Abstract

1. INTRODUCTION

Coronary artery disease (CAD) is a complex disease and it is the most common cardiovascular condition. 1 The risk of developing CAD is related to the interplay of genetic, lifestyle and environmental factors. Regional body adipose tissue (AT) distribution, rather than total body adiposity, is a well‐known marker for cardiovascular risk. 2 In particular, visceral adipose tissue (VAT), including epicardial adipose tissues (EAT) and mediastinal (MAT), has been studied in the last decade in terms of their relationship with CAD. EAT is the most studied cardiac AT and it has gained increased attention due to its location between the visceral pericardium and the heart, in addition to the fact that it is in contact with the coronary vessels. Recent studies have shown that EAT has paracrine or endocrine activities, it can secrete inflammatory factors and adipocytokines, and it has the ability to release and uptake free fatty acids, which plays a vital role in the development of CAD. 3 Moreover, studies have shown that the volume of EAT is associated with the incidence of CAD. 4 , 5 , 6 , 7 Another cardiac AT is MAT, which is situated outside the pericardium that encloses the heart and the relationship between MAT and the development of CAD has been demonstrated and a study reported that the increased volume of MAT was associated with an enhanced CAD risk profile. 8 Also, the volume of MAT was determined to be positively correlated with plasma triacylglycerol and C‐reactive protein, which are well‐known risk factors and play a role in the progression of the disease. 9 Furthermore, diseases associated with fat depots such as CAD are related with excessive accumulation of subcutaneous adipose tissue (SAT), while an increase in the number of adipocytes in the SAT is also accompanied by the presence of metabolic disorders. 10

As with most complex diseases, genetics plays an important role in the development of CAD. Genome‐wide association studies (GWAS) have identified a new susceptibility region located on human chromosome 9p21.3 (Chr9p21.3) containing multiple single nucleotide polymorphisms (SNPs) associated with CAD. 11 , 12 , 13 , 14 The nearest genes, approximately 100 kilobases (kb) away from the core CAD region, are two tumour suppressor genes (cyclin‐dependent kinase inhibitors) CDKN2A and CDKN2B that play an important role in cell cycle regulation, apoptosis, aging and inflammation, which are processes strongly involved in atherogenesis. From a functional perspective, CDKN2A and CDKN2B are functional candidate genes that are potentially implicated in the pathogenesis of atherosclerosis. 15 , 16 Antisense non‐coding RNA (ncRNA) in the INK4 locus (ANRIL ENSG00000240498) is another gene located at the same locus gene, which spans a region of almost 126.3kb and ANRIL overlaps at its 5′ end with CDKN2B and it has a role in the epigenetic regulation of the expression of adjacent protein‐coding genes, including MTAP, CDKN2A and CDKN2B through multiple mechanisms. 17 , 18 Recently, it was revealed that the risk locus could be responsible for regulating ANRIL expression to a certain extent, while 9p21.3 risk locus could also have an impact on CDKN2A and CDKN2B expression through ANRIL expression. 19 , 20 Another study demonstrated that deletion of the targeted 9p21.3 risk locus of orthologous ANRIL interval in mice reduced the expression of CDKN2A and CDKN2B in the heart and led to excessive proliferation of vascular cells that contribute to the development of atherosclerosis. 21 Several investigations have suggested that transcript variants of ANRIL, not only in linear transcript variants but also in circular transcript variants, could also be responsible for the development of CAD. 22 , 23 , 24 , 25 The most studied linear transcript variants of ANRIL with CAD are as follows; the longest ANRIL transcript variant: NR003529 (ENST00000428597.6), and two shorter variants of ANRIL; DQ485454 (ENST00000580576.6) and EU741058 (ENST00000455933.7). Recently, EU741058 and NR003529 transcripts were found to be significantly increased in peripheral blood mononuclear cells (PBMCs) and atherosclerotic plaque tissue from CAD patients compared to healthy individuals. A newly discovered circular transcript variant of ANRIL, circular antisense ncRNA in the INK4 locus (circANRIL or hsa_circ_0008574), consists of exons 5, 6 and 7, where exon 7 is non‐canonically spliced to exon 5. The circANRIL was found in many different cell lines and in many primary cell types, including vascular smooth muscle cells, macrophages, heart and vascular tissue. 24 , 25 Burd et al. found that circANRIL expression is associated with atherosclerotic risk. 25 Another study by Holdt et al. speculated that circANRIL is a prototype of a circRNA regulating ribosome biogenesis and conferring atheroprotection, and in the same study, they suggested that circANRIL remains a potential therapeutic target for the treatment of atherosclerosis. 24 Taken together, observations from all studies conducted thus far emphasize the importance of ANRIL and/or transcript variants of ANRIL in the mechanism mediating the 9p21.3 association, and suggest that ANRIL could be a possible candidate gene of CAD at the 9p21.3 risk locus.

