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. 2021 Sep 6;6(37):24016–24026. doi: 10.1021/acsomega.1c03171

Comprehensive Analysis of mRNA Expression Profiling and Identification of Potential Diagnostic Biomarkers in Coronary Artery Disease

Jia-Xin Chen 1, Shu He 1, Yan-Jun Wang 1, Xiong-Kang Gan 1, Ya-Qing Zhou 1, Lei Hua 1, Can Hou 1, Sheng Zhang 1, Han-Xiao Zhou 1, En-Zhi Jia 1,*
PMCID: PMC8459403  PMID: 34568680

Abstract

graphic file with name ao1c03171_0009.jpg

The aim of this study is to investigate mRNA expression profiling by RNA sequencing (RNA-seq) in patients with coronary artery disease (CAD) and validate differentially expressed genes (DEGs) as novel biomarkers for CAD. Transcriptome-wide mRNA expression analysis of peripheral blood mononuclear cells was performed in five CAD patients and five controls. Functional enrichment analyses, protein–protein interaction network construction, and hub gene selection were further conducted. Relative expression levels of hub genes were validated by quantitative reverse transcription PCR in larger cohorts. Spearman correlation test and multiple linear regression analysis were applied to examine the relationship between confounding factors with severity of coronary artery atherosclerosis. Receiver operating characteristic (ROC) curve analysis was adopted to identify potentially diagnostic biomarkers for CAD. A total of 527 upregulated and 653 downregulated mRNAs were identified as DEGs in CAD patients. The relative expression levels of beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC), F-box and leucine-rich repeat protein 4 (FBXL4), ubiquitin conjugating enzyme E2 D2 (UBE2D2), and ankyrin repeat and SOCS box containing 1 (ASB1) were significantly different between two groups (all p ≤ 0.05). The severity of coronary artery atherosclerosis was negatively associated with the BTRC gene relative expression level (r = −0.323, p < 0.001) and positively with UBE2D2 (r = 0.285, p < 0.001). ROC analysis of BTRC and UBE2D2 genes showed that the areas under the curve were 0.782 (95% CI: 0.720–0.845, p < 0.001) and 0.753 (95% CI: 0.681–0.824, p < 0.001), respectively. We described the characteristics of mRNA expression in the peripheral blood of CAD patients and controls by RNA-seq. Combined with Spearman correlation analysis and ROC analyses, BTRC and UBE2D2 genes had significantly diagnostic values, which may have potential to act as novel diagnostic biomarkers and therapeutic targets for CAD.

Introduction

Cardiovascular disease is one of the most predominant causes of mortality and morbidity globally, which deprives an estimated 17.9 million individuals of lives each year.1 Approximately, people who died of heart attack accounts for 80% of those.2 Coronary artery disease (CAD), also named as coronary heart disease, is characterized by atherosclerotic lesion of the coronary arteries, which narrows and obstructs the vascular lumen and contributes to myocardial ischemia and even necrosis. It is widely acknowledged that there are numerous risk factors for CAD including traditional external causes and genetic elements.3 People who are accompanied with diabetes, hypertension, obesity, smoking, hyperlipidemia, and family history are predisposed to CAD.4 Nowadays, serological markers such as troponin T/I and creatine kinase-MB are essential for the diagnosis of CAD, which are dependent on the onset of related symptoms.5 Moreover, imaging methods exampled as coronary angiography are accessible to an accurate diagnosis with the best sensitivity and specificity.6 However, it must be pointed that the current existence of various methods are concentrated on the late stage of CAD, which is the main reason for bad prognosis of affected individuals and heavy burden on social economics.

