Interaction and biological pathway analysis of proteomic products in patients with premature coronary artery disease

Abstract

Background

Premature coronary artery disease (PCAD) is characterized by early onset, rapid progression, and poor prognosis, which seriously affects patients’ health and quality of life. In this study, we analyzed the proteomic network and biological pathways of PCAD patients by bioinformatics methods, and mined out the key differential proteins, which provided a theoretical basis for clinical intervention.

Methods

Patients who attended the heart center of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to December 2024 and completed coronary angiography were selected. According to the relevant inclusion and exclusion criteria, a total of 129 patients were included, including 69 in the PCAD group and 60 in the control group. The clinical baseline data of the patients were systematically analyzed. Plasma protein extraction, trypsin digestion and mass spectrometry were completed. The mass spectrometry data were initially separated with the help of proteomics software, and the differential proteins were functionally enriched by RStudio software. Protein interaction networks were constructed by STRING platform and core differential proteins screened were visualized using Cytoscape software (MCODE plug-in).

Results

Differences in gender, smoking, alcohol consumption, hypertension, diabetes, HDL-C, Glu, FIB, LPa, NT-pro-BNP, PCT, and IL-6 were statistically significant (P < 0.05). Sex (P = 0.009, OR = 6.782,95% CI: 1.600-28.746), FIB (P = 0.001, OR = 2.662,95% CI: 1.471–4.818), and LPa (P = 0.041, OR = 1.002,95% CI: 1.000-1.004) were independent risk factors for PCAD. A total of 348 up-regulated proteins and 92 down-regulated proteins were screened by bioinformatics analysis. The occurrence of PCAD is associated with protein synthesis, intercellular communication, molecular interactions, ribosomal metabolism, glyoxylate and dicarboxylic acid metabolic pathways. Ribosomal and translational proteins influence the development of PCAD.

Conclusion

In this study, we found that gender, FIB, and LPa are risk factors for PCAD. The analysis identified 348 up-regulated and 92 down-regulated proteins. Among them, the differentially expressed proteins DHX9, F7, APCS, and PROC were closely related to the biological process of PCAD. The screened ribosomal and translational proteins showed high-frequency associations in protein-protein interaction networks, providing potential differentially expressed proteins for a deeper understanding of the disease.

Background

Cardiovascular disease is the leading cause of death worldwide, of which coronary artery disease (CAD) is the most common. CAD is a chronic disease characterized by coronary atherosclerosis leading to narrowing or occlusion of coronary arteries, resulting in myocardial ischemia, hypoxia, or necrosis. It is more prevalent in economically developed countries and is the leading cause of cardiovascular-related deaths globally, predicted to reach 23.6 million by 2030 [1]. In recent years, the mortality rate from coronary heart disease has shown a clear upward trend in both urban and rural areas, with a higher incidence rate in cities [2]. Premature coronary artery disease (PCAD) is a distinct subtype of CAD with unique risk factors, and its main features include a significantly higher prevalence in men than in women, disorders of lipid metabolism, and heart failure with preserved ejection fraction [3, 4]. PCAD is defined as coronary artery disease in men aged < 55 years and in women aged < 65 years, based on several guidelines and studies [5, 6].

Over the past two decades, there has been a significant increase in cardiovascular disease (CVD) risk factors among young people aged 18–45 years in developed countries, with an increasing prevalence of unhealthy risk factors, including obesity, physical inactivity, tobacco use, consumption of alcohol, and consumption of unhealthy foods, which are more prevalent among young people than among older adults. Compared with older patients with coronary artery disease, younger patients have fewer traditional coronary risk factors, but the onset of the disease is rapid, with most presenting as acute coronary syndrome (ACS), and out-of-hospital mortality is extremely high. In addition, vascular lesion characteristics, interventions, and long-term prognosis are significantly different in younger patients than in older patients, and the burden of disease is greater [4, 7]. Among young adults, hypertension, diabetes, smoking, and dyslipidemia (including elevated lipoprotein (a) and reduced high-density lipoprotein cholesterol) are all on the rise, significantly increasing the risk of PCAD [8, 9]. PCAD may involve a complex interaction of several factors, such as genetics, environment, and lifestyle habits.

