Upregulation of hsa_circ_0000745/hsa_circRNA_101996 in peripheral blood monocytes is associated with coronary heart disease

Study population

In our study, 374 inpatients were recruited between December 2020 and July 2021 at the Peking University People’s Hospital. The participants were divided into the CHD and non-CHD groups according to the American College of Cardiology/American Heart Association guidelines. CHD was diagnosed by coronary angiography (CAG) and defined as coronary stenosis ≥50 % in one of the coronary arteries at least [17]. The diagnosis was independently completed by two experienced interventional cardiologists through visual observation. There was no sign of coronary atherosclerosis or microvascular disease in the control group, as shown by negative treadmill exercise test (TET) and emission computed tomography (ECT) results. Patients were excluded based on the following criteria: (1) autoimmune disease, (2) any other systemic acute or chronic inflammatory disease, (3) liver and kidney dysfunction, (4) malignancies, (5) uncontrolled hypertension, or (6) valvular heart disease or malignant arrhythmias. We further divided the CHD group into four subgroups, including stable angina pectoris (SAP), acute myocardium infarction (AMI), unstable angina pectoris (UAP), and old myocardial infarction (OMI). Our study was approved by the Ethics Committee of Peking University People’s Hospital and conducted according to the ethical guidelines of the Declaration of Helsinki (1975). Informed consent was obtained from all participants.

Study design

All participants were examined by a senior cardiologist using CAG to verify the presence of CHD. The first five confirmed patients with CHD and five clinically matched controls were enrolled for circRNA screening using Agilent Gene Expression Hybridization Kit. The Microarray was scanned with an Agilent Microarray Scanner G2505C. Agilent Feature Extraction (version 11.0.1.1) software was used to extract the data. Total RNA was extracted from PBMCs obtained from venous blood samples for microarray analysis. The expression of hsa_circ_00000745 was verified in a cohort of 297 and 77 patients with and without CHD by extracting total RNA from PBMCs and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The association between the expression of hsa_circ_00000745 and CHD was investigated.

CircRNA microarray expression profiling

Total peripheral blood RNA was extracted from the peripheral blood PBMCs of five patients in the coronary heart disease group and five patients in the non-coronary heart disease group for microarray analysis. The purity and concentration of total RNA from OD260/280 and OD260/230 were determined with a spectrophotometer (NanoDrop ND-1000). Amplification and labeling were performed according to the manufacturer’s instructions. The labeled RNA was purified and hybridized onto a microarray (Human circRNA array, version 2.0) using an Agilent Gene Expression Hybridization Kit (Agilent, USA). The circRNA array data were analyzed using the Agilent Feature Extraction software (version 11.0.1.1). CircRNAs with fold change (FC) ≥1.5 and p values less than 0.05 were defined as differentially expressed circRNAs (DEcircRNAs). GPL Accession Number for microarray platform is GPL21825.

PBMC isolation and total RNA extraction

Blood specimens were collected from peripheral veins using ethylenediaminetetraacetic acid (EDTA)-acid-anticoagulated vacutainers. PBMCs from 3 mL of whole blood were extracted immediately using Ficoll-Paque (Cytiva, USA) according to the manufacturer’s instructions. Total RNA was extracted using the Rapid Blood Total RNA Extraction Kit (Takara, Japan). To clarify, the procedure involves taking 750 µL of TRIzol and combining it with PBMC (Peripheral Blood Mononuclear Cells). Phase separation is then performed by adding 200 µL of chloroform. The RNA is precipitated by adding 100 % isopropanol, followed by two washes with 75 % ethanol. Finally, the RNA is removed by adding 20 µL of RNase-free water. RNA samples were quantified using a Nanodrop 2000 spectrophotometer (Thermo Scientific, USA). All samples were dilute to a final concentration of 100 nM.

Reverse transcription and quantitative polymerase chain reaction

Reverse transcription was performed using a PrimeScript RT reagent kit (Takara Bio, Japan) according to the manufacturer’s protocol. Quantitative polymerase chain reaction (qPCR) was performed with SYBR-Green Premix Ex Taq (Takara Bio, Japan) and monitored using a Roche cobas z 480 sequence detection system (F. Hoffmann-La Roche, Ltd., Switzerland). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression was used as a loading control for normalization. The relative expression levels of hsa_circ_00000745 were determined using the 2^-△△ct method. The primers used for qPCR are shown in Supplementary Table S1.