As a complicated disease, CAD is interacted with environmental and genetics factors. Accumulating evidence has shown that gene expression including noncoding RNA regulation, DNA methylation, and protein modification play significant roles in the pathogenesis and progression of CAD.7 Known as genetic instructions, messenger RNA (mRNA) codes protein synthesis and then prompts cell regeneration, migration, and various processes of diseases.8 Upon recognizing this concept, two mRNA vaccines were successfully adopted to control the COVID-19 pandemic last month.9 Moreover, Liu et al. reported that the CKLF-like MARVEL transmembrane domain containing member 5, CMTM5 gene had close relationship with CAD, functioning as a risk factor to promote the development of coronary artery atherosclerosis.10 By means of chip sequencing from online datasets and quantitative reverse transcription PCR (qRT-PCR), Henderson and Wilson described that the 10 hub genes (AKT1, EGFR, CDC42, FGF2, MMP2, MYC, ACTB, IGF1, CXCR4, and LYN) could be used as potential diagnostic biomarkers and therapeutic targets of CAD.11 Virtually, mRNA is featured by abundant biological functions, having the potential to replace the advantageous proteins in some chronic diseases12 and serves as a measurement to predict the effectiveness of primary prevention therapy for CAD.13 Thus, mRNA medicine can provide new insights into risk factors, diagnostic biomarkers, and therapeutic targets of CAD, which is intend to prompt timely treatment and improve the life quality of CAD population.

Therefore, we profiled transcriptome-wide mRNA expression of peripheral blood mononuclear cells (PBMCs) in CAD patients and controls and investigated the hub gene expression from the CAD patients by qRT-PCR. This research might provide a promising diagnosed basis and suggest novel therapeutic targets for CAD patients.

Results

Landscape of Differentially Expressed mRNAs in CAD Patients

RNA sequencing was performed to discover the expression profiling of mRNAs in the PBMCs in CAD patients (n = 5) and controls (n = 5), which was consistent with our previous study.14 After the transcripts per million normalized read count of 10 samples, hierarchical clustering of mRNAs was performed (Figure 1A), and the results indicated that there were significant differences of mRNA expression profile in CAD patients by comparison with healthy controls. Overall, 1180 mRNAs were considered as differentially expressed genes (DEGs) with a fold change threshold of over 2 or statistical significance (p < 0.05). The scatter plot and volcano map determined the differentially high or low expression of mRNAs between two groups (Figure 1B,C), including 527 upregulated mRNAs and 653 downregulated 653 mRNAs.

Figure 1.

Figure 1

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Differentially expressed mRNAs between CAD patients (n = 5) and controls (n = 5). (A) Hierarchical clustering map of two groups. The expression of mRNAs is hierarchically clustered on the y-axis; CAD patients and controls are hierarchically clustered on the x-axis. Expression values are presented in red and green to indicate upregulation and downregulation, respectively. C indicates CAD patients and N indicates controls. (B) Scatter plot of two groups. X-axis: controls (normalized), Y-axis: CAD patients (normalized). The red and blue lines represent the fold change. The mRNAs above the red line and below the blue line indicate more than 2.0-fold change in mRNAs between CAD patients and controls. (C) Volcano plot of two groups. X-axis: log2 (fold change); Y-axis: log10 (p-value). The vertical green lines indicate 2.0-fold up and down, and the horizontal green line represents a p-value of 0.05. The red points represent upregulated or downregulated mRNAs with statistical significance.