Xinjiang is located in the northwestern frontier of China. Its unique geography, climate, and living environment have shaped a lifestyle that is completely different from other parts of China: a diet consisting mainly of high-salt milk tea, beef and mutton, dairy products, and noodles, with very little fruit and vegetables [10, 11]. This dietary pattern tends to lead to the accumulation of risk factors for cardiovascular disease, such as obesity, hypertension, and hyperlipidemia, which in turn drives the incidence of coronary heart disease. The incidence and pathologic changes of PCAD are steadily increasing. The disease has a significant impact on society by significantly increasing morbidity and mortality from cardiovascular events [12]. Through the integration of epigenomics, transcriptomics, proteomics, and metabolomics multi-omics research methods. It can systematically monitor the dynamic changes of the whole chain from DNA modification, gene expression regulation, protein synthesis, and metabolite generation [13]. These multi-omics studies may also provide a new research perspective and theoretical basis for in-depth analysis of the pathogenesis of PCAD. Advances in proteomics technology have opened more opportunities for the development of new diagnostic and therapeutic tools. Using proteomics to comprehensively scan the entire life cycle, capturing the temporal patterns of development, maturation, and aging [14]. Meanwhile, with the advance of bioinformatics technology, analyzing changes in disease-related protein products and their interactions is important to gain insight into the pathogenesis of PCAD [15]. Proteomic analysis has revealed a large number of proteins, complex networks, and pathways associated with early atherosclerosis. Proteins in the blood circulation can provide important information about human health and can serve as potential biomarkers and drug targets [16]. Biomarker combination screening can detect PCAD risk and identify high-risk young people for targeted interventions [17]. This study is based on existing proteomics research on PCAD patients, integrating relevant data and conducting pathway analysis to reveal the biological functions of these proteins and identify potential differentially expressed proteins in PCAD. These findings aim to provide insights for future research and clinical applications.

Methods

Study population

This study was conducted in accordance with the Declaration of Helsinki. The population who attended coronary angiography in the heart center of the First Affiliated Hospital of Xinjiang Medical University within the period from January 2023 to December 2024 were selected. After screening by inclusion and exclusion criteria, PCAD and control populations that met the inclusion criteria were included in the study. Finally, 129 cases were included, 69 cases in the PCAD group and 60 cases in the control group. General information such as sex, age, smoking, and alcohol consumption was controlled. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval No. K202001-011). Subjects participated in this study voluntarily and signed an informed consent form.

Criteria for inclusion and exclusion

Criteria for inclusion: ① Age: women < 65, men < 55; ② Selected patients who first visited the First Affiliated Hospital of Xinjiang Medical University between January 2023 and December 2024 with symptoms of chest pain and other discomforts; ③ Baseline data such as gender, age, history of diabetes, history of hypertension, and laboratory indicators of all patients were collected and recorded from the medical record management system of the First Affiliated Hospital of Xinjiang Medical University; ④ There was no previous history of coronary angiography; ⑤ Clinical information and past medical history were clear.

Criteria for exclusion: ① Metabolic disorders such as hyperthyroidism; ② Patients with secondary dyslipidemia; ③ Severe cardiovascular diseases: acute cerebrovascular disease, severe myocarditis, acute heart failure, cardiomyopathy, and severe heart valve disease; ④ Patients with various causes of hepatic impairment, renal impairment, and combined heart failure, malignant arrhythmia, pulmonary insufficiency, and other severe impairment of vital organ function; ⑤ Patients with diseases that can cause systemic and localized inflammatory responses and active infectious diseases; ⑥ Malignant tumors and acute infectious diseases.

Clinical information collection

General condition indicators such as gender and age were collected from all patients, and height and weight were measured using a standardized method. Indicators of the patient’s past medical history, such as smoking, alcohol consumption, hypertension, diabetes mellitus, and abnormal lipid metabolism, were entered. Disease diagnosis was made using criteria consistent with national or international standards [18].

Collection of blood samples

About 4 ml of peripheral venous blood samples were retained from the enrolled patients on the following day, after 8 h of fasting, using an anticoagulation blood collection tube. The sites of blood collection were all the median elbow vein, and the volume of blood collected was about 4 ml All blood was separated from plasma and blood cells using a cryo-centrifuge within 1 h of collection. Centrifugation conditions: 4 ℃, 3500 rpm/min, 15 min. The upper plasma layer was withdrawn and stored in a −80 ℃ refrigerator for backup, avoiding repeated freezing and thawing of the samples.

