initiation
Risk factors including genetic predisposition (JCAD, NOS3), oxidative stress, and familial hypercholesterolemia (PCSK9, LDLR, APOB) initiate vascular endothelial cell (VEC) dysfunction. Additional contributors include age, lifestyle factors, chronic diseases, infections, and autoimmune conditions [17].
The inflammatory cascade begins when compromised VEC barrier function allows LDL penetration into the arterial intima. Oxidized LDL triggers the endothelial expression of adhesion molecules (ICAM-1, VCAM-1), promoting monocyte adhesion and migration. Released inflammatory mediators (MCP-1, IL-6, TNF-α) drive monocyte-to-macrophage differentiation and smooth muscle cell (SMC) phenotype transformation [17,18,19].
3.2. Vascular Remodeling and Lesion Progression
Upon activation of the inflammatory response, SMCs migrate and proliferate from the intima to the endothelium as an adaptive response to injury aimed at repairing the damaged vessel wall. SMCs migrating to the intima synthesize and secrete collagen, elastic fibers, and other extracellular matrix (ECM) components, which cause the vessel wall to thicken and the lumen to narrow. At the same time, ECM remodeling occurs, and the balance of matrix metalloproteinases (MMPs) and their inhibitors (TIMPs) is disrupted, with a relative increase in the activity of MMPs, which degrade ECM components and change the structure and composition of the vessel wall, an important feature of vascular remodeling [20].
Macrophages phagocytose ox-LDL to form foam cells, which accumulate in large numbers under the intima and release inflammatory mediators, forming an inflammatory microenvironment. This exacerbated the inflammatory response, further promoted the proliferation and migration of SMCs, and accelerated vessel wall thickening and lumen narrowing. Foam cell apoptosis releases lipids and cellular debris to form lipid cores, which increase plaque size and worsen the structural and functional damage of the vessel wall [21].
3.3. Persistent Effects of Plaque Formation and Vascular Remodeling
With lesion progression, the ECM secreted by SMCs encapsulates lipid cores to form an atheromatous plaque fibrous cap. Fibrous cap thickness and stability are critical for plaque development. In this process, vascular remodeling continues. On the one hand, SMCs continue to proliferate and synthesize the ECM to thicken the fibrous cap in order to stabilize the plaque; on the other hand, the continuous action of MMPs may degrade the fibrous cap to make it thinner, and the stability of the plaque decreases.
The structure and function of the vessel wall are constantly changing in vascular remodeling. Intimal thickening, luminal narrowing, and plaque formation affect local hemodynamics, resulting in changes in blood flow velocity and shear stress. Slowed blood flow and low-shear-stress regions promote lipid deposition and inflammatory cell aggregation, exacerbating vessel wall injury; high-shear-stress regions promote the proliferation of SMCs and thickening of the fibrous cap, which maintains the integrity of the vessel wall, creating a vicious cycle [22].
3.4. Plaque Progression and Clinical Consequences
Over time, unstable plaques form with thin fibrous caps, lipid-rich cores, and significant inflammatory cell infiltration. The unstable plaque is prone to rupture in response to triggers such as hemodynamic fluctuations and increased inflammatory response. After rupture, the lipid core and tissue factor activate platelet aggregation and the coagulation system to form thrombus, leading to acute lumen occlusion and triggering acute coronary syndromes (ACSs), such as myocardial infarction and other serious cardiovascular events [23].
Although stable plaques are relatively stable, the increase in volume will also aggravate coronary artery stenosis, leading to insufficient blood supply to the myocardium, causing chronic myocardial ischemia symptoms, such as stable angina pectoris, affecting the quality of life and prognosis of patients.
In summary, vascular remodeling occurs early in the pathogenesis of coronary atherosclerosis, and it is intertwined and synergistic with lipid deposition and inflammatory responses to drive disease progression and ultimately lead to cardiovascular complications. An in-depth understanding of this process is important for the early identification and intervention of atherosclerosis to reduce the risk of cardiovascular disease.
4. Role of Genetic Factors in Coronary Atherosclerosis
4.1. Core Role of Genetic Factors
Genetic factors play a crucial role in the development and progression of coronary atherosclerosis. Studies of twins and familial cases have shown that genetic factors account for approximately 40–60% of the risk of developing CAD [24]. GWASs have further identified hundreds of genetic variants associated with CAD, highlighting the significant role of genetic susceptibility in individual differences in disease risk [25] (Figure 2).
4.2. Key Genes and Their Functions
Several key genes play important roles in the pathogenesis of coronary atherosclerosis. Among them, the JCAD/KIAA1462 gene encodes a protein closely related to endothelial cell adhesion [26]. Mechanistic studies have elucidated JCAD’s molecular pathway in endothelial cells. Research has revealed that JCAD forms a crucial interaction with LATS2 (large tumor suppressor kinase 2), which serves as a key regulatory component of the Hippo signaling pathway. Through this interaction, JCAD functions as a negative regulator of Hippo signaling [27]. This negative regulation results in enhanced activity of YAP (yes-associated protein), which acts as the primary transcriptional effector of the pathway [28].
GUCY1A3 encodes the α1-subunit of soluble guanylyl cyclase (sGC), which produces cyclic guanosine monophosphate (cGMP), triggering multiple cellular responses including platelet aggregation inhibition and smooth muscle cell relaxation, serving as a central enzyme in cardiovascular regulation [29]. A genome-wide association study meta-analysis identified NOS3, which encodes endothelial NO synthase as a CAD risk gene [30]. Furthermore, genetic studies have demonstrated the NO-sGC-cGMP pathway’s role in mediating both CAD and myocardial infarction risk. Notably, genetic variants at the GUCY1A3 locus have been shown to influence NOS3 expression [29].