Functional Enrichment Analysis, PPI Network Construction, and Hub Gene Selection

To elucidate the biological role of DEGs, 1180 genes were respectively subjected to gene ontology (GO) analysis (Figure 2) and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway analysis (Figure 3). Mitochondrion (GO:0005739), cytoplasm (GO:0005737), small-molecule catabolic process (GO:0044282), benzene-containing compound metabolic process (GO:0042537), steroid dehydrogenase activity (GO:0016229), and outward rectifier potassium channel activity (GO:0015271) were obtained for GO terms. KEGG pathway analyses indicated that apoptosis (hsa04210) and cytokine–cytokine receptor interaction (hsa04060) may function as fundamental roles in the pathogenesis of atherosclerosis. When analyzed by STRING database, these DEGs were presented as protein–protein–interaction (PPI) network with 3451 edges and 1136 nodes via Cytoscape software. CytoHubba analysis section showed the top 10 hub genes (Figure 4), including SKP2, BTRC, FBXL4, UBE2D2, ASB15, SPSB1, ASB1, KCTD6, KBTBD8, and KLHL25 calculated by the Maximal Clique Centrality algorithm (MCC). In these 10 hub genes, SKP2 presented with the highest degree (degree = 1,310,000,000,000). The biological functions of the top 10 hub gene are summarized in Table S1. In addition, the top 10 hub genes were verified by qRT-PCR to explore potential diagnostic biomarkers in CAD.

Figure 2.

Figure 2

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GO annotations of differentially expressed mRNAs.

Figure 3.

Figure 3

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KEGG pathway annotations of differentially expressed mRNAs.

Figure 4.

Figure 4

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The PPI network of DEGs was constructed using Cytoscape. The hub gene analysis was performed by cytoHubba. Top 10 nodes are ranked by MCC. The gradual color represents the degree score. (A) String interactions with top 10 nodes and neighbors and (B) string interactions of top 10 nodes.

Baseline Characteristics of the Study Population

In our present study, 210 participants including 174 CAD patients and 36 controls were investigated, aged 63.69 years (SD, 11.142). Demographic, baseline, and clinical characteristics of all subjects are presented in Table 1. The results indicated that there were significant differences in gender and Gensini score between two groups (both p ≤ 0.05). However, the groups did not differ with regard to age, body mass index, smoking, drinking, C-reaction protein, total cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, glucose, and lipoprotein (a).

Table 1. Baseline Characteristics of the Study Population.

variables CAD patients (n = 174) controls (n = 36) F value/chi-square P value
gender (M/F) 130/44 18/18 8.775 0.003
age (years) 63.71 ± 11.28 63.61 ± 10.52 –0.043 0.966
BMI (kg/m2) 24.98 ± 3.05 25.06 ± 2.64 0.142 0.887
smoking, n (%) 43.7 33.3 1.123 0.289
drinking, n (%) 27.6 20.0 0.757 0.384
hypertension, n (%) 62.1 50.0 1.557 0.212
SBP (mmHg) 129.80 ± 16.90 133.47 ± 19.10 1.075 0.284
DBP (mmHg) 76.17 ± 11.92 80.63 ± 9.54 1.946 0.053
CRP (mg/L) 3.19 (1.56–4.71) 2.12 (1.53–5.03) –0.748 0.455
total cholesterol (mg/dL) 3.62 (3.06–4.32) 3.81 (3.00–4.67) –0.457 0.658
TG (mg/dL) 1.30 (0.95–1.72) 1.28 (0.88–1.67) –0.325 0.745
HDL-C (mg/dL) 0.97 (0.85–1.15) 1.04 (0.85–1.26) –1.110 0.267
LDL-C (mg/dL) 2.08 (1.67–2.65) 2.17 (1.73–2.82) –0.511 0.609
GLU (mg/dL) 5.23 (4.62–6.20) 5.07 (4.58–5.82) –0.814 0.416
Lp (a) (mg/L) 172.00 (72.00–387.00) 122.50 (69.50–235.50) –1.254 0.210
Gensini score 62.00 (25.50–100.00) 0.00 (0.00–4.75) –9.169 P < 0.001
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Validation of the Top 10 Hub Genes

Expression of SKP2, BTRC, FBXL4, UBE2D2, ASB15, SPSB1, ASB1, KCTD6, KBTBD8, and KLHL25 relative to GAPDH was performed between two groups (Table 2). The results indicated that the mRNA level of BTRC, FBXL4, UBE2D2, and ASB1 was statistically different by comparison with them. Also, the expression level of BTRC, FBXL4, and ASB1 genes was much lower in CAD patients, and the expression level of UBE2D2 was significantly higher (Figure 5), which was consistent with our sequencing data. However, no significant differences of SKP2, SPSB1, ASB15, KCTD6, KBTBD8, and KLHL25 mRNA expression levels were discovered between two groups.

Table 2. Expression Levels of mRNAs in Validation Population.

mRNAs CAD patients (n = 174) controls (n = 36) P value
BTRC 0.858 ± 0.199 1.012 ± 0.053 <0.001
FBXL4 0.740 ± 0.304 1.036 ± 0.262 <0.001
UBE2D2 1.516 ± 0.729 1.022 ± 0.218 <0.001
ASB1 0.732 ± 0.203 1.123 ± 0.744 0.010
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Figure 5.

Figure 5

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Validation of the relative mRNA expression level by real-time qPCR. ***P < 0.001 and *P < 0.05.

Spearman Correlations between Clinical Characteristics and mRNA Expression Levels of the Study Subjects

The association between the BTRC, FBXL4, UBE2D2, and ASB1 mRNA expression levels and clinical characteristics of the study subjects were analyzed by the Spearman correlation test (Table 3). The results showed that BTRC (r = −0.323, p < 0.001), FBXL4 (r = −0.374, p < 0.001), and ASB1 (r = −0.308, p < 0.001) were negatively associated with the Gensini score, while the UBE2D2 mRNA expression level presented a positive relationship with severity of coronary artery atherosclerosis (r = 0.285, p < 0.001). Additionally, the FBXL4 expression level was associated with HDL-C, glucose, and BTRC gene (all p < 0.05). These correlations suggested that the four hub genes may be involved in the pathogenesis of coronary artery atherosclerosis.

Table 3. Spearman Correlations between Clinical Characteristics and mRNA Expression Levels of the Study Subjects.

  BTRC
 FBXL4
 UBE2D2
 ASB1
parameters coefficient P coefficient P coefficient P coefficient P
age (years) –0.137 0.052 –0.112 0.118 –0.068 0.335 –0.089 0.229
gender 0.006 0.928 0.106 0.135 –0.038 0.584 –0.043 0.556
BMI (kg/m2) 0.027 0.722 0.061 0.429 –0.076 0.314 0.070 0.377
SBP (mmHg) 0.094 0.182 0.000 0.996 0.032 0.653 0.137 0.063
DBP (mmHg) 0.166 0.018 0.069 0.338 0.010 0.893 0.220 0.003
CRP (mg/L) 0.044 0.531 0.011 0.874 0.039 0.585 0.023 0.758
total cholesterol (mg/dL) 0.023 0.747 –0.002 0.981 0.021 0.765 –0.078 0.292
TG (mg/dL) –0.059 0.404 –0.099 0.172 0.057 0.428 0.121 0.102
HDL-C (mg/dL) 0.125 0.078 0.142 0.049 –0.006 0.931 –0.034 0.647
LDL-C (mg/dL) 0.033 0.643 0.021 0.776 0.022 0.757 –0.085 0.253
GLU (mg/dL) –0.042 0.552 –0.150 0.037 0.092 0.198 –0.125 0.091
Lp (a) (mg/L) –0.098 0.168 –0.089 0.218 0.038 0.598 –0.129 0.081
Gensini score –0.323 <0.001 –0.374 <0.001 0.285 <0.001 –0.308 <0.001
BTRC NA NA 0.386 <0.001 0.041 0.560 0.263 <0.001
FBXL4 0.386 <0.001 NA NA –0.003 0.969 0.227 <0.001
UBE2D2 0.041 0.560 –0.003 0.969 NA NA –0.120 0.101
ASB1 0.027 0.710 0.113 0.126 –0.058 0.428 NA NA
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Association between Severity of Coronary Artery Atherosclerosis and Confounding Factors by Multiple Linear Regression Analysis

To identify the possible related factors with the severity of coronary artery atherosclerosis, multiple linear regression analysis was performed. In the multiple linear regression analysis model by using the Gensini score as a dependent variable and other variables as the independent ones, UBE2D2 (β = 0.326, p < 0.001), BTRC (β = −0.207, p = 0.011), and FBXL4 (β = −0.188 p = 0.016) were significantly associated with the severity of coronary artery atherosclerosis (R2 = 0.361) (Table 4 and Figure 6).

Table 4. Multiple Linear Regression Analysis between Variables and Severity of Coronary Artery Atherosclerosis.

variables β 95% CI P value
UBE2D2 0.326 14.679–38.632 <0.001
BTRC –0.207 –119.385–15.654 0.011
FBXL4 –0.188 –68.282–7.125 0.016
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Figure 6.

Figure 6

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Association between confounding factors and severity of coronary artery atherosclerosis by liner regression analyses

Receiver Operating Characteristic Curve Analyses for Diagnostic Biomarkers of CAD

To further explore the diagnostic biomarker of CAD, ROC analyses were conducted. The area under the receiver operating characteristic (ROC) curves and the cutoff value of mRNA are shown in Table 5 and Figure 7. There were significant differences in the diagnostic cutoff value of BTRC (95% CI: 0.720–0.845, p < 0.001), FBXL4 (95% CI:0.675–0.854, p < 0.001), UBE2D2 (95% CI: 0.681–0.824, p < 0.001), and ASB1 (95% CI: 0.553–0.816, p = 0.002), whose areas under the curve (AUC) were 0.782, 0.765, 0.753, and 0.685, respectively.

Table 5. ROC Curve Analyses for Diagnostic Predictors of CAD.

mRNA AUC (95% CI) P value cut-off sensitivity specificity Youden index
BTRC 0.782 (0.720–0.845) <0.001 0.941 0.628 0.963 0.591
FBXL4 0.765 (0.675–0.854) <0.001 0.951 0.756 0.667 0.423
UBE2D2 0.753 (0.681–0.824) <0.001 1.281 0.521 0.917 0.438
ASB1 0.685 (0.553–0.816) 0.002 0.954 0.846 0.556 0.402
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Figure 7.

Figure 7

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ROC curve analyses of mRNAs as diagnostic predictors for CAD.

Discussion

CAD is a public health problem all over the world and considered as the main reason for morbidity and mortality. The purpose of this study was to perform a comprehensive analysis of mRNA expression profiling and to identify the hub genes of CAD, which provides new insights into potentially diagnostic biomarkers and therapeutic targets. In our study, we described the characteristics of mRNA expression in the peripheral blood of CAD patients and controls by RNA-seq. Compared with two groups, we identified 1180 significantly differentially expressed mRNAs, including 527 which were upregulated and 668 which were downregulated mRNAs, representing 7.9% of the total number. GO and KEGG functional enrichment analyses show that these 1180 DEGs were mainly localized in the mitochondrion (GO:000573) for cellular component, annotated as metabolic process (GO:0044282) for biological process, interpreted as oxidoreductase activity (GO:0016229), and steroid dehydrogenase activity (GO:0016628) for molecular function, which indicated that cytokine–cytokine receptor interaction and apoptosis may play important roles in the occurrence and development of atherosclerosis. Furthermore, by means of the PPI network and hub genes integrated analysis, we selected the top 10 hub genes for validation in enrolled 210 individuals. The relative expression level of BTRC, FBXL4, and ASB1 genes was statistically lower in CAD patients, and the UBE2D2 expression level was significantly higher, which was consistent with our sequencing data. The Spearman correlation test and a multivariable linear regression model demonstrated that the expression levels of BTRC and UBE2D2 were significantly associated with the severity of coronary artery atherosclerosis. The area under the ROC curve illustrated that BTRC, FBXL4, and UBE2D2 had significant diagnostic values for CAD.

With the development of methods and technology in exploring the gene expression profiling of various diseases, it had provided new insights into novel biomarkers for diagnosis and treatment aspect of chronic diseases, including CAD.15 According to a previous study reported by Aquila, HES1 and SIRT1 genes played a protective role in the process of inflammation and oxidative stress in patients with stable angina.16 Also, Jing et al. discovered that the expression mRNA levels of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and Homer 1 were statistically different between CAD patients and controls through large sample verification.17 Hueso’s previous study illustrated that CCR7 and FOXO1 had associated with atherosclerosis progression, having potential to function as blood markers by miRNA and mRNA counter-expression analysis.18 Compared with these previous studies, it must be emphasized that our present study demonstrated the comprehensive analysis of mRNA expression profiles in the peripheral blood mononuclear cell of CAD by RNA-seq, elucidated the biological functions of differentially expressed mRNA, and identified BTRC and UBE2D2 as potentially diagnostic biomarkers for CAD.

Beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC) gene also known as betaTrCP has been previously reported in thyroid cancer cells by Wu, which is interacted with VEGFR-2 degradation and ubiquitination to inhibit the process of angiogenesis.19 In addition, the association between the DNA methylation level of BTRC (cg24381155) and CAD was found by Miao et al. in 2019.20 Ubiquitin conjugating enzyme E2 D2 (UBE2D2) gene was formed as a stable complex with MUL1-RING, which may play an important role in apoptosis, cell growth, and regulation of mitochondrial function according to Lee’s study.21 It is obvious that our previous study suggested that prevention of mitochondrial dysfunction may be a promising target in CAD by MS analysis of human coronary arteries.22 Therefore, the present study may provide new insights into potentially diagnostic biomarkers and novel therapeutic targets of CAD.

There are some limitations in our study. As a single-center study from Jiangsu Province Hospital, the First Affiliated Hospital of Nanjing Medical University, there were only 210 participants composed of 174 CAD patients and 36 controls enrolled. A multicenter study with larger cohorts should be conducted to verify these differentially expressed mRNAs as diagnostic biomarkers for clinical practice. Additionally, as functional validation experiments were not performed, the specific mechanisms by which these different expressed mRNAs are involved in the development of atherosclerosis were not explored. We are conceived that the functional roles and mechanisms of these mRNAs should be clarified and validated in the future.

Conclusions

In our study, RNA sequencing of PBMCs in CAD patients and controls was adopted to identify 1180 significantly differentially expressed mRNAs, which were significantly related to CAD. Functional enrichment analyses, PPI network construction, and hub gene selection were performed to explore potentially diagnostic biomarkers of CAD. Additionally, the relative expression levels of BTRC, FBXL4, UBE2D2, and ASB1 gene showed significant differences between two groups validated by qPCR. Combined with multiple linear regression analysis and ROC analyses, these results illustrated that BTRC and UBE2D2 gene had significantly diagnostic values for CAD. This study suggested that these DEGs may play predominant roles in the pathogenesis of coronary artery atherosclerosis and have the potential to act as novel diagnostic biomarkers and therapeutic targets for CAD.

Materials and Methods

Study Population

From October 2020 to April 2021, a total of 210 individuals consisting of 174 CAD patients and 36 controls (with a mean age of 63.69 years, SD 11.142) were enrolled from the First Affiliated Hospital of Nanjing Medical University. The excluded criteria are as follows: congenital heart defects, cardiomyopathy, type 1/2 diabetes, malignant tumors, cerebrovascular diseases, severe bacterial/virus/fungi infection, severe hepatic insufficiency, and renal insufficiency. Participants with one of the above diseases were obligated to retreat from this study. Consistent with discharge diagnosis, more than 50% stenosis of main coronary arteries supplying blood to the heart were selected into CAD groups and less than 50% stenosis of these major coronary arteries were selected as controls.23 According to the results of coronary angiography, the degree of coronary artery stenosis was evaluated by calculating the Gensini score.24 The medical history (age, gender, smoking history, alcohol intake, blood pressure, and heart rate) and laboratory parameters (C-reaction protein, total cholesterol, triglyceride, glucose, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and lipoprotein a) were extracted from medical records. Five CAD patients and five controls were selected for transcriptome high-throughput sequencing, which has been explained comprehensively in our previous study.14 In addition, the whole participants were used for validation of differentially expressed mRNAs and to explore the hub genes in the pathogenesis of CAD.