Plasma protein extraction and trypsin digestion

2µL of plasma samples were first mixed with 98 µL of 50 mM NH4HCO3 buffer and incubated at 95 °C for 3 min to inactivate proteins. After cooling to room temperature, trypsin was added and digested for 16 h at 37 °C in a 1:25 ratio of enzyme to protein. The digestion reaction was terminated by adding 10 µL of ammonia. Subsequently, the peptides were dried at 60 °C using a SpeedVac. Next, 100 µL of 0.1% formic acid (FA) was added to solubilize the peptides, vortexed and mixed for 3 min, and centrifuged at 1,000 × g for 2 min. For desalting, the 3 M C18 membrane column was sequentially activated with 100 µL of 100% acetonitrile and 50% acetonitrile, washed twice with each solution, and equilibrated with 100 µL of 0.1% formic acid. The peptides were then loaded onto an activated C18 membrane column and eluted twice with 100 µL of 0.1% formic acid. Finally, 100 µL of elution buffer (a mixture of 0.1% formic acid and 50% acetonitrile) was added to the membrane column for elution, and only the eluate was collected. The collected peptides were subsequently dried at 60 °C using a vacuum concentrator.

Mass spectrometry

Peptides were analyzed using a Q Exactive HF-X Hybrid Quadrupole-Orbitrap mass spectrometer coupled to a high-performance liquid chromatography system. Dried peptide samples were redissolved in 100 µL of Solvent A (0.1% formic acid in water). The peptide concentration of each sample was determined by NanoDrop at 280 nm absorbance. Subsequently, 100 ng of peptide was injected into a column with an inner diameter of 75 μm and a length of 9 cm according to a standard loading volume. The column was packed with 1.9 μm ReproSil-Pur C18-AQ packing. Separation was performed using a 15-minute gradient. Mass spectrometry analysis was performed in data-independent acquisition (DIA) mode. MS1 spectra of ions with mass-to-charge ratios ranging from 300 to 1400 were detected by an Orbitrap mass analyzer with a resolution of 30,000. The automatic gain control (AGC) target value was set to 3E6, and the maximum ion implantation time was 20 ms. The resolution of the DIA segment is set to 15,000, and the AGC target value is 1E6. The default state of charge for MS2 acquisition is set to 2.

Quality control

Inclusion and exclusion criteria were strictly followed to collect information about the study subjects. The medical records of all study subjects were carefully written. Strict quality control measures were implemented to ensure the reliability of the mass spectrometry performance and the stability of the whole experimental process. Digested peptide fragments from HEK293T cells (derived from the National Basic Cell Bank) were analyzed along with mixed quality control (QC) samples prepared from all patient plasma samples. In total, 8 HEK293T QC runs and 12 hybrid QC runs were performed. The preparation and analysis of the mixed QC samples strictly followed the same experimental procedures and parameter settings as the cohort plasma samples. Most quality control values are concentrated between 0.93 and 1, indicating high data stability, with most data points fluctuating within a small range (Fig. 1).

Fig. 1.

Correlation analysis of quality control

Data analytics

The data of this study were analyzed using SPSS 27.0 statistical software. Quantitative data were tested for normality. Data that conformed to normal distribution and chi-square were expressed as mean ± standard deviation (X̅±S). Independent samples t-test was used for comparison of the two groups of data. Data were skewed, then expressed as median and interquartile spacing [M(P25, P75)]. Comparisons between the two groups were made using the rank sum test. Count data were expressed as frequencies and component ratios, and comparisons between groups were made using the χ2 test. Independent risk factors for PCAD were analyzed using multifactorial logistic regression.

Protein interaction network mapping using Cytoscape v3.10.3 software. Analyze protein interactions. The MCODE algorithm was used to screen core differentially expressed proteins for early diagnosis and prognostic assessment of diseases. Analyses were performed using the ggVolcano, clusterProfiler, enrichplot, and ggplot2 packages in R version 4.4.0. Analyzed to generate volcano maps. Pathway analysis of GO in terms of biological process (BP), cellular composition (CC), and molecular function (MF). KEGG identifies significant molecules or proteins in the signaling pathway. Significant functional enrichment was P < 0.05.