PCSK9’s influence on CAD operates through multiple mechanisms, including binding to epidermal growth factor (EGF) domains on receptors, interacting with receptors in lipid rafts and cell membrane micro-domains, and modulating gene expression and protein responses involved in cardiac complications [31]. Most importantly, the protein’s interaction with LDLR follows two distinct pathways, namely extracellularly, where circulating PCSK9 binds to LDLR’s EGF-A domain, forming a complex that undergoes endocytosis and lysosomal degradation, preventing LDLR recycling and increasing plasma LDL-c levels; and intracellularly, where PCSK9 binds LDLR in the trans-Golgi network, directing it to lysosomes before reaching the cell surface—a process requiring PCSK9’s catalytic activity. Mutations in this gene have a significant effect on LDL-c metabolism [32]. Except for the role in lipid metabolism, PCSK9, also associated with expression of apoptosis-related proteins such as Bax and caspase-3, possibly induces the apoptosis of endothelial cells and stimulates the differentiation of vascular smooth muscle cells into a synthetic phenotype [33].
4.3. Genetic Association with Familial Hypercholesterolemia
Familial hypercholesterolemia (FH) is a monogenic hereditary disorder typically caused by loss- or gain-of-function mutations in the LDLR, APOB, or PCSK9 genes. FH patients exhibit significantly elevated plasma LDL levels, making them a high-risk group for early-onset coronary atherosclerosis. These findings reveal the direct contribution of monogenic mutations to CAD risk [34].
4.4. Polygenic Cumulative Effects
CAD is not determined by a single gene but driven by the cumulative effects of multiple genes and environmental factors. Many risk loci identified through GWASs contribute modestly individually, but their combined effects can significantly increase disease risk [35]. Studies using polygenic risk scores (PRSs) have shown that individuals with high genetic risk have a significantly higher incidence of CAD, even under similar environmental conditions [36].
4.5. Epigenetic Regulation
Genetic susceptibility is not only reflected in gene mutations and variations but also involves epigenetic regulatory mechanisms. For example, abnormal changes in DNA methylation levels have been identified as an important regulatory mechanism in CAD [37]. Additionally, the dysregulated expression of certain miRNAs (e.g., miR-126 and miR-33) may contribute to CAD pathogenesis by modulating lipid metabolism and inflammatory responses [38].
5. Applications of Genomic Research Methods in the Diagnosis of Coronary Atherosclerosis
5.1. Genome-Wide Association Studies (GWASs)
5.1.1. Evolution and Rationale of GWASs in CAD Research
Early GWASs of coronary atherosclerosis had significant limitations. Small, geographically restricted sample sizes compromised genetic diversity and generalizability, while first-generation genotyping platforms had poor SNP detection capabilities. Despite strict statistical thresholds (p < 5 × 10−8), results were often unreliable and difficult to validate independently [39].
Recent GWASs in CAD have made significant progress, largely driven by the analysis of expanded cohorts [40]. A comprehensive genome-wide association analysis of 3221 cardiovascular patients led to the identification of novel candidate SNPs in BAAT, BCL3, and CMTM6 genes. These genetic variants were found to have direct functional impacts on cholesterol levels and LDL biosynthesis, particularly in relation to patients’ responses to statin therapy in CAD treatment, highlighting the clinical relevance of these genetic discoveries [41].
New challenges have emerged regarding genetic structure differences because of population stratification. These differences can generate false positive or false negative results that affect the accuracy of research conclusions [39]. Several methods that correct for stratification have been studied, including detecting stratification, inferring genetic ancestry, family-based association tests, mixed models, and low-frequency and rare variants. Current studies must develop robust methodologies to properly address this statistical challenge and ensure reliable findings [42].
5.1.2. Key CAD Genes Identified by GWASs and the Value of Their Detection
Genome-wide association studies (GWASs) have identified several genes strongly associated with coronary artery disease (CAD). Four genes in particular—JCAD/KIAA1462, GUCY1A3, PCSK9, and SORT1—have emerged as especially significant. Based on a comprehensive literature review, we analyzed these genes’ characteristics and evaluated their potential clinical applications.
JCAD/KIAA1462: JCAD, previously known as KIAA1462, represents a promising therapeutic target for cardiovascular disease intervention [43]. Multiple genome-wide association studies have consistently identified JCAD as a significant risk locus for coronary artery disease [26,44]. The STARNET study (Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task) revealed a key CAD-associated single nucleotide polymorphism, rs2487928, which functions as a highly significant expression quantitative trait locus (eQTL) for JCAD in the arterial wall and atherosclerotic aortic root [45]. Research indicates this variant may interfere with normal endothelial cell function and disrupt physiological processes, thereby elevating coronary heart disease risk [28,44,46].
Research by Hara and colleagues has demonstrated that JCAD plays a crucial role in vascular function. Their studies showed that JCAD knockdown reduces angiogenic processes and decreases the vascular endothelial growth factor-induced phosphorylation of both extracellular signal-regulated kinase and VEGF receptor 2 (VEGFR2). These findings suggest that JCAD’s influence extends beyond coronary artery disease, potentially affecting tumor growth through its impact on vascular development [47].
GUCY1A3: As previously described, GUCY1A3 plays a crucial role in CAD vasoregulatory mechanisms and can be analyzed through PCR and the sequencing of specific gene fragments in peripheral blood. Research by Kessler et al. revealed that the intronic variant rs7692387 contains a DNase I site, where ZEB1 transcription factor preferentially binds to the non-risk allele, leading to increased GUCY1A3 expression and sGC activity, with higher sGC expression correlating to reduced atherosclerosis risk in both humans and mice. Their study also found that carriers of the homozygous GUCY1A3 risk allele have an elevated risk of cardiovascular death or stent thrombosis within 30 days after coronary stenting, possibly due to increase on-aspirin platelet reactivity [29]. Kessler et al. also demonstrated that carriers of homozygous GUCY1A3 risk allele face higher risks of cardiovascular death or stent thrombosis within 30 days after coronary stenting, potentially due to an increase in on-aspirin platelet reactivity [48]. Further investigation by Hall et al., through two randomized placebo-controlled trials in primary prevention, demonstrated that aspirin reduced CVD events in individuals homozygous for the GUCY1A3 risk allele, while heterozygote individuals experienced more events when taking aspirin [49]. These findings collectively underscore GUCY1A3’s significance in pathological CAD progression.
PCSK9: Lipid metabolism dysfunction plays a central role in CAD progression, with PCSK9 emerging as a key clinical target for treating hypercholesterolemia due to its deep involvement in lipid metabolism.