The aim of the study was first to assess whether the AT and PBMCs expressions of ANRIL and ANRIL transcript variants were associated with CAD susceptibility, and to investigate the impact of 9p21.3 locus variations on the expression of ANRIL and transcript variants in PBMCs in AT in Tanzanian CAD patients.

2. MATERIALS AND METHODS

3. RESULTS

3.1. Clinical and anthropometric characteristics of the study population

The clinical and anthropometric characteristics of the study group are presented in Table 1. When the lipid profiles were compared, serum TG, TC, LDL were higher in all CAD patients than in the non‐CAD patients (p<0.05, respectively), whereas serum HDL levels were significantly higher among non‐CAD patients (p<0.05). The average fasting plasma glucose levels of the CAD patients were significantly higher than those of non‐CAD patients (p<0.05). CAD patients had higher levels of systolic blood pressure and diastolic blood pressure. There was also a higher prevalence of obesity, hypertension, diabetes, hyperlipidaemia and smokers in CAD patients compared to non‐CAD patients (p<0.05). In addition, CAD patients were predominantly male (76%). Hypertension was the more commonly associated clinical condition in CAD patients (92%), while the least common one was determined to be hyperlipidaemia (56%).

TABLE 1

Baseline characteristics of the study groups.

Variables	CAD (n=200)	Non‐CAD (n=220)	p‐value a
Age (years)	60.04±7.67	58.64±10.27	0.580
Sex/male (%)	19 (76%)	10(40%)	0.010
Weight (kg)	84.56±11.58	67.52±14.56	0.001
Height (m)	1.60±0.088	1.65±0.073	0.291
BMI (kg/m2)	32.36±4.70	23.84±3.98	0.001
Systolic BP (mmHg)	133.88±28.89	122.64±24.56	0.037
Diastolic BP (mmHg)	91.40±15.54	77.08±16.33	0.003
Glucose (mmol/L)	8.01±2.14	4.13±0.78	0.001
Cholesterol (mmol/L)	5.88±0.95	3.95±0.72	0.001
HDL (mmol/L)	0.84±0.28	1.36±0.30	0.001
LDL (mol/L)	3.97±0.76	2.56±0.66	0.001
VLDL (mmol/L)	1.19±0.44	0.52±0.28	0.001
TG (mmol/L)	2.63±1.31	0.98±0.41	0.001
Obesity (%)	18 (72%)	0	0.001
Hypertension (%)	23 (92%)	11 (44%)	0.001
Diabetes (%)	17 (68%)	1 (4%)	0.001
Hyperlipidaemia (%)	14 (56%)	0	0.001
Smoking (%)	17 (68%)	7 (28%)	0.005

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Note: Values are presented as mean±SD.

Bold and italic values are indicates statistical significant value (p < 0.05).

^a Comparisons of differences between mean values of two groups unpaired Student's t‐test was used.

3.2. Allele and genotypic association of different SNPs

The allele and genotype frequencies of five SNPs (rs10757274, rs2383207, rs2383206, rs10811656 and rs10757278) were analysed for all participants including 200 CAD patients and 220 non‐CAD patients and the results are presented in Table S2. All SNPs were at HWE in both groups (p>0.05). Significant differences were observed in the genotype and allele frequencies of rs10757274, rs2383206, rs10811656 and rs10757278 variants between CAD and non‐CAD patients (p<0.005). The genotype frequencies of rs10757274, rs2383206, rs10811656 and rs10757278 SNPs remained significant when analysed in the subgroup including 25 CAD patients undergoing CABG surgery and 25 non‐CAD patients undergoing heart valve surgery (Table 2). Furthermore, the risk alleles rs10757274 G allele, rs2383206 G allele, rs10811656 T allele and rs10757278 G allele were found to be statistically significant in CAD patients compared to non‐CAD patients of the subgroup.