Results

Clinical baseline data comparisons

The data of clinical information of the two groups were compared. The results of the analysis showed that age, family history, body mass index (BMI), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), C-reactive protein (CRP), and Homocysteine (Hcy) were not statistically significant when analyzed differently (P > 0.05). However, the analysis of variance for gender, smoking, alcohol consumption, hypertension, diabetes, high-density lipoprotein cholesterol (HDL-C), glucose (Glu), fibrinogen (FIB), lipoprotein (a) (LPa), N-terminal pro-brain natriuretic peptide (NT-pro-BNP), procalcitonin (PCT), and interleukin-6 (IL-6) was statistically significant (P < 0.05). Compared to the control group, the PCAD group had a significantly higher percentage of male patients (94.2%) compared to only 5.8% of female patients. In addition, the PCAD group also had higher rates of smoking, alcohol consumption, hypertension, and diabetes than the control group (Table 1).

Table 1.

Comparison of general clinical baseline data between PCAD and control groups

Clinical baselines	Control groups (N = 60)	PCAD (N = 69)	χ/t/z	P value
Gender/n (%)			8.220	0.004
Male	46 (76.70%)	65 (94.20%)
Female	14 (23.30%)	4 (5.80%)
Age (years)	36 (34,39)	37 (34,39)	− 0.527	0.598
Smoking/n (%)	21 (35.00%)	40 (58.00%)	6.794	0.009
Drinking/n (%)	13 (21.70%)	26 (37.70%)	3.902	0.048
Hypertension/n (%)	10 (16.70%)	22 (31.90%)	3.984	0.046
Diabetes/n (%)	2 (3.30%)	10 (14.50%)	4.737	0.030
Familial history/n (%)	16 (26.70%)	10 (14.50%)	2.956	0.086
BMI (kg/m2)	26.98 ± 4.22	27.63 ± 4.43	− 0.843	0.401
TC (mmol/L)	4.26 ± 0.93	4.12 ± 1.07	0.753	0.453
TG (mmol/L)	1.72 (0.99,2.66)	2.01 (1.21,3.17)	− 1.740	0.082
LDL-C (mmol/L)	2.69 (2.31,3.09)	2.48 (1.89,3.17)	− 1.150	0.250
HDL-C (mmol/L)	0.95 ± 0.23	0.86 ± 0.18	2.474	0.015
UA (mg/dl)	329.95 (283.74,403.88)	351.00 (299.75,424.52)	− 1.596	0.110
Glu (mmol/L)	4.55 (4.08,5.21)	5.15 (4.35,6.55)	− 2.540	0.011
FIB (g/L)	2.82 (2.44,3.07)	3.30 (2.72,4.30)	− 3.721	<0.001
LPa (mg/L)	147.14 (46.30,194.13)	186.81 (100.89,315.71)	− 3.180	0.001
NT-ProBNP (ng/L)	19.87 (18.47,44.62)	76.20 (23.25,351.50)	− 5.232	<0.001
PCT (ng/ml)	0.03 (0.01,0.04)	0.04 (0.03,0.05)	− 3.250	0.001
IL-6 (pg/ml)	3.80 (2.26,5.23)	5.10 (2.39,7.91)	− 2.168	0.030
CRP (mg/L)	7.14 (4.59,8.98)	7.92 (4.56,11.52)	− 1.093	0.274
Hcy (µmol/L)	12.60 (10.82,14.59)	12.56 (11.13,15.17)	− 0.534	0.594

Bold values indicate statistical significance (P<0.05)

PCAD was used as the dependent variable, while gender, hypertension, diabetes, FIB, Lp(a), and IL-6 (these variables had P < 0.05 in univariate analysis) were used as independent variables. Logistic regression analysis was performed. After adjusting for confounding factors, the analysis results showed that gender (P = 0.013, OR = 6.129, 95% CI: 1.472–25.516), FIB (P = 0.001, OR = 2.604, 95% CI: 1.451–4.673), and LP(a)[(P = 0.029, OR = 1.002, 95% CI: 1.000–1.004)] were independent risk factors for the occurrence of PCAD (Table 2).

Table 2.