Several studies were conducted to verify the efficacy and safety of the PCSK9 inhibitors in treating dyslipidemia and cardiovascular disease. The development of PCSK9 inhibitors has progressed through three generations, namely first-generation monoclonal antibodies (evolocumab and alirocumab) with proven efficacy and safety in follow-up studies [50,51], second-generation small-interfering RNA (inclisiran) validated through phase III trials and ongoing cardiovascular outcome studies [52], and third-generation approaches including oral drugs (MK-0616), fusion protein (lerodalcibep), vaccination, and gene-editing strategies [53].
Beyond lipid regulation, PCSK9 influences inflammatory pathways crucial to atherosclerosis development. It regulates TLR4 activation through MyD88 and TRIF pathways (predominantly MyD88), inducing PCSK9 transcription in the aorta and activating NF-kB, which affects apoptosis and autophagy [54]. Additionally, research by Ding et al. revealed that recombinant PCSK9 enhances the expression of multiple scavenger receptors (SRs), particularly LOX-1, SRA, and CD36, with LOX-1 showing the highest upregulation [55]. These SRs facilitate ox-LDL uptake by macrophages during early atherosclerosis, demonstrating PCSK9’s intertwined role in inflammation and its significance in atherosclerosis progression and subsequent myocardial ischemia.
SORT1: SORT1 plays a crucial role in lipid metabolism through its regulation of hepatic VLDL secretion and plasma LDL-C levels. Research by Strong et al. demonstrated that hepatic sortilin overexpression reduces plasma cholesterol by decreasing VLDL secretion, while knockout models show increased VLDL secretion [56]. The gene’s importance is highlighted by genome-wide association studies identifying the 1p13 locus containing SORT1, as strongly associated with both LDL-C levels and myocardial infarction risk [57]. SNPs in this region, particularly rs12740374, create a C/EBP transcription factor binding site that alters SORT1 expression. Additionally, SORT1 functions in post-translational PCSK9 regulation by facilitating its secretion, forming an important regulatory network in lipid metabolism. Recent therapeutic developments include antisense oligonucleotides targeting SORT1 for potential cardiovascular disease treatment, though these approaches are still in experimental stages [58]. These findings collectively emphasize SORT1’s significance in both lipid regulation and cardiovascular disease pathogenesis.
5.1.3. Progress and Challenges of GWASs in Advancing the Clinical Use of CAD
GWASs have fundamentally transformed our understanding of CAD genetics, revealing its complex polygenic nature. While individual genetic variants show modest effects, their cumulative impact significantly influences disease susceptibility and progression [4].
Recent advances in PRSs have demonstrated promising clinical applications. A landmark 10-year longitudinal study of 288 patients revealed that a high PRS correlated with increased baseline plaque volume and accelerated progression. Each standard deviation increase in the PRS led to a 0.69% greater plaque volume progression per decade after multivariate adjustment. The integration of PRSs with traditional risk factors improved predictive accuracy for non-calcified plaque progression (AUC: 0.73 vs. 0.69, p = 0.039) [59]. Meta-analyses across multiple cohorts have consistently shown that individuals with a high PRS have a higher risk of CAD compared to those with low scores [60].
Machine learning approaches have further enhanced PRS accuracy. Advanced algorithms incorporating both genetic and clinical variables have achieved improved risk stratification, particularly in identifying high-risk individuals who might benefit from early intervention. These computational advances have enabled a more sophisticated modeling of gene–environment interactions and better account for population-specific genetic architectures.
However, significant challenges persist in translating GWAS findings to clinical practice. Technical barriers include platform heterogeneity in genotyping arrays, which creates systematic differences that imputation methods only partially address [39]. Statistical challenges remain in controlling false positive results and managing multiple testing burden, despite advances in PRS construction methodology. Clinical implementation faces additional hurdles, including the need for standardized testing protocols, provider education, and clear guidelines for risk communication [61].
Recent research has focused on developing more robust statistical frameworks and validation strategies. The integration of PRSs with established risk assessment tools has shown promise in improving risk stratification accuracy [62]. A population-specific calibration of PRSs and the consideration of ancestry-specific genetic architectures have emerged as crucial factors in improving predictive accuracy across diverse populations.
Looking forward, the field is moving toward more comprehensive risk assessment models that integrate multiple data types, including genetic, clinical, and environmental factors. Forrest et al. demonstrated a machine learning-based ISCAD model’s effectiveness in large biobanks (n = 95,935), achieving AUCs of 0.91–0.95. The model showed strong clinical correlation, with coronary stenosis increasing 12% per ISCAD quartile and mortality risk escalating across deciles (HR 1.0–56). Notably, 46% of undiagnosed high-ISCAD individuals showed clinical CAD evidence per ACC/AHA guidelines, highlighting its utility in subclinical detection [63]. Success in addressing these challenges could revolutionize CAD prevention and management, enabling truly personalized cardiovascular medicine.
5.2. Genetic Sequencing Analysis
5.2.1. Overview of Genetic Sequencing Technologies and Their Characteristics
Common genetic sequencing technologies include first-generation sequencing (Sanger sequencing), second-generation sequencing (e.g., Illumina sequencing), and third-generation sequencing (e.g., PacBio sequencing). Each has unique features in sequencing throughput, read length, accuracy, and cost, suitable for different research scenarios [64]. (1) First-generation sequencing (Sanger): high accuracy but low throughput and relatively high cost. (2) Second-generation sequencing (Illumina): high throughput and low cost, ideal for large-scale studies. (3) Third-generation sequencing (PacBio): long read lengths and high resolution, particularly useful for structural variant (SV) detection. As sequencing technologies advance, second- and third-generation sequencing are increasingly used complementarily. The high throughput and low cost of second-generation sequencing combined with the long-read and high-resolution capabilities of third-generation sequencing enable a comprehensive analysis of CAD’s genetic basis.
5.2.2. Applications of Genetic Sequencing in CAD Diagnosis
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(1). Whole-Genome Sequencing (WGS) for Identifying Key Mutations
Large-scale WGS studies in CAD patients have identified non-coding region variants significantly associated with CAD. For example, SNPs in the enhancer region of the SORT1 gene have been shown to alter lipid metabolism-related gene expression, affecting CAD risk [65].