Statistical analysis

Normalization and subsequent data processing were performed with the R software limma package. Cluster3.0 draw relevant graphics software. T-test was used to test the difference of gene expression between the two groups. FC≥1.5 and p≤0.05 were considered statistically significant. Bioconductor software and EASE (Expressing Analysis SystematicExplorer) GO and KEGG Analysis, Fisher exact probability for statistical tests (p<=0.05). The data are shown as means ± standard deviations or proportions when appropriate. Horizontal lines indicate medians when the scatter plot depicts circRNA expression. Categorical variables were tested by the chi-square test, while continuous variables were tested using the Mann-Whitney U test. Logistic regression analyses were performed to calculate the odds ratios (OR) for CHD. Statistical significance was set at p<0.05. All statistical analyses were analysed using SPSS software (version 25.0; SPSS Inc., Chicago, IL, USA) and GraphPad Prism 8.0.1 (GraphPad Software Inc.).

DEcircRNA screening in patients with CHD

To identify the DEcircRNAs between patients with CHD and controls, we extracted the peripheral blood total RNA from the PBMCs of five patients with CHD and five controls. CircRNA microarray analysis indicated that the expression of circRNAs differed significantly between the CHD and control groups (Figure 1). A total of 224 DEcircRNAs were identified between the two groups which 183 DEcircRNAs were upregulated and 41 were downregulated (|FC|≥1.5, p≤0.05). On analyzing the chromosome distribution of DEcircRNAs, the upregulated circRNAs in the CHD group were mainly concentrated on chromosome 11, while the downregulated circRNAs were mainly located on chromosome 12 compared to the control group. The total DEcircRNAs were mostly located on chromosome 1 (Figure 2A). The analysis of circRNA types showed that the upregulated and downregulated circRNAs in the CHD group compared to the control group were mostly exon circRNAs (Figure 2B). Among these DEcircRNAs, hsa_circ_0000745/hsa_circRNA_101996 had the highest fold change (FC=2.621 and p=0.0027) (Supplementary Table S2).

Figure 1:

Differentially expressed circRNA profile in peripheral blood mononuclear cells between CHD group and control group. (A) Cluster heat map of differentially expressed circRNAs between CHD group and control group. The bottom coordinate is the sample number and group, and the right is the name of circRNA with obvious differences between groups. The closer the color is to red, the higher the expression level is, and the closer it is to green, the lower the expression level is. (B) Volcano plot of differentially expressed circRNA between CHD group and control group, and the red part is the circRNA that meets the statistical conditions (p-value≤0.05, fold change≥1.5).

Figure 2:

Bioinformatic analysis of differentially expressed circRNAs in PBMCs of CHD. (A) The distribution of differentially expressed circRNAs on chromosomes. (B) Distribution of the coding genes of up- and downregulated circRNAs. (C) GO analysis of the up- (left panel) and downregulated (right panel) circRNAs(p<0.05). (D) KEGG analysis of the up- (left panel) and downregulated (right panel) circRNAs.

Gene ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG) analyses

GO analysis (Figure 2C) showed that the upregulated circRNAs were mainly involved in mRNA catabolism, protein methylation, peptidyl-lysine modification, mitosis, organelle assembly, and nuclear protein export. Downregulated circRNAs were mainly concentrated in integrin-mediated signaling pathways, platelet aggregation, intercellular adhesion, and cell differentiation. In terms of molecular function, the upregulated circRNAs were mainly involved in RanGTP enzyme binding, chromosome binding, enzymatic action, histone methyltransferase action, nuclease action, and nucleoplasmic carrier action, whereas the downregulated circRNAs were predominantly involved in actin binding, myofilament binding, cadherin binding, ADP binding, and oxidoreductase interactions. In terms of cellular components, the upregulated genes were mainly concentrated in the intracellular region, nuclear cytoplasm, chromosomes, intracellular organelles, nuclear cavity, nuclear membrane, and nuclear cytoplasm. The downregulated genes were mainly enriched in adhesion junctions, anchor junctions, cell cortex, and intracellular and membrane-bound organelles.