Logistic regression analysis of independent risk factors for PCAD

Variables	B	SE	Wald	P	OR	95%CI
Gender	1.813	0.728	6.206	0.013	6.129	1.472–25.516
Age	0.117	0.068	2.951	0.086	1.124	0.984–1.284
Hypertension	0.518	0.503	1.060	0.303	1.678	0.626–4.497
Diabetes	0.744	0.928	0.643	0.423	2.105	0.341–12.975
FIB	0.957	0.298	10.293	0.001	2.604	1.451–4.673
LPa	0.002	0.001	4.774	0.029	1.002	1.000–1.004
IL-6	0.016	0.028	0.332	0.564	1.016	0.962–1.073

Bold values indicate statistical significance (P<0.05)

Screening and characterization of differential proteins

Among the differential proteins identified in PCAD, 348 up-regulated proteins and 92 down-regulated proteins were screened from 2343 differential proteins, including DHX9, EEF1A1, F7, APCS, and PROC. The expression patterns of these differential proteins were visualized and analyzed by volcano and heat maps (Fig. 2A and B). Detailed data on the fold change of the most significantly altered differentially expressed proteins among the screened up-regulated and down-regulated differentially expressed proteins (Table 3).

Fig. 2.

Screening of differential proteins. A Volcano plot of differential proteins. B Heat map of differential proteins

Table 3.

Differential protein detailed fold change data

Up	Fold-change	Down	Fold-change
EEF1A1	2.27	PROC	0.52
ITIH4	2.11	F7	0.50
NCF1	2.02	NPC2	0.49
APEX1	1.96	APCS	0.48
DHX9	1.88	PROZ	0.43

Functional enrichment analysis of differential proteins

In order to investigate the proteomic features, Gene Ontology (GO) enrichment analysis results showed three categories of Biological Process, Cellular Component, and Molecular Function, totaling 1554 items (BP:1137, CC:223, MF:194). BP includes catabolic processes, intracellular amide metabolic processes, mRNA metabolic processes, peptide metabolic processes, amide biosynthesis processes, peptide biosynthesis processes, translation, RNA splicing, translation regulation, and cytoplasmic translation. CC occurs in cell lumens, organelle lumens, intracellular organelle lumens, extracellular regions, vesicles and parts of extracellular regions, extracellular spaces, extracellular organelles, extracellular vesicles, and exosomes. MF is mainly characterized by binding, protein binding, heterocyclic compound binding, organic ring compound binding, nucleic acid binding, protein-containing complex binding, resultant molecular activity, RNA binding, mRNA binding and mRNA3-UTR binding (Fig. 3).

Fig. 3.

GO enrichment analysis of differential proteins

The Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched 26 meaningful pathways. The results show the top 10 pathways in p-value, including amino acid biosynthesis, glyoxylate and dicarboxylic acid metabolism, fatty acid degradation, carbon metabolism, ribosomes, glycolysis and gluconeogenesis, complement and coagulation cascade reactions, spliceosomes, coronavirus disease, and lysosomes. Based on the enriched counts, the coronavirus disease pathway and the ribosomal pathway therefore predominate (Fig. 4).

Fig. 4.

KEGG enrichment analysis of differential proteins

PPI network analysis construction and hub protein screening

Protein-Protein Interaction (PPI) network analysis was performed on the differential proteins. The STRING platform was used to construct the protein-interaction network, and then Cytoscape software was used to visualize the PPIs. The MCODE algorithm was used to find the central sub-network in the protein-protein interaction network. The 25 differential proteins with the highest connectivity were selected for visualization. The results showed that RPL36A, RPL12, RPS15A, EEF1A1, RPL38, RPS3, RPS23, RPS11, RPS7, RPL7A, RPL6, TPT1, RPL8, RACK1, RPL27, ETF1, RPS29, RPL26, RPS28, RPL10 and RPL37A, RPS14, CCDC124, RPS26, and SERBP1 were identified as key proteins with the highest connectivity. Among them, ribosomal proteins were predominant. These findings reveal a possible strong biological link between ribosomal proteins and PCAD. However, their specific biological functions still need to be further verified (Fig. 5A and B).

Fig. 5.

Construction of PPI network and screening of differential proteins. A Analysis of differential protein interactions. B Screening of differential proteins

Discussion

CAD is characterized by the accumulation of atherosclerotic plaque in epicardial arteries and is the leading cause of death in both developed and developing countries [19]. The global prevalence of PCAD is on the rise. However, it may be due to the diversity and variability of patients’ symptoms and their insidious clinical presentation. The disease is often misdiagnosed or confused with other conditions, leading to delays in treatment [4]. At present, the “Study on Clinical and Genetic Characteristics of Coronary Artery Disease in Young People Under 45 Years of Age (GRAND)” in China has become the first prospective multicenter clinical research project of the largest scale in the world, focusing on PCAD patients under 45 years of age. It analyzes the impact of genes, predisposing factors, clinical characteristics, and interventions on the long-term prognosis of Chinese PCAD patients through three dimensions: clinical, genomic, and metabolomic [20].

In this study, patients aged 45 years and younger were studied, and subjects were divided into PCAD and control groups. The results of multifactorial logistic regression analysis showed that gender, FIB, and LP(a) were independent risk factors for PCAD. In addition, the PCAD group had higher rates of smoking, alcohol consumption, hypertension, and diabetes compared to the control group. The prevalence of diabetes mellitus is higher in people with PCAD. This phenomenon contrasts with the results of a prospective cohort study in France. In French patients with PCAD, obesity and diabetes were less prevalent, while active smoking and high LDL-C levels were more common [21]. However, diabetes is a significant risk factor for PCAD in the United States. This discrepancy may be related to environmental factors, lifestyle (e.g., diet, smoking, and drinking habits), and genetic background in different regions. A study reported that between 1995 and 2013. among young patients with a first diagnosis of obstructive coronary heart disease. Although smoking prevalence and median LDL-C decreased, the prevalence of diabetes and hypertension increased. In the 10 years following the diagnosis of PCAD, moreover, the cumulative incidence of new-onset diabetes among patients with no history of diabetes was 16% [22].

Gender is an uncontrollable factor, with a higher prevalence of males than females in PCAD patients [4, 23]. However, parts of the population, especially the younger age group of 20–40 years. Both men and women may develop the disease despite the lack of traditional risk factors for CVD. It is worth noting that although sex hormones are cardioprotective in women, there is still a prevalence of the disease in the female population [24]. There is a 10-year age gap in the onset of coronary artery disease (CAD) between women and men due to the cardioprotective effects of sex hormones. Traditional cardiovascular risk factors (e.g., smoking, diabetes, hypertension, and hypercholesterolemia) lead to an earlier onset of coronary artery disease (CAD) in men than in women. This approximately 10-year difference is largely attributed to the cardioprotective effects of sex hormones [25, 26]. FIB is a three-chain glycoprotein involved in thrombosis. It is not only a prothrombotic and pro-inflammatory factor, but also a marker associated with inflammation and is capable of directly inducing atherosclerosis formation. In addition, FIB is closely associated with the onset, progression, and prognosis of CAD [27]. Elevated levels of FIB are more pronounced in patients with PCAD. The binding of FIB to the fibrinogen receptor disables fibrinogen-induced thrombolysis, which in turn leads to a reduction in thrombolytic capacity and promotes thrombosis [28, 29]. Young patients associated with traditional atherosclerotic risk factors have different risk profiles. Non-traditional risks such as genetics, inflammation, thrombosis, psychosocial, environmental, and other factors play an important role in the development of PCAD due to atherosclerosis, and the role of LP(a) in particular should not be overlooked [30]. It has been shown that elevated LP(a) levels triple the risk in patients with PCAD and are positively correlated with the degree of coronary artery stenosis [31, 32]. It is suggested that LP(a) testing is clinically important in young and middle-aged populations.

In this study, we screened 348 up-regulated proteins and 92 down-regulated proteins that may be involved in driving the pathology of PCAD by regulating inflammatory responses, lipid metabolism, and vascular endothelial function. The discovery of these differentially expressed proteins provides new clues for a deeper understanding of the pathogenesis of PCAD. Xinjiang is located in northwestern China, and its ecological environment, economic level, genetic background, lifestyle, and dietary habits are significantly different from other regions of China. Studies have shown that the frequency of N5, N10-methylenetetrahydrofolate reductase (MTHFR) C677T and proprotein convertase subtilisin/kexin type 9 (PCSK9) E670G mutations is significantly higher in Uyghur and Kazakh patients with coronary heart disease. This suggests that genetic susceptibility is one of the main causes of high incidence [33]. However, the MTHFR C677T mutation causes enzyme defects, leading to elevated levels of homocysteine (Hcy) in serum. Hcy triggers post-transcriptional upregulation of DHX9 in macrophages and endothelial cells through ROS [34]. It was found that the DHX9 protein enhanced the inflammatory response within macrophages by promoting the transcriptional activity of the DHX9-p65-RNA polymerase II complex, which in turn accelerated the pathological process of atherosclerosis [35]. Lp(a) penetrates the arterial wall, promotes cholesterol deposition in the intima, and activates endothelial cells, triggering inflammation of the vascular wall. In addition, Lp(a) interacts with various cellular components, not only exacerbating the inflammatory response but also leading to endothelial dysfunction and smooth muscle cell proliferation [36]. Individuals carrying DHX9 protein mutations or abnormal expression may be more susceptible to atherosclerosis at a young age due to these mechanisms, thereby increasing the risk of PCAD. By regulating the activity and concentration of coagulation factor VII, the F7 protein may be able to directly influence thrombosis and, thus, the development of coronary heart disease [37, 38]. A cross-sectional survey of 11,608 individuals from the Han, Uyghur, and Kazakh ethnic groups revealed that the overall prevalence of the five major CVD risk factors—high cholesterol, hypertension, obesity, diabetes, and smoking—was significantly higher among the Uyghur and Kazakh populations compared to the Han population. This suggests that ethnic differences and regional environmental factors jointly contribute to the higher CVD burden in Xinjiang compared to the national average [39]. These intermediate risk factors further exacerbate the risk of thrombosis by affecting the function or expression of F7 protein. They may play a bridging role in the influence of F7 protein on PCAD risk. This provides important clues for future in-depth understanding of the pathogenesis of PCAD.

GO enrichment analysis revealed key biological processes involved in differentially expressed proteins, including translation, RNA metabolism and binding, generation of extracellular vesicles and exosomes, and molecular binding functions. These processes are critical for protein synthesis, RNA processing, intercellular communication, and molecular interactions. Abnormalities in these biological processes may cause imbalances in the expression of proteins and cellular dysfunction associated with atherosclerosis, thereby facilitating the pathologic progression of PCAD. It has been shown that plasma exosomes containing miRNAs have an important role in regulating atherosclerosis [40, 41]. This finding may provide a deeper understanding of the mechanisms of endothelial dysfunction in early atherosclerosis and open new avenues for therapeutic strategies in this disease. Regulating the expression of specific miRNAs may help to slow down the process of atherosclerosis and thus reduce the risk of PCAD.

KEGG metabolic pathways as an intuitive tool. Not only can it clearly show the relationship and composition of various metabolic pathways in an organism, but also accurately characterize the composition of molecules within the metabolic pathways and their interactions. This study reveals the importance of the top 10 pathways in the study by analyzing the p-value, including amino acid biosynthesis, glyoxylate and dicarboxylic acid metabolism, fatty acid degradation, carbon metabolism, ribosome function, glycolysis/glycolysis, complement and coagulation cascade, spliceosome function, pathways associated with coronavirus infection, and lysosomal function. Dysregulation of one of these metabolic pathways, glyoxylate and dicarboxylic acid metabolism, is more common in patients with coronary artery disease, especially in patients with PCAD. These metabolic disorders may promote atherosclerosis by affecting the inflammatory response and lipid metabolism [42]. The latest review indicates that intermittent fasting, by aligning the eating window with the circadian rhythm, can simultaneously improve insulin sensitivity, suppress inflammation, and reshape lipid metabolism within 8–12 weeks, thereby significantly reducing traditional risk factors. This circadian synchronization mechanism has been shown to reduce myocardial oxidative stress and improve endothelial function, directly blocking the core pathway of PCAD (“circadian disruption → metabolic-inflammatory abnormalities → premature atherosclerosis”) [43]. The rise in the prevalence of diabetes and obesity in recent decades has led to the prominence of metabolic disorders and an increase in the severity of the metabolic syndrome (MetS) [44]. MetS, characterized by obesity, hyperglycemia, hypertension, and dyslipidemia, is a progressive, chronic, pathophysiological state that significantly increases the risk of cardiovascular disease, particularly PCAD. Dysregulated metabolism of metabolic pathways of amino acids (alanine, aspartate, and glutamate) may be involved in the development of CAD [42]. Certain amino acids, such as glycine, cysteine, alanine, glutamate, and glutamine, significantly influence the role of macrophages in atherosclerosis by participating in specific biological pathways. These amino acids may affect the lipid metabolism function of macrophages, which in turn promotes their accumulation of large amounts of circulating lipids within the arterial wall [45]. Modulating the metabolism of these amino acids or their role in cellular pathways may help slow the development of atherosclerosis and reduce the risk of PCAD. Otherwise, over time atherosclerotic plaque will gradually fibrosis and deposit calcium salts, advanced plaque may protrude into the lumen of the artery will block blood flow, ultimately leading to tissue ischemia [46].

To further explore the expressed proteins associated with PCAD pathogenesis, we established a PPI interaction network to identify potential expressed proteins. The results of the study identified 25 of the most closely linked core-expressed proteins. These differential proteins (mainly ribosomal proteins) may drive the development of PCAD by influencing processes such as protein synthesis, stress responses, and inflammatory responses in vascular cells. However, there is no direct evidence that these ribosomal protein genes are directly associated with PCAD. There are reports that ribosomal proteins (RPs) activate the p53-dependent mitochondrial apoptosis pathway by triggering the ribosomal stress response, thereby inducing cell cycle arrest or programmed apoptosis in vascular endothelial cells. Endothelial cell apoptosis, as the initiating step in atherosclerosis, may promote lipid deposition and plaque formation [47, 48]. The ribosomal stress response of RPs may also accelerate PCAD progression through this pathway. Future studies should validate these hypotheses through genetic analysis (such as screening for RP mutations in PCAD patients) and animal models. One study analyzed protein expression differences in the whole blood of patients with coronary artery disease by RNA sequencing and found that the expression level of ribosomal proteins may be associated with the development of coronary artery disease [49]. Although the exact mechanism is not fully defined, our study reveals that these differentially expressed proteins play an important role in the pathogenesis of PCAD and show good predictive efficacy. Future studies need to explore further the mechanism of action of these differential proteins in PCAD to reveal their potential pathophysiological significance.

The association between proteins and diseases does not necessarily require a causal relationship to enable effective prediction. The CAPRE study further enhanced the accuracy of risk assessment through proteomic screening and comprehensive analysis, independent of established risk factors or biomarkers [50]. A study utilizing a population cohort of approximately 45,000 participants comprehensively described the circulating proteome associated with coronary artery disease, heart failure, atrial fibrillation, and aortic stenosis [51]. We focused our research on PCAD patients aged 45 and under, exploring the interactions of the selected differentially expressed proteins in PCAD. This provides important insights into the potential causal effects of proteins associated with PCAD for future research.

However, our study has the following limitations. First, because of the inherent nature of case-control studies, selection bias and recall bias are difficult to avoid completely, and potential confounders cannot be adequately adjusted for. Therefore, further randomized clinical trials are needed to validate our findings. Secondly, the function and mechanism of action of the differential proteins have not been explored more deeply in this study. It needs to be verified by animal and cell experiments in the future.

Conclusions

In conclusion, the analysis based on clinical data reveals that gender, FIB, and LP(a) are important risk factors for PCAD. Through bioinformatic methods, we identified 348 proteins with up-regulated expression and 92 proteins with down-regulated expression. Among them, differentially expressed proteins such as DHX9, F7, APCS, and PROC are closely associated with multiple biological processes in PCAD. Of special merit. The key differential proteins screened by PPI network analysis, especially those associated with the ribosome and translation processes. These proteins showed a high frequency of connections in the network. This may indicate that they play an important role in the pathogenesis of PCAD. These findings provide new ideas for the development of diagnostic tools and therapeutic strategies for PCAD, which are expected to reduce the incidence of PCAD significantly.

Abbreviations

CAD

Coronary artery disease

PCAD

Premature coronary artery disease

ACS

Acute coronary syndromes

MetS

Metabolic syndrome

ASCVD

Atherosclerotic cardiovascular diseases

BMI

Body Mass Index

TC

Total cholesterol

TG

Triglyceride

LDL-C

Low-density lipoprotein cholesterol

HDL-C

High-density lipoprotein cholesterol

UA

Uric acid

CRP

C-reactive protein

Hcy

Homocysteine

Glu

Glucose

FIB

Fibrinogen

LPa

Lipoprotein A

NT-ProBNP

N-terminal pro-brain natriuretic peptide

PCT

Procalcitonin

IL-6

Interleukin-6

Author contributions

All authors contributed to the article. Blood sample collection and data collection were performed by X L, Y Y, D A, Q Z, and G W. Manuscript preparation and data analysis were performed by L C, C S. Verification of data was performed by L W, B F and L W. F L and Y C checked the work for revisions. All authors commented on the manuscript. All authors read and approved the final manuscript.

Funding

“Tianshan Talents” Cultivation Program for Young Talents in Science and Technology (No. 2022TSYCCX0033).

Data availability

All data in this study were available from the authors or corresponding authors.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (Approval No. K202001-011) and complied with the Declaration of Helsinki. Informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.