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(2). Whole-Exome Sequencing (WES) for Identifying Pathogenic Mutations
WES has been widely used to analyze rare variants associated with familial CAD. For example, studies on familial hypercholesterolemia patients identified a loss-of-function mutation in the LDLR gene, closely related to elevated LDL-C levels, providing a precise therapeutic target.
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(3). Long-Read Sequencing for Structural Variant Detection
Third-generation sequencing excels in detecting structural variants (SVs) and copy number variations (CNVs). For example, PacBio sequencing identified a large deletion in the GUCY1A3 gene region, closely linked to smooth muscle dysfunction and increased CAD risk.
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(4). Liquid Biopsy for Precision Diagnosis
Second-generation sequencing has also been applied in liquid biopsies, detecting circulating cell-free DNA (cfDNA) mutations in blood for early CAD diagnosis. For instance, studies show that CAD patients exhibit elevated frequencies of PCSK9 mutations in plasma cfDNA, which could serve as a potential biomarker for disease progression [66].
Genetic sequencing has become a core tool for CAD research and diagnosis. The integrated use of different sequencing technologies allows researchers to comprehensively analyze CAD’s genetic basis at genomic, transcriptomic, and structural levels, supporting early diagnosis, risk stratification, and individualized treatment. However, challenges such as sequencing costs, data complexity, and clinical translation need further exploration and optimization.
Transcriptomic techniques have also been extended to analyze plasma and exosomal RNA, providing non-invasive diagnostic tools for CAD. For example, one study found that inflammatory gene mRNA (e.g., CCL2) expression was significantly upregulated in plasma exosomes of CAD patients, suggesting its potential as a marker for early detection [66].
5.3. Applications and Discoveries of Transcriptomics in CAD Diagnosis
5.3.1. Key Genes and Pathways Identified by Transcriptomics
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(1). Inflammation-Related Genes
RNA-seq studies have shown a significant activation of inflammatory factors (e.g., IL6, TNF-α) and their related signaling pathways in the arterial wall tissues of CAD patients. For instance, elevated IL6R expression is closely associated with local inflammatory responses, serving as a key driver of CAD plaque formation and rupture [67].
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(2). Lipid Metabolism Genes
Transcriptomics has revealed the abnormal expression of several lipid metabolism-related genes in CAD [68]. For example, the upregulation of APOB and SREBF1 may exacerbate cholesterol accumulation and promote the progression of atherosclerotic lesions.
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(3). Vascular Function Genes
RNA-seq analysis also identified significant abnormalities in genes related to vascular smooth muscle and endothelial function. The reduced expression of NOS3, for instance, may lead to decreased nitric oxide synthesis, further aggravating vascular constriction and dysfunction [69].
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(4). Identification of Potential Biomarkers for Early Diagnosis
Transcriptomics data assist in identifying potential molecular markers for the early diagnosis of CAD, such as the following:
CDKN2B-AS1 (ANRIL): A long non-coding RNA (lncRNA) associated with CAD. Its expression is significantly upregulated and may influence smooth muscle cell proliferation by regulating cyclin-dependent kinase inhibitors [70].
MIR-33: This miRNA, linked to lipid metabolism and inflammation, shows abnormal expression in CAD patients and may serve as a key molecular marker for disease diagnosis and therapeutic response evaluation [71].
5.3.2. Transcriptomic Features of Disease Severity
RNA-seq studies have revealed significant transcriptional differences across different stages of CAD (e.g., early lipid plaques, stable plaques, and unstable plaques). For instance, the following have been observed:
(1) Unstable plaques: an elevated expression of matrix metalloproteinases (e.g., MMP9) suggests an increased risk of plaque rupture.
(2) Stable plaques: an increased expression of collagen-related genes (e.g., COL1A1 and COL3A1) indicates enhanced mechanical strength of the plaques.
Transcriptomics provides a wealth of resources for understanding the molecular mechanisms of CAD and developing diagnostic biomarkers [72]. Using techniques like RNA-seq, researchers have not only uncovered gene expression changes associated with CAD progression but also identified several molecular markers with diagnostic and therapeutic potential. However, translating these findings into clinically applicable diagnostic tools and further exploring the specific transcriptional characteristics of different disease stages require deeper research and technological innovation.
5.4. Epigenomics
5.4.1. Core Research Areas of Epigenomics
Epigenomics primarily studies mechanisms such as DNA methylation, histone modifications (e.g., methylation, acetylation), and non-coding RNAs (e.g., miRNAs, lncRNAs) that regulate gene expression [73]. These epigenetic modifications affect cellular physiological functions and the development of diseases by altering gene transcription activity and chromatin structure without changing the DNA sequence.
5.4.2. Applications and Discoveries of Epigenomics in CAD Diagnosis
Epigenomics provides new insights into the early diagnosis and risk assessment of coronary atherosclerosis (CAD). Large-scale epigenomic studies have identified multiple epigenetic markers closely associated with CAD progression.
5.4.3. Diagnostic Applications of DNA Methylation
(1) Specific gene methylation patterns: studies have shown that the DNA methylation levels of specific genes (e.g., APOB, NOS3) in the plasma or lesions of CAD patients are significantly upregulated or downregulated, making them potential biomarkers for disease diagnosis.
(2) Predicting disease progression: for instance, a study on CAD patients revealed that the methylation status of the DNMT3B gene is significantly associated with arterial plaque instability, providing molecular evidence for risk prediction [74].
5.4.4. Diagnostic Potential of miRNAs
(1) Plasma miRNA detection: numerous studies have reported significantly elevated levels of inflammation-related miRNAs (e.g., miR-146a, miR-21) in the plasma of CAD patients, indicating their potential as non-invasive biomarkers for early disease detection [75].
(2) Therapeutic response evaluation: changes in the expression levels of certain miRNAs are strongly associated with reduced inflammatory responses following drug interventions (e.g., statins), suggesting their application in monitoring treatment efficacy.
(3) Role of lncRNAs in risk stratification: the expression levels of long non-coding RNAs (lncRNAs), such as ANRIL, are significantly associated with plaque stability and cardiovascular event risk in CAD patients [76]. For example, ANRIL regulates the CDKN2B gene, influencing smooth muscle cell proliferation and inflammatory responses, making it a critical candidate marker for CAD risk stratification.