KEGG analysis (Figure 2D) revealed that the pathways with the most differentially expressed circRNAs between the CHD and non-CHD groups were as follows: upregulated genes were mainly enriched in lysine degradation, ubiquitin-mediated proteolysis, RNA degradation, RNA transport, human T-cell leukemia virus infection, cell senescence, and protein processing in the endoplasmic reticulum. Downregulated genes were mainly enriched in carbon metabolism, glycolysis/gluconeogenesis, major carbon metabolism in cancer, focal adhesion, pathogenic Escherichia coli infection, bacterial invasion of epidermal cells, amino acid synthesis, cytoskeleton regulation, RNA degradation, and other pathways.

These findings suggest that there are distinct changes in gene expression related to various biological processes and pathways in the studied condition. The upregulation of genes related to lysine degradation, proteolysis, RNA regulation, viral infection, cell senescence, and protein processing indicates potential alterations in these cellular processes. Conversely, the downregulation of genes involved in carbon metabolism, energy production, cancer-related pathways, cell adhesion and invasion, amino acid synthesis, cytoskeleton regulation, and RNA degradation may imply disruptions in these cellular functions. Further research would be necessary to elucidate the specific implications and mechanisms of these gene expression changes in the context of the studied condition.

Hsa_circ_0000745/hsa_circRNA_101996 is upregulated in patients with CHD

To investigate whether hsa_circ_0000745/hsa_circRNA_101996 is associated with CHD, we determined the expression of hsa_circ_0000745/hsa_circRNA_101996 in a cohort of 77 controls and 297 patients with CHD. The patient characteristics are shown in Table 1. In the CHD group, 206 (69.4 %) patients were men with a median age of 63.69 years. In the non-CHD group, 48 patients (62.3 %) were men, with a median age of 59.44 years. Hemoglobin (Hb), platelet (PLT), and HDL levels were significantly lower in the CHD group than in the non-CHD group, while the APOA1 and sdLDL levels were higher in the CHD group than in the non-CHD group. We found that hsa_circ_0000745/hsa_circRNA_101996 expression was significantly increased in patients with CHD (Figure 3A). Patients with CHD were further divided into four subgroups: SAP, UAP, AMI, and OMI. We found that hsa_circ_0000745/hsa_circRNA_101996 expression was higher in the UAP and AMI groups than in the SAP group. These data demonstrate that the levels of hsa_circ_0000745/hsa_circRNA_101996 in PBMCs were increased in patients with CHD and were positively associated with the severity of myocardial damage (Figure 3B).

Figure 3:

Hsa_circ_0000745/hsa_circRNA_101996 is upregulated in CHD patients. (A, B) The expression of hsa_circ_0000745/hsa_circRNA_101996 in PBMCs was detected in 297 cases of CHD and 77 cases of non-CHD using a real-time qPCR assay. The statistical methods were ANOVA. (A) The expression of hsa_circ_0000745/hsa_circRNA_101996 in stable angina pectoris (SAP), unstable angina pectoris (UAP), acute myocardium infarction (AMI) and old myocardial infarction (OMI) was analyzed. Data are expressed as the median with interquartile range, and the p-value is indicated.

Hsa_circ_0000745/hsa_circRNA_101996 is associated with CHD

Next, we performed a multifactorial logistic regression analysis of the patients with CHD. As shown in Figure 3, hsa_circ_0000745/hsa_circRNA_101996 expression was upregulated in the CHD group. Subsequently, hsa_circ_0000745/hsa_circRNA_101996 expression was divided into four groups according to quartiles. After adjusting for the traditional risk factors (age, sex, body mass index [BMI], smoking, alcohol consumption, C-reactive protein [CRP], small and dense low-density lipoprotein [sdLDL], and lipoprotein-associated phospholipase A2 [LP-PLA2]), it was found that the second hsa_circ_0000745/hsa_circRNA_101996 expression quartile was a risk factor for CHD compared to the lowest quartile (OR: 2.890; 95 % confidence interval [CI]: 1.261–6.6, p=0.012). At the same time, we found that the highest hsa_circ_0000745/hsa_circRNA_101996 expression quartile was a risk factor for CHD compared to the lowest quartile (OR: 2.709; 95 % CI: 1.126–6.519, p=0.026) (Figure 4).

Figure 4:

Risk factors for CHD enrolled in binary logistic regression analysis. The expression of hsa_circ_0000745/hsa_circRNA_101996 was divided according to the quartiles (Q). Q1: lower quartiles; Q2: low quartiles: Q3: middle quartiles; Q4: higher quartiles.