TABLE 2

The genotypic and allelic frequency distributions of SNPs on chromosome 9p21.3 in the subgroup.

SNP	Genotypic frequencies n (%)			p‐value a	Allelic frequencies			X 2	OR/CI(95%)	p‐value
	Genotype	CAD (n=25)	Non‐CAD (n=25)		Allele	CAD (n=25)	Non‐CAD (n=25)
rs10757274	AA	3 (12)	17 (68)
	AG	16 (64)	7 (28)	0.001	A/G	0.44/0.56	0.82/0.18	15.49	5.79/2.32–14.43	0.001
	GG	6 (24)	1 (4)
rs2383207	AA	3 (12)	12 (48)
	AG	13 (52)	2 (8)	0.563	A/G	0.38/0.62	0.52/0.48	1.98	1.76/0.79–3.91	0.159
	GG	9 (36)	11 (44)
rs2383206	AA	3 (12)	14 (56)
	AG	13 (52)	7 (28)	0.001	A/G	0.38/0.62	0.70/0.30	10.31	3.80/1.65–8.74	0.001
	GG	9 (36)	4 (16)
rs10811656	CC	6 (24)	19 (76)
	CT	11 (44)	4 (16)	0.002	C/T	0.46/0.54	0.84/0.16	15.87	6.16/2.41–15.75	0.001
	TT	8 (32)	2 (8)
rs10757278	AA	7 (28)	19 (76)
	AG	12 (48)	5 (16)	0.001	A/G	0.52/0.48	0.86/0.14	13.51	5.67/2.14–14.99	0.001
	GG	6 (24)	1 (8)

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Note: The subgroup includes 25 CAD patients undergoing CABG and 25 non‐CAD patients undergoing valve placement.

Abbreviations: CI, confidence interval; OR, odd ratio.

Bold and italic values are indicates statistical significant value (p < 0.05).

^a The genotypic and allelic frequency distributions of polymorphisms between the groups were compared using x² and HWE test. In all cases differences were considered significant at p<0.05.

3.3. ANRIL and ANRIL splice variants expression levels in AT and PBMCs

For expression analysis, we included 25 CAD patients undergoing CABG and 25 non‐CAD patients undergoing valve replacement. The expression levels of ANRIL, circANRIL, NR003529, EU741058 and DQ485454 were studied in EAT, MAT, SAT and PBMCs (Figure 1). ANRIL expression levels were significantly up‐regulated in PBMCs of the CAD patients compared to non‐CAD patients (fold change=1.6, p<0.001). Although ANRIL expression levels in EAT, MAT and SAT were found to be increased in CAD patients compared to non‐CAD patients, the differences were not significant (Figure 1A). circANRIL was significantly down‐regulated in PBMCs of CAD patients compared to non‐CAD patients (fold change=5.3, p<0.001) (Figure 1E). The associations of ANRIL and circANRIL expressions with the CAD severity (double stenotic vessels disease [n=13] versus triple stenotic vessels disease [n=12]) were also evaluated. A statistically significant difference in the expression levels of circANRIL was determined between the two groups (p<0.05) (Figure 2). Moreover, the expression levels of EU741058 in PBMCs was down‐regulated in CAD patients compared to non‐CAD (fold change ≈0.8), but the difference was not significant.

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FIGURE 1

Relative expression levels of ANRIL, NR003529, EU741058, DQ485454 and cicrANRIL in EAT, MAT, SAT and in PBMCs among subgroup includes 25 CAD patients undergoing CABG and 25 non‐CAD patients undergoing valve placement. (A) Relative expression levels of ANRIL in CAD and non‐CAD patients; (B) Relative expression levels of NR003529 in CAD and non‐CAD patients; (C) Relative expression levels of EU741058 in CAD and non‐CAD patients; (D) Relative expression levels of DQ485454 in CAD and non‐CAD patients and (E) Relative expression levels of circANRIL in CAD and non‐CAD patients. AU, arbitrary unit; PDMC, peripheral blood mononuclear cells. *p<0.05.

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FIGURE 2

Association of expression levels of circANRIL with severity of coronary artery disease. The analyses were done in subgroup CAD patients that were divided according to the severity of the disease.

3.6. Future importance of the variables

Machine learning (ML) is a highly effective method for disease prediction using ML techniques. It is able to capture the complex interactions between predictors and outcomes during the data process, and could provide a new and novel discernment towards the disease. 31 Recent studies show that random forest (RF) is the most efficient algorithm for the prediction of CAD and it consistently improves the accuracy of the prediction system. 32 , 33 We measured the importance of different features for the risk factors of CAD together with the expression levels of ANRIL and its transcript variants by the mean decrease impurity (Gini importance) of all decision trees in a tuned RF model. The importance of included variables obtained from the tuned RF model is presented in Figure 3. As expected, age, systolic BP, BMI and smoking were among the top risk factors. In addition, we observed that expression levels of ANRIL and circANRIL in PBMCs were also among the top risk factors.

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FIGURE 3

Feature importance analysis of included variables obtained from a tuned random forest model in Tanzanian CAD patients. BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure.

3.7. Improvement in the diagnostic value

Multivariate logistic regression analyses revealed that weight (OR=0.91, 95%CI=0.85–0.96), height (OR=3.54, 95%CI=1.60–7.81), BMI (OR=0.61, 95%CI=0.46–0.80), systolic BP (OR=0.98, 95%CI=0.96–1.0), smoking (OR=5.46, 95%CI=1.62–18.35), diastolic BP (OR=0.95, 95%CI=0.90–0.99), sex (OR=0.22, 95%CI=0.06–0.71) and the expression levels of ANRIL (OR=2.05, 95%CI=1.74–2.35) and circANRIL (OR=13.63, 95%CI=3.74–49.60) in PBMCs were potential transcriptional biomarkers for CAD. To test comparisons of the diagnostic value of expression levels of ANRIL and circANRIL to the top risk factors which are clinical features and observed in the RF model in the development of CAD, ROC curve analysis was performed and the area under curve (AUC) was calculated. Three models for CAD prediction based on clinical features and expressions of ANRIL and circANRIL were built. The first model (clinical model) consisted of CAD risk factors: weight, height, BMI, systolic BP, diastolic BP, smoking and age. The second model (clinical+ANRIL expression model) consisted of the clinical model and ANRIL expression, and in the last model (clinical+circANRIL expression model), circANRIL expression was included in the clinical model. The AUC value was determined to be 0.844 for the clinical model (95% CI: 0.724–0.963, optimal cut‐off: 0.55, specificity: 0.88, sensitivity: 0.80), while the introduction of ANRIL expression increased the AUC from 0.844 to 0.912 (95% CI 0.821–1.0, optimal cut‐off:0.61, specificity:0.96, sensitivity:0.84, p=0.02) (Figure 4A), while the circANRIL expression into the clinical model including the CAD risk factors (age, weight, height, BMI, systolic BP and smoking) the AUC significantly increased from 0.844 to 0.980 (95%CI 0.953–1.0, optimal cut‐off:0.33, specificity:0.88, sensitivity: 1.0, p=0.009) (Figure 4B). Finally, ROC analyses suggested that the detection of ANRIL expression and circANRIL expression together with risk factors of CAD exhibited a higher diagnostic performance compared with the detection of risk factors only. This result implies that the combination of ANRIL and circANRIL expressions in PBMCs has significant potential to be a sensitive and reliable transcriptional biomarker that possibly has a higher diagnostic value for CAD.

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FIGURE 4

ROC curve analyses of the diagnostic value of expression levels of ANRIL and circANRIL. ROC curve comparisons between models. (A) Clinical model and clinical+ANRIL expression model, (B) clinical model and clinical+circANRIL expression model. AUC, area under curve; ROC, receiver operating characteristic.

4. DISCUSSION

While a big part of the human genome has already been transcribed, only ≈2% of the genome appear to be protein‐coding, the rest being deemed ‘non‐coding’. Recent studies have demonstrated that the human genome is prevalently transcribed and produce many thousands of ncRNAs that play a vital role in cellular processes, and the identification of the role of ncRNAs in the disease process could increase our understanding of the pathogenesis of complex diseases. 34 Accumulating studies indicate that several long ncRNAs (lncRNAs), which are a member of the ncRNAs, play important roles as key regulators of gene regulation in the development of cardiovascular diseases. 35 ANRIL is one of the most important antisense transcripts lncRNA, receiving widespread attention as a potentially novel biological regulator. Many roles have been attributed to ANRIL including DNA damage response, DNA repair, epigenetic regulation of CDKN2A/B, controlling the cell cycle checkpoints, apoptosis and RNA splicing. 36 A growing number of recent studies confirm that ANRIL plays pivotal roles in diverse physiological and pathological processes in CAD. 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 37 We herein aimed to ascertain whether the ANRIL and ANRIL transcript variants were involved in the manifestation of CAD susceptibility and where ANRIL transcripts are associated with the 9p21.3 CAD risk in the Tanzanian patients. In the present study, the expression of ANRIL and different transcript variants of ANRIL in PBMCs and in various ATs was reported for the first time.

We reported differential expressions of ANRIL and its transcript variants in CAD patients compared to non‐CAD patients. ANRIL expression levels were significantly increased in the PBMCs of CAD patients, but no statistically significant expression was observed in AT for ANRIL also linear transcripts. On the other hand, one of the main findings of our study was that circANRIL expression was decreased in PBMCs of CAD patients compared with non‐CAD patients, and it well‐discriminated CAD patients from non‐CAD patients, and the circANRIL level was associated with increased CAD severity. Our study demonstrated that up‐regulated ANRIL expression could possibly be involved in CAD pathogenesis. This is in concordance with other studies that investigated the association of ANRIL expression and CAD in different populations. 38 , 39 Moreover, it was demonstrated that increased expression of ANRIL led to increased inflammation, CAD risk, severity and poor prognosis of CAD. 40 , 41 , 42 ANRIL expression was also shown to be associated with inflammation cytokines MCP1 and IL‐10 42 and the knockdown of ANRIL significantly promoted cell proliferation and tubule formation and inhibited inflammatory activation and apoptosis of human umbilical vein endothelial cells. 42 In contrast, in the study of Yang et al. no significant association was reported between CAD and ANRIL expression in the Chinese Han population. 43

Furthermore, we observed a significant down‐regulation of circANRIL expression in CAD patients compared to non‐CAD patients, and according to the comparison of circANRIL expression according to the severity of CAD, we found that triple stenotic vessels disease CAD patients had lower expression levels of circANRIL compared to double stenotic vessels disease CAD patients. We therefore suggest that high expression of circANRIL may play an atheroprotective role in CAD pathogenesis, which would indicate a synergetic effect between circANRIL and ANRIL. Holdt et al. also suggested that circANRIL has a protective role against atherosclerosis. 24 Moreover, recent studies have shown that increased circANRIL expression in human primary smooth muscle cells and vascular endothelial cells in a rat model of CAD could induce apoptosis and inhibit the proliferation; therefore, increased circANRIL expression could decrease the susceptibility of CAD. 44 , 45 However, we failed to detect the same significance of ANRIL and circANRIL expression levels in AT. One potential explanation for this result could be that ANRIL and circANRIL could be differentially expressed in AT and PBMCs. We also found decreased expression levels of EU741058 in PBMCs of CAD patients compared to non‐CAD patients, but the differences only had borderline statistical significance. Several previous studies have reported that EU741058 variant down‐regulated in CAD patients and these results are in agreement with our findings. 43 , 46

Further analysis revealed that rs10757278 and rs10811656 altered the expression levels of ANRIL and ANRIL transcript variants, not only in PBMCs but also in AT. This result emphasized that rs10757278 and rs10811656 could control the expression levels of ANRIL and ANRIL transcript variants. The circANRIL expression levels in PBMCs were significantly down‐regulated in the CAD risk genotype carriers of rs10757278 (GA and GG) and rs10811656 (CT and TT) compared to wild type carriers. Also, the risk genotypes carriers of rs10757278 (GA and GG) and rs10811656 (CT and TT) had significantly higher ANRIL expression in the EAT, MAT and in PBMCs of CAD patients. A recent study by Liu et al. showed that the expression of ANRIL significantly decreased in rs10757278 risk allele carriers in the T cells of 170 healthy individuals. 47 A potential explanation for the diverse results between the research by Liu et al., and our study might be that the samples they used were T‐cells from healthy individuals, whereas in our research, the samples were from EAT, MAT and PBMCs of patients suffering from CAD. 47 Also, the expression levels of NR003529 and EU741058 in EAT, MAT and in PBMCs were statistically significantly higher in the CAD patients carrying the risk genotype of rs10757278 and rs10811656 compared to wild type carriers, but the expression of DQ485454 remained unaffected. This finding indicated that NR003529 and EU741058 were abundantly transcribed in CAD risk genotype carriers leading to the development of CAD.

It is well known that EAT is an important component of VAT due to its contiguity to the CA and the effects of endocrine and paracrine activity secreting pro‐inflammatory and anti‐inflammatory cytokines and chemokines, and it has therefore been suggested to influence coronary atherosclerosis development. 48 , 49 In recent years, MAT has also gained increasing attention due its contribution to the development of atherosclerosis. 50 , 51 Our results also demonstrated the clinical importance of both EAT and MAT in CAD.

Another finding in our study was the increased expression levels of circANRIL in PBMCs. which was also associated with reduced severity of CAD. The circANRIL expression was statistically significantly decreased in triple stenotic vessels disease compared to double stenotic vessels disease (2‐fold). A previous study reported that EU741058 and NR00359 expressions were pre‐dominantly associated with the severity of CAD. 22 Although we could not find any association between EU741058 and NR00359 expression and CAD severity, we successfully showed that circANRIL expression was directly associated with the severity of CAD.

In clinical practice, the increased levels of glucose, TC, TG, LDL and BMI, decreased HDL levels and also hypertension are consistently attributed as major risk factors for CAD. In our study, we observed that these risk factors of CAD were correlated with ANRIL and circANRIL expression levels in the blood. This co‐regulation suggests that ANRIL and circANRIL regulate the risk factors leading to CAD development and they may serve as early disease indicators in CAD patients.

Although the CAD mortality rate is high, the chance of survival is higher if the diagnosis is made sufficiently early in an accurate and efficient way. Therefore, experts have attempted to identify a method of accurately predicting CAD at an early stage by using new statistical techniques, such as data mining, which could help to identify the risk models of the disease, as well as to recognize the disease patterns and the influencing factors. The feature importance selection methods provide the opportunity to increase the accuracy of the prediction of disease compared to traditional methods of classification. In particular, the combination of the feature importance selection algorithm such as RF, which has better accuracy for CAD prediction than other algorithms, is necessary for the efficient prediction of CAD. 33 , 52 We examined the importance of different features for the risk factors of CAD together with the expression levels of ANRIL and its transcript variants and we observed that age, systolic BP, BMI and smoking were among the top risk factors as we expected. In addition, the expression levels of ANRIL and circANRIL in PBMCs were also among the top risk factors. The ROC analysis was conducted accordingly and the analyses showed that a good predictive value of circANRIL expression together with risk factors of CAD exhibited a higher diagnostic performance compared to CAD risk factors alone. The circANRIL expression levels in PBMCs may be a new and non‐invasive diagnostic tool for the diagnosis of CAD. As a screening tool in clinical practice, circANRIL has potential diagnostic value and is worthy of clinical promotion. 53

ANRIL became one of the most popular lncRNA since the discovery of 9p21.3 region as a susceptibility region for CAD. The SNPs in the 9p21.3 region are located 4‐bp apart in the STAT1 binding site of the ANRIL gene. 54 The risk SNPs could disrupt the binding site of STAT1 and this would in turn affect the expression of ANRIL, leading to increased enhancer activity through the following possible mechanism (Figure 5). It is well known that ANRIL regulates the expression of protein‐coding genes via a physically interaction with the specific site, including the Alu element to CBX7 and SUZ12 components of the Polycomb complex (PRC1 and PRC2). ANRIL acts as a pivotal regulator and affects the expression of CDKN2A and CDKN2B tumour suppressor genes in 9p21.3 locus through the same mechanism 55 , 56 (Figure 5). On the other hand, in an animal model, it was demonstrated that ANRIL upregulates vascular endothelial growth factor (VEGF) and activates the nuclear factor kappa B (NF‐κB) signalling pathway. 38 , 57 Moreover, the circANRIL, binds and negatively regulates pescadillo ribosomal biogenesis factor 1 (PES1) protein in the PeBoW complex, known as an essential complex to ribosome biogenesis in macrophages and vascular smooth muscle cells. Furthermore, this negative regulation induces p53 activation and nucleolar stress, resulting in the inhibition of proliferation and induction of p53 in proliferating cells 24 (Figure 5). Taken together, our results emphasize that significant changes of ANRIL and circANRIL expression levels could be directly or indirectly influenced by the progression of atherosclerosis in CAD. ANRIL and circANRIL could be a feature as potential transcriptional biomarkers as well as therapeutic targets and they might also be a useful disease marker.

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FIGURE 5

The possible effect mechanism of the genotype of 9p21.3of linear ANRIL and circANRIL functions according to our results. The risk alleles lead to up‐regulation of ANRIL and down‐regulation of circANRIL. Increased ANRIL expression regulated of the expression of adjacent protein‐coding genes, including CDKN2A and CDKN2B leading to pro‐atherogenic cell properties (increased cell adhesion, increased proliferation and decreased apoptosis) through CBX7 and SUZ12 function in the polycomb complex. 55 , 56 circANRIL binds PES1 protein and impairs ribosome biogenesis, leading to activation of p53 and a subsequent increase in apoptosis and a decrease in proliferative rate. 24 PES1, pescadillo ribosomal biogenesis factor 1; CBX7, Chromobox homologue 7; p53, tumour protein 53; SUZ12, polycomb repressive complex 2 subunit.

5. CONCLUSIONS

Our results revealed that ANRIL expression levels in PBMCs were significantly increased while circANRIL expression levels were significantly decreased in CAD patients when compared to non‐CAD patients. Additionally, circANRIL expression was found to be related with the severity of CAD. Both rs10757278 and rs10811656 SNPs were also determined to be associated with not only ANRIL but also transcript variants of ANRIL (EU741058, NR003529 and circANRIL). Not last, the RF model and ROC analyses showed that measuring the expression levels of circANRIL could offer reliable and sensitive prognostic value and allow for early detection and better monitoring of treatment response or disease recurrence for CAD. Taken together, our findings can provide a new liquid transcriptional biomarker to be used in combination with clinical decision‐making that can improve the early diagnosis and treatment of cardiovascular diseases.

AUTHOR CONTRIBUTIONS

Gokce Akan: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (equal); investigation (lead); methodology (lead); project administration (equal); resources (equal); software (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (lead); writing – review and editing (equal). Evarist Nyawawa: Data curation (equal); writing – review and editing (equal). Bashir Nyangasa: Data curation (equal); writing – review and editing (equal). Mehmet Kerem Turkcan: Formal analysis (equal); software (equal); writing – review and editing (equal). Erasto Mbugi: Conceptualization (equal); project administration (equal); supervision (equal); writing – review and editing (equal). Mohammed Janabi: Conceptualization (equal); project administration (equal); supervision (equal); writing – review and editing (equal). Fatmahan Atalar: Conceptualization (equal); funding acquisition (equal); project administration (equal); supervision (equal); writing – review and editing (equal). A preprint has previously been published. 58

FUNDING INFORMATION

This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflicts of interest.

Supporting information

Notes

Akan G, Nyawawa E, Nyangasa B, et al. Severity of coronary artery disease is associated with diminished circANRIL expression: A possible blood based transcriptional biomarker in East Africa. J Cell Mol Med. 2024;28:e18093. 10.1111/jcmm.18093 [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

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