5.4.5. Histone Modification Mechanisms and Biomarker Development
Studies have found that the reduced demethylation of histone H3K27 in the endothelial cells of CAD patients may enhance the expression of adhesion molecules, promoting inflammatory cell infiltration and serving as a potential epigenetic marker for CAD progression.
5.4.6. Multi-Omics Integrated Diagnostic Tools
Integrating multi-dimensional epigenomic data, such as DNA methylation, miRNA expression, and histone modifications, can significantly improve the accuracy of CAD diagnosis. For example, a recently developed multi-omics scoring model successfully distinguished early CAD patients from healthy individuals, providing an efficient molecular diagnostic tool for clinical application [77].
Epigenomics demonstrates significant potential in CAD research. By comprehensively analyzing DNA methylation, histone modifications, and non-coding RNAs, researchers have uncovered molecular mechanisms underlying CAD and identified numerous epigenetic markers with diagnostic and prognostic value [78]. However, challenges such as sample size, data standardization, and detection technologies remain obstacles to clinical translation. In the future, with the integration of multi-omics technologies and the advancement of large-scale studies, epigenomics will play an increasingly vital role in the precise diagnosis and personalized treatment of CAD.
We compared the genomic technologies involved and their contribution to CAD research, as shown in Table 1.
6. Coronary Atherosclerosis-Related Gene Markers and Their Diagnostic Potential
6.1. Expression Changes in Gene Markers During Early CAD Stages
The expression changes in gene markers in the early stages of CAD provide critical clues for early disease detection. For example, mitochondrial and antioxidant enzyme paraoxonase, polymorphisms in genes that predominantly produce reactive oxygen species (NADH oxidase, endothelial-type nitric oxide synthase, and myeloperoxidase), i.e., genes for genetic markers of oxidative stress, have an important role in suggesting patients with an increased risk of CAD due to oxidative stress [79]. miRNAs play an important role in the regulation of pathophysiological processes such as cell adhesion, proliferation, lipid uptake, efflux, and inflammatory mediator production, which provides new molecular insights into their effects on these pathways in coronary atherosclerosis and helps to identify potential therapeutic approaches. The potential of miRNAs as biomarkers for diagnosis, prognosis, or therapeutic response in cardiovascular disease has been particularly enhanced by the realization [80] that miRNAs can be detected extracellularly, even in circulating blood [80]; also, miR-33 controls vascular homeostasis and cardiac response to stress. In addition to miR-33 and miR-122, single nucleotide polymorphisms near miRNA genes have been associated with abnormal lipid levels in human circulation. Some of these miRNAs, such as miR-148a and miR-128-1, target proteins involved in cellular cholesterol metabolism, such as the low-density lipoprotein receptor (LDLR) and ATP-binding cassette A1 (ABCA1) [80,81]; miR-223, which inhibits the hmgs1, sc4mol, and srb1 genes, which are involved in HDL uptake and cholesterol production [82]; and nuclear factor (NF)-2, a single nucleotide polymorphism near the miRNA genes, have been linked to abnormal lipid levels in human circulation. Nuclear factor (NF)-κB signaling is also an important pathway for the activation of several other pro-inflammatory and pro-thrombotic factors. Two cytokine-responsive miRNAs (miR-181b and miR-146a) control NF-κB [83]; the most common miRNAs in CAD (miR-1, miR-133a, miR-208a, and miR-499) are prominently expressed in the heart and play important roles in cardiac physiology. Although a large number of miRNAs have been found to be expressed in ACS and stable CAD, miR-1, miR-133, miR-208a, and miR-499 are usually considered as biomarkers of ACS [84], and these biomarkers, especially miR-499, whose concentration gradient levels correlate with myocardial injury, are the most probable diagnostics of ACS and stable CAD [85]. The abnormal expression of JCAD and GUCY1A3 in vascular endothelial cells may reflect endothelial dysfunction, an early pathological event in atherosclerosis, even before clinical symptoms appear. Using highly sensitive molecular detection techniques, these changes can be identified at an early stage, enabling timely intervention.
6.2. Coupling with Specific Physiological Processes
6.2.1. Regulation of Lipid Metabolism
Variations in PCSK9 and SORT1 affect core mechanisms of lipid metabolism, directly influencing the progression of arterial plaque formation. Circulating pcsk9 degrades the LDL receptor in lysosomes, leading to elevated ldl cholesterol. Camilla Gustafsen identified sortilin, encoded by the hypercholesterolemia risk gene SORT1, as a high-affinity sorting receptor for PCSK9. In healthy populations, circulating PCSK9 is positively correlated with sortilin, suggesting that sortilin is involved in human PCSK9 secretion. Sortilin was confirmed to be a key regulator of PCSK9 activity [86].
By detecting the activity of these gene markers, individual risks of lipid metabolism abnormalities can be assessed, thereby optimizing treatment strategies.
6.2.2. Amplification of Inflammatory Signals
Changes in the expression of inflammation-related genes, such as IL6R, reflect local inflammatory states, which are critical for assessing plaque stability and monitoring disease progression. Genetic analysis showed that the coronary artery disease-associated risk variant rs2832227 was associated with BACH1 gene expression in patients’ carotid plaques, and endothelial Bach1 deficiency reduced turbulent blood flow or Western diet-induced atherosclerotic lesions in atherosclerotic mice, macrophage content in plaques, endothelial cell adhesion molecule (ICAM1 and VCAM1) expression, and decreased blood TNF-α due to IL-1β sub [87]. The NOS2A rs2297518 polymorphism was correlated with increased CCA-IMT, the IL1A rs1609682 polymorphism was correlated with susceptible plaques, and the HABP2 rs7923349 polymorphism showed a correlation. The GMDR analysis showed TNFSF4 rs1234313, IL1A rs1609682, TLR4 rs1927911, ITGA2 rs1991013, NOS2A rs2297518, IL6R rs4845625, ITGA2 rs4865756, and HABP2 rs7923349. There was significant gene–gene interactions between NOS2A rs8081248 and HABP2 rs932650 [88].
6.2.3. Tools for Risk Stratification
Gene markers can also be used for risk stratification in CAD patients based on the association between their expression levels and disease severity. For instance, the following can be stated:
A high expression of SORT1 may indicate a higher plaque burden. Sortilin, which is encoded by SORT1, plays an important role in the development of cardiovascular disease and has functions beyond regulating LDL cholesterol [58,89]. Gain-of-function mutations in PCSK9 may be associated with high-risk patient populations. PCSK9 potently reduced the level of LDLR. These markers provide a basis for risk prediction and therapeutic decision-making within the framework of precision medicine.