The methods were performed in accordance with the Declaration of Helsinki, and all experimental protocols were approved by the ethics committee of Nanjing Medical University and the First Affiliated Hospital of Nanjing Medical University. The written informed consent was obtained from all patients or their families under the Declaration of Helsinki.

PBMC Isolation

The whole 210 participants draw a volume of 9 mL of fasting blood samples upon admission to hospital by venipuncture, which were stored in anticoagulation tubes at 4 °C. Then, lymphocyte separation medium (TBD, Tianjin, China) was applied to separate PBMCs from the middle white monolayer after centrifuging at 2000 rpm 4 °C for 20 min by means of density gradient centrifugation. Finally, PBMCs were extracted and reserved in the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) at −80 °C.

RNA Isolation, Library Construction, and Sequencing

Experimental protocols of RNA isolation, library construction, and sequencing were specifically described in our previous publication.25 RNA was extracted according to standardized protocols. Briefly, RNA was separated with 200 μL of chloroform, precipitated with an equal volume of isopropanol, and washed with 1 mL of 75% ethanol. Then, it was dried for 5 min and dissolved in RNase-free water. The concentration and quality of RNA were evaluated using a NanoDrop ND1000 spectrophotometer (Agilent Inc. USA). RNA (2 μg) of each sample was used for library construction and sequencing. First, we used Ribo-Zero rRNA Removal Kits (Illumina, USA) to remove the rRNAs from the total RNA of each sample following the manufacturer’s instructions. Next, RNA libraries were generated using rRNA-depleted RNAs with the TruSeq Stranded Total RNA Library Prep Kit (Illumina, USA), whose quality and quantity were controlled by using the BioAnalyzer 2100 system (Agilent Technologies, USA). Finally, the 10 pM libraries were denatured as single-stranded DNA molecules, captured on Illumina flow cells, amplified in situ as clusters, and sequenced for 150 cycles on an Illumina HiSeq Sequencer, which were performed by several independent bioinformatics technologists.

Bioinformatics Analyses

After quality filtering and Q30 measurement, the high-quality reads were aligned to the reference genome (UCSC hg19) guided by the Ensembl gtf gene annotation file with HISAT2 software (v2.04). Then, several independent instructors got the FPKM as the expression profiles of mRNA by using Cuffdiff software (part of Cufflinks, v2.2.1) to calculate fold change and p-value for differentially expressed mRNA identification. Hierarchical clustering of these differentially expressed mRNAs was performed in R heatmap.2 with the transcripts per million normalized read count. GO and KEGG pathway enrichment analyses were performed on these differentially expressed mRNAs.

Construction of the PPI Network and Selection of Hub Genes

The 1180 DEGs were totally loaded into STRING (https://string-db.org/cgi/input.pl) database (version 11.0)26 to get PPI networks and the interaction data among all co-expression genes were picked with a significant confidence score >0.9. Then, the interaction data were typed into the Cytoscape software (version 3.8.0)27 to structure a PPI network. Based on the above data, we used cytoHubba to explore important hubs and clustering an interactome network calculated by MCC.28 The parameters were set as follows: Hubba nodes = top 10 nodes ranked by degree, display options = check the first-stage nodes, display the shortest path, and display the expanded sub network.

qRT-PCR

qRT-PCR was used to verify the top 10 hub genes. TransScript One-Step gDNA Removal (Vazyme, Nanjing, China) and cDNA Synthesis SuperMix (Vazyme) were adopted to prepare for the process of cDNA synthesis according to the manufacturer’s instructions. Primers were designed from Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast), and the sequencing of primers for qRT-PCR are listed in Table S2. StepOnePlus (Applied Biosystems) equipment with the SYBR qPCR Master Mix (Vazyme) was used,29 and cycle threshold (CT) values of all samples were normalized to GAPDH. The relative mRNA expression levels were calculated using the 2–ΔΔct method.

Data Analysis

Data were analyzed using the Statistics Package for Social Sciences (version 22) and GraphPad Prism (version 8.0). Normally distributed variables were presented as mean ± standard deviation (SD), and the comparisons between two groups were analyzed by independent-sample t-test. Otherwise, variables with a skewed distribution were presented as median and quartile ranges, and the comparisons were made using the Mann–Whitney U-test. Categorical variables were compared using chi-square analyses. Spearman correlation test was used to assess the relationship between clinical characteristics and mRNA relative expression levels. Multiple linear regression analysis was used to explore the factors influencing the severity of coronary artery atherosclerosis. Furthermore, we defined CAD patients as group 1, while the controls as group 0. ROC curve analysis of expression levels of mRNAs was performed to determine diagnostic predictors with the sensitivity, specificity, and Youden index for CAD. Two-tailed P value less than 0.05 was considered statistically significant.

Ethical Approval and Consent to Participate

Informed consent for the research use of the samples was obtained from the patients or their families. The methods were performed in accordance with the approved guidelines, and all experimental protocols were approved by the ethics committee of Nanjing Medical University and the First Affiliated Hospital of Nanjing Medical University.

Availability of Data and Materials

All data and materials have been made available from the corresponding author if necessary.

Acknowledgments

This study received support from the National Natural Science Foundation of China (grants 81170180, 30400173, 30971257, and 81970302) and the Priority Academic Program Development of Jiangsu Higher Education Institutions. E.-Z.J. is an Assistant Fellow at the Collaborative Innovation Center for Cardiovascular Disease Translational Medicine.

Glossary

Abbreviations

ASB1

ankyrin repeat and SOCS box containing 1

ASB15

ankyrin repeat and SOCS box containing 15

BTRC

beta-transducin repeat containing E3 ubiquitin protein ligase

CAD

coronary artery disease

CRP

C-reaction protein

DEGs

differentially expressed genes

FBXL4

F-box and leucine-rich repeat protein 4

GAPDH

glyceraldehyde-3-phosphate dehydrogenase

GLU

glucose

HDL-C

high-density lipoprotein cholesterol

LDL-C

low-density lipoprotein cholesterol

KBTBD8

kelch repeat and BTB domain containing 8

KCTD6

potassium channel tetramerization domain containing 6

KLHL25

kelch-like family member 25

PPI

protein–protein interaction

qRT-PCR

quantitative real-time polymerase chain reaction

ROC

receiver operating characteristic curve

SKP2

S-phase kinase associated protein 2

SPSB1

splA/ryanodine receptor domain and SOCS box containing 1

TG

triglyceride

UBE2D2

ubiquitin conjugating enzyme E2 D2

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.1c03171.

  • Significantly functional enrichment analysis of 10 hub genes in CAD patients and primers of the top 10 hub genes (PDF)

Author Contributions

† J.-X.C. and S.H. contributed equally to this paper. As a guarantor, E.-Z.J. conceived the study. J.-X.C. and S.H. designed the study and wrote the draft. X.-K.G., Y.-J.W., and S.Z. enrolled the participants and collected the data under the supervision of Y.-Q.Z., L.H., C.H., and H.-X.Z. All authors agree to be accountable for all aspects of the work.

The authors declare no competing financial interest.

Supplementary Material

ao1c03171_si_001.pdf (91.5KB, pdf)

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