We compared key genetic markers of coronary atherosclerosis and their clinical roles, as shown in Table 2.
7. Applications of Gene Markers in Different Diagnostic Scenarios
7.1. Early Disease Detection
Gene marker detection can identify asymptomatic high-risk individuals, enabling early prevention. Early genetic association studies focused on polymorphisms situated within or near genes with a plausible biological role in the disease of interest (so-called candidate gene studies) [90].
In individuals carrying high-risk variants of JCAD or GUCY1A3, genetic screening combined with lifestyle interventions can effectively reduce the incidence of atherosclerosis. MiRNA’s potential as a diagnostic, prognostic, or therapy response biomarker for cardiovascular disease has been particularly increased by the realization that miRNA may be detected outside of cells, even in circulating blood [91]. A study showed that measuring PCSK9 activity levels in blood samples could predict CAD risk 3–5 years in advance. PCSK9 may be a potential target for pharmacologic treatment for this unmet medical need [86].
7.2. Risk Stratification
The expression patterns of gene markers can classify patients into different risk groups as follows:
(1) High-risk group: Patients with high PCSK9 or SORT1 activity may require more aggressive treatment strategies, such as a combination of PCSK9 inhibitors and statins. Risk alleles in SORT1, ABCG8, APOE, and LDLR showed a statistically significantly higher frequency in blood relatives than in the 1000 Genomes Project [89].
(2) Intermediate-risk group: Patients with mild abnormalities in JCAD or NOS3 expression could benefit from a combined strategy of lifestyle interventions and preventive medications. For this, there are preventive medications that can be taken; low-dose colchicine (0.5 mg/d orally) has been shown to safely lower major adverse cardiovascular events by 31% among those with stable atherosclerosis and by 23% after recent myocardial infarction [92].
(3) Low-risk group: Patients without significant abnormalities in genetic testing results may undergo long-term monitoring based on standard protocols. This dedicated pathway for a previously overlooked CAD population, with an accompanying registry, aims to improve outcomes through enhanced adherence to evidence-based secondary prevention and an additional diagnosis of modifiable risk factors observed [93].
7.3. Dynamic Monitoring and Treatment Guidance
The dynamic monitoring of gene markers can also guide treatment response evaluation.
Changes in PCSK9 expression levels after statin therapy can reflect treatment efficacy and plaque stability. Coronary plaques that are prone to rupture and cause adverse cardiac events are characterized by large plaque burden, large lipid content, and thin fibrous caps. Statins can halt the progression of coronary atherosclerosis [94]. Statins are recommended as the first-line therapy for primary and secondary cardiovascular prevention in patients with hypercholesterinaemia and hypertriglyceridemia [95]. Reduced expression of inflammatory genes, such as IL6R, may indicate the effectiveness of anti-inflammatory therapies, such as IL-6 inhibitors. The expanding literature has led to understanding of the proatherogenic role for IL-6 in cardiovascular disease and thus the potential for IL-6 inhibition as a novel method for vascular protection [96].
8. Challenges and Future Directions
Gene markers associated with coronary atherosclerosis, such as JCAD, GUCY1A3, PCSK9, and SORT1, have demonstrated significant diagnostic and prognostic value in multiple studies. By analyzing the expression changes and functional characteristics of these genes, researchers have deepened their understanding of CAD pathogenesis and developed tools for early detection and risk stratification based on gene markers.
However, the clinical translation of gene markers still faces challenges, including sample size, standardization of detection methods, and technological costs. In the future, with the integration of multi-omics data and the advancement of large-scale cohort studies, gene markers are expected to play a more significant role in the precise diagnosis and individualized treatment of CAD (Figure 3).
8.1. Challenges in the Application of Genomic Diagnosis in Coronary Atherosclerosis
Dynamic Changes in Gene Markers
-
(1). Changes During Disease Progression
Coronary atherosclerosis (CAD) is a multi-stage dynamically evolving pathological process involving early lipid deposition, chronic inflammatory responses, plaque stabilization, and later, plaque rupture. When the arterial endothelium encounters certain bacterial products or risk factors as diverse as dyslipidemia, vasoconstrictor hormones inculpated in hypertension, the products of glycoxidation associated with hyperglycemia, or pro-inflammatory cytokines derived from excess adipose tissue, these cells augment the expression of adhesion molecules that promote the sticking of blood leukocytes to the inner surface of the arterial wall [97,98]. As a major consequence of the inflammatory ferment underway in the early atheroma, SMCs migrate from the tunica media into the intima. These cells proliferate and elaborate a rich and complex extracellular matrix. Certain constituents of the extracellular matrix (notably proteoglycans) bind lipoproteins, prolong their residence in the intima, and render them more susceptible to oxidative modification and glycation (nonenzymatic conjugation with sugars). The expression and function of gene markers can vary significantly across different stages of the disease. For example, the following can be observed:
Early stages: Inflammation-related genes (e.g., IL6, TNF-α) show significantly elevated expression, reflecting endothelial dysfunction and inflammatory infiltration. Early atherosclerotic plaques, which consisted predominantly of SMCs, showed enhanced ECE expression in luminal endothelial cells and intimal SMCs [99].
Late stages: Genes associated with plaque rupture and thrombosis (e.g., MMP9) exhibit increased expression, while genes highly expressed in the early stages may return to baseline or decrease. In advanced atherosclerotic plaques, distinct ECE expression was found in accumulated macrophages and in endothelial cells of intraplaque microvessels, while luminal endothelial cells showed relatively weak immunoreactivity for ECE [99].
This dynamic nature necessitates the development of time-based marker monitoring models to capture key molecular events during CAD progression. However, most current studies focus on specific time points and lack longitudinal dynamic monitoring data, limiting the potential of genomic markers in precise CAD diagnosis.
-
(2). Changes Following Therapeutic Interventions
The impact of drug treatments and interventions on the expression of gene markers presents another significant challenge.
Statins: By inhibiting HMG-CoA reductase, statins lower LDL-C levels but may also affect the expression of PCSK9 and other lipid metabolism-related genes, altering the detection outcomes of gene markers. Statins play a role in plaque regression with a reduction in lipid content. These drugs further stabilize atherosclerotic plaque with thickened fibrous caps and macrocalcification that serves to stabilize atheromas [100].
Anti-inflammatory therapies: Anti-inflammatory drugs, such as IL-6 inhibitors, may reduce the expression of inflammation-related genes, diminishing their utility in diagnosis and risk assessment during the later stages of treatment. Furthermore, Ma et al. found that Tan IIA (anti-inflammatory drugs) reduced vascular endothelial inflammation and prevented plaque formation via the COX-2/TNF-a/NF-κB signaling pathway. We have demonstrated for the first time that Tan IIA plays a vital role in attenuating atherosclerosis by downregulating COX-2 expression [100,101]. These results suggest that LLD, mostly statin in this study, might prevent the exaggeration of the G/G genotype-raising effect on inflammatory markers [102].
Currently, the long-term effects of drugs on the dynamic expression of gene markers, particularly the restoration patterns and clinical significance of gene expression after discontinuing therapy, remain insufficiently explored. This knowledge gap restricts the application of gene markers in personalized treatment and therapeutic monitoring.
8.2. Challenges at the Clinical Application Level
8.2.1. Lack of Unified Clinical Standards and Diagnostic Guidelines
Genomic studies have identified numerous potential molecular markers for CAD diagnosis, but their clinical application is hindered by insufficient standardization.
8.2.2. Differences in Detection Techniques
Significant variability exists among the gene detection platforms (e.g., Illumina, Ion Torrent) and data analysis pipelines used by different institutions, leading to inconsistent results. Currently used clinical risk algorithms, including the Framingham Risk Score, the Systematic Coronary Risk Evaluation 2 (SCORE2) system, and the Second Manifestations of Arterial disease 2 (SMART2), are based on traditional risk factors for cardiovascular disease and retrospectively predict future events with limited accuracy [103].
8.2.3. Lack of Unified Thresholds
Diagnostic thresholds for gene markers are often determined independently by researchers, lacking unified clinical guidelines. This not only affects the comparability of results but also limits the broader implementation of gene markers in multi-center studies.
Organizations such as the International Organization for Standardization (ISO) are working to establish technical standards and guidelines for genomic diagnosis, but widespread adoption will require time and large-scale validation.
8.3. Challenges in Integrating Genomic Diagnosis with Traditional Methods
In clinical practice, genomic diagnosis needs to be integrated with traditional methods, such as imaging (e.g., coronary angiography, 2 MORE) and biochemical markers (e.g., LDL-C levels 2 MORE), to achieve higher diagnostic accuracy. However, this integration faces several challenges.
8.3.1. Integration of Multidimensional Data
Several genetic loci have been reported to be significantly associated with CAD by GWASs. Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal [80]. Genomic data often contain complex, multidimensional information, requiring advanced data analysis methods and algorithms for effective integration with imaging and biochemical data. Artificial intelligence and machine learning algorithms are considered potential tools to address this issue, but their clinical feasibility needs further validation.
8.3.2. Optimization of Diagnostic Strategies
Large-scale clinical trials are needed to determine whether gene markers should be used as auxiliary indicators or core diagnostic criteria. Questions such as how to adjust treatment strategies based on changes in gene markers and how to quantify their weight relative to traditional indicators remain unresolved. Except for rare forms that follow a Mendelian inheritance pattern, CAD is a multifactorial trait caused by both genetic and environmental factors. Unlike single gene disorders, genetic studies of complex traits such as CAD are compounded by the lack of a perfect cosegregation between the risk allele and the phenotype and the high prevalence of the risk allele in the population. In a complex trait, the presence of a risk allele is neither necessary nor sufficient to cause the phenotype; hence, establishing causality is difficult [104].
Although genomic diagnosis holds tremendous potential for early detection and the risk assessment of coronary atherosclerosis, its application faces multiple challenges. The dynamic changes in gene markers and the impact of therapeutic interventions limit diagnostic reliability, while the lack of standardized guidelines and difficulties in integrating genomic methods with traditional approaches impede clinical adoption.
In the future, the establishment of dynamic monitoring models, the integration of multi-omics data, and the advancement of standardization efforts will be key to overcoming these challenges. These efforts will promote the comprehensive application of genomic diagnosis in CAD, laying the foundation for precision medicine.
9. Conclusions
Recent advances in CAD genetic research have identified promising markers like JCAD/KIAA1462 and GUCY1A3, offering potential breakthroughs in early diagnosis and personalized medicine. However, clinical implementation faces significant challenges, including dynamic marker variability and the absence of standardized diagnostic guidelines, highlighting the need for comprehensive multicenter studies to establish unified protocols for widespread clinical adoption.
The future of CAD management lies in integrating multi-omics data and understanding cellular energy metabolism pathways, as demonstrated by ATP5G1’s role in mitochondrial function [105]. This gene’s influence on mitochondrial ATP synthesis and subsequent impact on cardiomyocyte energy supply illustrates the potential for targeted interventions based on individual genetic profiles. Research increasingly focuses on developing comprehensive models incorporating genomics, transcriptomics, proteomics, and metabolomics data while investigating critical gene–environment interactions such as dietary habits and air pollution exposure.
The development of dynamic risk assessment models integrating multiple data layers promises more precise early detection and monitoring capabilities. These advanced approaches enable real-time risk evaluation and personalized intervention strategies, potentially transforming CAD prevention and treatment through truly individualized medical care. Success in this endeavor requires continued collaboration across disciplines, including cardiovascular pathology, genetics, and bioinformatics, to translate these scientific advances into improved patient outcomes.
Conceptualization and documentation: H.L., Y.Z. (Yueyan Zhao), Y.Z. (Yuchen Zhang), X.Z., J.Z. and L.B.; visualization: Y.Z. (Yueyan Zhao), H.L., Y.Z. (Yuchen Zhang), Y.L. and J.Z.; original draft: H.L., Y.Z. (Yueyan Zhao), Y.Z. (Yuchen Zhang), X.Z., R.Y. and Y.Y.; supervision: S.S.; review and editing: S.S., J.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
The authors thank everyone who contributed to the research and preparation of this manuscript.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Pathological cascade in coronary atherosclerosis development. This figure illustrates the progression of coronary atherosclerosis, beginning with vascular endothelial cell (VEC) dysfunction caused by genetic predispositions (e.g., JCAD, NOS3), oxidative stress factors (arterial shear stress, dyslipidemia, and smoking), and familial hypercholesterolemia (PCSK9, LDLR, APOB). LDL infiltration into the arterial wall promotes foam cell formation, driven by impaired LDL metabolism (SORT1). The resulting inflammatory response involves cytokines such as TNF-α, IL-6, and MCP-1, which stimulate smooth muscle cell (SMC) proliferation and migration. These processes contribute to vascular remodeling, lumen narrowing, and fibrous cap formation, which may ultimately lead to rupture, foam cell apoptosis, and advanced atherosclerotic lesions.
Enlarge this image.Figure 2. GWAS-identified genes and their role in coronary artery disease pathophysiology. This figure highlights the molecular pathways involving key genes identified through GWASs that contribute to coronary artery disease (CAD). Lipid metabolism: Genes such as PCSK9 and LDLR regulate low-density lipoprotein (LDL) cholesterol levels. Gain-of-function mutations in PCSK9 lead to hypercholesterolemia and increased CAD risk by affecting LDL uptake and degradation. Endothelial cell function: The JCAD/KIAA1462 gene encodes a protein critical for endothelial cell adhesion and vascular integrity. Variants impair endothelial cell function, promoting CAD development. Smooth muscle contraction/relaxation: The GUCY1A3 gene encodes soluble guanylyl cyclase (sGC), a mediator of smooth muscle relaxation. Mutations disrupt vascular tone and contribute to CAD risk. Inflammatory response: Genes like IL6R influence cytokine signaling, contributing to vascular inflammation and lesion progression. MicroRNAs such as miR-126 and miR-33 further modulate inflammatory and lipid transport pathways.
Enlarge this image.Figure 3. Clinical applications of genetic markers in coronary artery disease. This figure summarizes the integration of genetic markers and their clinical utility in diagnosing and managing coronary artery disease (CAD) across different stages. Genetic markers and miRNAs: Key markers such as PCSK9, SORT1, JCAD, NOS3, and associated miRNAs (miR-148a, miR-33, miR-122) are identified from blood samples. These markers are linked to lipid metabolism, vascular inflammation, and endothelial function, contributing to CAD risk stratification and management. Risk stratification: Patients are categorized into high-risk (e.g., PCSK9/SORT1 mutations), intermediate-risk (e.g., JCAD/NOS3 variants), and low-risk groups based on genetic profiles, guiding interventions such as statins, PCSK9 inhibitors, or regular monitoring. Disease stage diagnosis: Genetic and inflammatory markers like IL6R, NOS2A, and IL1Ars2297518 are utilized to differentiate between early-stage CAD (characterized by inflammatory infiltration with markers like IL-6 and TNF-α) and advanced-stage CAD (associated with plaque rupture and infarction, mediated by MMP-9). Clinical applications: These markers facilitate early disease detection, dynamic monitoring of disease progression, and personalized interventions to reduce CAD risk and improve patient outcomes.
Genomic Techniques and Their Contributions to CAD Research.
| Genomic Technique | Applications in CAD | Key Features/Strengths | Challenges |
|---|---|---|---|
| GWAS | Identification of CAD-associated genetic loci (e.g., JCAD, SORT1) across large populations | High-throughput; hypothesis-free approach | Small effect size of individual SNPs |
| Whole-Genome Sequencing (WGS) | Comprehensive genome-wide analysis | Detection of rare variants in non-coding regions | High cost and data complexity |
| Whole-Exome Sequencing (WES) | Focused on protein-coding regions | Identifies pathogenic mutations (e.g., LDLR in familial CAD); cost-effective | Misses regulatory and non-coding elements |
| Transcriptomics (RNA-seq) | Profiling of mRNA expression changes (e.g., IL6R) | Captures dynamic gene expression variability | Sensitive to sample preparation |
| Epigenomics | Identifies epigenetic regulatory mechanisms (e.g., ANRIL, miR-146a) | Uncovers complex regulatory mechanisms | Complex data interpretation |
| Liquid Biopsy | Early detection of CAD risk markers (e.g., PCSK9) from blood cfDNA or RNA | Minimally invasive | Expression variability due to interventions |
Key gene markers in coronary atherosclerosis and their clinical roles.
| Gene Marker | Primary Function | Pathophysiological Role | Diagnostic and Therapeutic Potential | Relevant Studies |
|---|---|---|---|---|
| JCAD/KIAA1462 | Endothelial adhesion and barrier function | Promotes endothelial dysfunction | Early detection of vascular endothelial damage | [ |
| GUCY1A3 | Vascular smooth muscle relaxation | Impairs smooth muscle relaxation | Risk prediction and potential target for vascular therapy | [ |
| PCSK9 | LDL-C metabolism | Elevates plasma LDL-C levels | Cholesterol-lowering therapies (e.g., PCSK9 inhibitors) | [ |
| IL6R | Inflammatory signaling | Drives chronic vascular inflammation | Biomarker for inflammation-driven CAD progression | [ |
| ANRIL | Regulation of smooth muscle proliferation | Influences plaque stability | Risk stratification for cardiovascular events | [ |
| SORT1 | Lipid transport and metabolism | Regulates LDL metabolism | Risk stratification and therapeutic target for lipid disorders | [ |
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; Zhou, Huixian 5 ; Zhang, Jinming 8 ; Song, Siyuan 9 1 School of Medical Imaging, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou 221004, China
2 Medical and Information College, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou 221004, China
3 Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
4 Greenwich Hospital, Yale New Haven Health, Greenwich, CT 06519, USA
5 Department of Internal Medicine, Montefiore Medical Center Wakefield Campus, 600 East 233rd Street, Bronx, NY 10466, USA
6 Department of Radiology, Ohio State University, Columbus, OH 43210, USA
7 Department of Clinical Medicine, The First Clinical Medical College, Norman Bethune University of Medical Sciences, Jilin 130021, China
8 Department of Integrative Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA
9 Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA