Association between microRNA-146a rs2910164 polymorphism and coronary heart disease: An updated meta-analysis : Medicine

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Research Article: Systematic Review and Meta-Analysis

Association between microRNA-146a rs2910164 polymorphism and coronary heart disease: An updated meta-analysis

Bao, Qinxue MDa,*; Li, Rui BDa; Wang, Chengfeng BDa; Wang, Shan BDa; Cheng, Minli BDa; Pu, Chunhua MDa; Zou, Lei BDa; Liu, Chao BDa

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doi: 10.1097/MD.0000000000031860
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1. Introduction

Coronary heart disease (CHD) is a worldwide chronic complex disease with high morbidity and mortality caused by genetic and environmental factors.[1,2] Although we have many advances to diagnose and predict the prognosis of CHD, some new and feasible ways need to be explored to meet the requirement of clinical work. According to epidemiological studies, many risk factors, including smoking, diabetes, hypertension, and genetic variations, are involved in the pathological progress of CHD.[3–5] With the development of genomics and proteomics, many new candidate biomarkers have emerged to diagnose and predict CHD.

MicroRNA (miRNA) is a set of short non-coding RNA that could negatively regulate mRNA’s translation. The length of MiRNA is approximately 18 to 25 nucleotides, and it could bind with several target genes.[6,7] Moreover, one target gene also could bind with several miRNAs to affect the process of mRNA translation. Studies indicate that many pathophysiological procedures for different diseases, such as cancer, hypertension, stroke, diabetes, and CHD, consist of various cellular pathways.[8,9] One of the vital mechanisms is miRNA regulation.[10,11] Studies show that miR-146a is involved in the process of CHD.[12,13] For instance, a previous study showed that miR-146a expression was higher in the coronary artery disease (CAD) group.[14] Furthermore, treatments with angiotensin II receptor blockers and statin could suppress the level of miR-146a and toll-like receptor 4 signal pathway, which might be the molecular mechanism concerning the anti-atherosclerosis of angiotensin II receptor blockers and statin in CAD patients. Other studies demonstrated that miR-146a was likely associated with the development of atherosclerosis.[15,16]

Several studies have detected the association between miR-146s rs2910164 polymorphisms and CHD.[17] However, due to the small samples, findings from different groups showed contradictory results that miR-146a rs2910164 might increase the risk of CHD or not be associated with CHD.[18,19] Xu Liu et al have demonstrated that rs2910164 polymorphism of miR-146a was significantly associated with CHD risk.[20] Therefore, we obtained the present meta-analysis referring to 18 case-control studies to assess the relationship between miR146a rs2910164 polymorphisms and CHD risk. Our meta-analysis is an updated meta-analysis with a larger sample size explored to examine further the association (6859 cases and 8469 controls).

2. Materials and methods

2.1. Publication search strategy

We processed a systematic search by Chengfeng Wang, Shan Wang, and Chao Liu independently using the database including PubMed, Cochrane Library, EMBASE, Web of Science, China’s National Knowledge Infrastructure, VIP, and Wan Fang until April 30, 2022. The keywords were as follows: (“microRNA-146a” OR “miRNA-146a” OR “miR-146a” OR “rs2910164”) AND (“polymorphism” OR “polymorphisms” OR “single nucleotide polymorphism” OR “SNP” OR “variant” OR “variants” OR “variation” OR “genotype” OR “genetic” OR “mutation”) AND (“coronary heart disease” OR “CHD” OR “coronary artery disease” OR “CAD” OR “acute myocardial infarction” OR “ACS” OR “myocardial infarction” OR “MI” OR “acute myocardial infarction” OR “AMI” OR “cardiovascular disease” OR “ischemic heart disease” OR “IHD”). Chengfeng Wang, Shan Wang, and Chao Liu also manually examined the reference lists within the eligible studies to figure out additional involved research. The meta-analysis was based on previously published studies; thus, no ethical approval and patient consent were required.

2.2. Inclusion and exclusion criteria

All the eligible studies should match the inclusion criteria: Case-control design; Evaluation concerning miR-146 rs29010164 and CHD; The genotype of the control should accord with the Hardy-Weinberg equilibrium (HWE); The study has sufficient data for present statistics; Languages including Chinese and English. The exclusion criteria were as follows: case report, review, meta-analysis, repeat publication, abstract, letter, animal model, or mechanism research. Moreover, studies that were not conforming to the inclusion criteria should be excluded.

2.3. Data extraction and quality evaluation

Two investigators (Chunhua Pu and Lei Zou) performed the data extraction independently, which consisted of the name of the first author, published year, country of the participants, genotyping methods, tissue, numbers of cases and controls, genotype (GG/CG/CC) frequencies of cases and controls, allele(C/G) frequencies of case and control, and HWE of P-value in controls as shown in Table 1. We evaluated the quality of the studies with the software Revman5.4. During data extraction and quality assessment, Qinxue Bao participated in discussions when Chunhua Pu and Lei Zou encountered discrepancies.

Table 1 - Main characteristics of included studies in our meta-analysis.
Author Year Country Genotyping methods Tissue Samples size GG CG CC G C HWE of P-value in control
Case Control Case Control Case Control Case Control Case Control Case Control
Wu Qi 2022 China RT-PCR Venous blood 92 100 10 28 37 52 45 20 57 108 127 92 P > .05
Agiannitopoulos 2020 Greece PCR-RFLP, HRM,Sanger Sequencing Peripheral blood leukocyts 200 200 91 101 95 84 14 15 277 286 123 114 .6657
Mir 2020 Indian ARMS-PCR Peripheral blood 100 100 11 5 51 40 38 55 73 50 127 150 P > .05
Zhang Linxun 2020 China RT-PCR Venous blood 100 100 14 28 64 52 22 20 92 108 108 92 P > .05
Qiu 2019 China SNPscan Venous blood 505 1109 60 154 246 516 194 436 366 824 634 1368 .946
Manuel 2018 Mexico TaqMan Peripheral blood 218 595 116 277 85 267 17 51 317 821 119 369 P > .05
SHRESTHA 2018 China PCR-RFLP Peripheral blood 295 253 47 52 164 112 84 89 258 216 332 290 P > .05
Wang 2017 China MALDI-TOF MS Peripheral blood 353 368 62 84 155 179 136 105 279 347 427 389 .645
Bastami 2016 Iran TaqMan Peripheral whole blood 300 300 111 150 155 128 34 22 377 428 223 172 .5718
Sung 2016 Republic of Korea PCR-RFLP Peripheral blood leukocytes 522 535 77 73 242 260 203 202 227 406 648 664 .46
Huang 2015 China TaqMan Peripheral blood 722 721 143 132 308 348 266 237 594 612 840 822 .83
Chen 2014 China PCR-LDR Peripheral blood leukocytes 919 889 269 301 463 435 187 153 1001 1037 837 741 P > .05
Hamann 2014 Germany HRM Whole blood 206 200 120 117 74 73 12 10 314 307 98 93 .748
Xiong 2014 China PCR-RFLP Peripheral whole blood 295 283 41 61 141 125 113 97 223 247 367 319 ≥.05
Chen Lin 2013 China TaqMan Whole blood 658 658 181 194 305 330 172 134 667 718 649 598 P > .05
Ramkaran 2013 South Africa PCR–RFLP Whole blood 106 100 50 45 43 46 13 9 143 136 69 64 .8501
Yang Ying 2012 China TaqMan Peripheral blood leukocytes 853 948 165 189 392 457 272 271 722 835 936 999 P > .05
Li Ling 2010 China TaqMan Venous blood 415 1010 82 210 184 455 149 345 348 875 482 1145 0.186
ARMS-PCR = Amplification Refractory Mutation System polymerase chain reaction, HRM = high-resolution melting, HWE = Hardy-Weinberg equilibrium, MALDI-TOF MS = matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, PCR-LDR = polymerase chain reaction-ligation detection reaction, PCR-RFLP = polymerase chain reaction-restriction fragment length polymorphism, RT-PCR = reverse transcription-polymerase chain reaction, SBI = silent brain infarction.

2.4. Statistical analysis

To assess the association between miR-146a rs2910164 polymorphism and CHD risk, the review manager (Rveman5.4, Cochrane Collaboration, London, UK) performed the statistical analysis by Qinxue Bao and Minli Cheng. HWE of the control in each study was calculated by X2-test. When the P-value was < 0.05 was considered a disequilibrium of the control group. The odds ratios (ORs) with 95% confidence intervals (CIs) were applied to evaluate the association between miR-146a rs2910164 polymorphism and susceptibility of CHD. There were 5 genetic models, including the allelic model (G vs C), homozygous model (GG vs CC), heterozygous model (CG vs CC), dominant model (GG + CG vs CC), and recessive model (GG vs CG + CC) in the current study. The pooled odds ratios were assessed via Z-test, defined as significant with P < .01(0.05/5) after Bonferroni correction. I-square statistical test was applied to evaluate the heterogeneity between studies. The random-effect model was employed when I2 > 50% using the Mantel-Haenszel method, which indicates evident heterogeneity in our included study.[21] Otherwise, the fixed-effect model was used. Begg’s funnel plot was utilized to estimate the publication bias.

2.5. Trial sequential analysis (TSA)

Because of random error or lack of statistical accuracy and power, the results of meta-analysis might acquire false results, defined as type I errors (false-positive errors) and type II errors (false-negative errors). Consequently, Qinxue Bao did the trial sequential analysis (TSA) to evaluate whether cumulative data were sufficiently powerful to draw the conclusion. The meta-analysis results were used to set the incidence in control group and the relative risk reduction. We calculated the required information size (RIS) by TSA Beta (Copenhagen Trial Unit, Centre for Clinical Intervention Research, Denmark.), using α = 0.05(2-sided) and β = 0.20(a power of 80%) to reach a reliable consequence.[22–24]

2.6. Target prediction and enrichment analysis

To determine the possible function of miR-146a, we exploited the target scan human 8.0[25]( to predict the target gene and then conducted the enrichment analysis via WEB-based GEne SeT AnaLysis Toolkit[26] (Web Geatalt, Moreover, we also analyzed the relationship between miR-146a rs2910164 polymorphism and disease by miRNASNPv3[27] (!/).

3. Results

3.1. The features of the eligible articles

Figure 1 shows the entire screening process of our study. A total of 1191 studies were acquired from PubMed, Cochrane Library, EMBASE, Web of Science, China’s national knowledge infrastructure, VIP, and Wan Fang databases. Among them, 203 duplicates were precluded from the current study. Another 988 studies were screened according to the titles and abstracts. An amount of 43 alternative articles were evaluated through a full-text review. These 24 articles were excluded because of unavailable data and duplicate data. Ultimately, 18 articles in our study contained 6859 cases and 8469 controls. The features of the original studies are exhibited in Table 1.

Figure 1.:
Flow chart of the screening process of our study.

3.2. Results of the meta-analysis

The results of the current meta-analysis for the association between miR-146a rs2910164 polymorphism and CHD risk are shown in Table 2 and Figure 2.

Table 2 - Association between miR-146a rs2910164 polymorphism with coronary heart disease (CHD).
Genotype comparison Whole/Subgroup n OR [95% CI] Z (P value) Heterogeneity of study design
χ 2 df (P value) I 2 (%)
Allelic model (G vs. C) Whole 18 0.86 [0.78, 0.95] 2.95 (.003*) 64.54 17 (<.00001) 74%
Chinese 11 0.86 [0.78, 0.94] 3.31(.0009*) 25.70 10 (.004) 61%
Other 7 0.93 [0.70, 1.22] 0.56(.58) 37.96 6 (<.00001) 84%
Large size 7 0.86 [0.77, 0.96] 2.63(.009*) 22.24 6 (.001) 73%
Small size 11 0.86 [0.72, 1.04] 1.58(.11) 42.19 10 (< .00001) 76%
homozygous model (GG vs. CC) Whole 18 0.79 [0.67, 0.92] 2.97 (.003*) 35.68 17 (.005) 52%
Chinese 11 0.74 [0.62, 0.88] 3.40(.0007*) 22.34 10 (.01) 55%
Other 7 0.96 [0.68, 1.37] 0.21(.83) 10.93 6 (.09) 45%
Large size 7 0.85 [0.76, 0.96] 2.68(.007*) 4.35 6 (.63) 0%
Small size 11 0.70 [0.50, 0.98] 2.07(.04) 27.68 10 (.002) 64%
heterozygous model (CG vs. CC) Whole 18 0.89 [0.83, 0.97] 2.78(.005*) 34.09 17 (.008) 50%
Chinese 11 0.87 [0.75, 1.01] 1.80(.07) 26.67 10 (.003) 63%
Other 7 0.99 [0.80, 1.21] 0.14(.89) 6.37 6 (.38) 6%
Large size 7 0.88 [0.80, 0.97] 2.67(.008*) 6.24 6 (.40) 4%
Small size 11 0.92 [0.70, 1.22] 0.55(.58) 27.40 10 (.002) 64%
dominant model (GG + CG vs. CC) Whole 18 0.87 [0.76, 0.99] 2.18 (.03) 39.86 17 (.001) 57%
Chinese 11 0.83 [0.72, 0.96] 2.52(.01) 28.12 10 (.002) 64%
Other 7 1.00 [0.76, 1.31] 0.00(1.00) 9.54 6 (.15) 37%
Large size 7 0.88 [0.80, 0.96] 2.99(.003*) 5.44 6 (.49) 0%
Small size 11 0.85 [0.63, 1.15] 1.06(.29) 34.37 10 (.0002) 71%
recessive model (GG vs. CG + CC) Whole 18 0.86 [0.76, 0.97] 2.36 (.02) 38.04 17 (.002) 55%
Chinese 11 0.82 [0.71, 0.94] 2.88(.004*) 19.71 10 (.03) 49%
Other 7 0.99 [0.76, 1.28] 0.10 (.92) 16.48 6 (.01) 64%
Large size 7 0.93 [0.84, 1.02] 1.55 (.12) 4.86 6 (.56) 0%
Small size 11 0.77 [0.61, 0.98] 2.10 (.04) 30.05 10 (.0008) 67%
Other (Caucasian, Indian, Korean, Mexico).
95% CI = 95% confidence interval, CHD = coronary heart disease, CI = confidence interval.
* Survived the Bonferroni correction.

Figure 2.:
Forest plots of odds ratios for the association between microRNA-146a rs2910164 and coronary heart disease (CHD) risk in the whole population. (A) G vs. C; (B) GG vs. CC; (C) CG vs. CC; (D) GG + CG vs. CC; (E) GG vs. CG + CC. CHD = coronary heart disease.

We gained 6859 cases and 8469 controls from 18 eligible studies. The G allele at rs2910164 was associated with significantly decreased CHD risk under the allelic model (OR = 0.86, 95% CI = 0.78‐0.95, P = .003), homozygous model (OR = 0.79, 95% CI = 0.67‐0.92, P = .003), heterozygous model (OR = 0.89, 95% CI = 0.83‐0.97, P = .005), dominant model (OR = 0.87, 95% CI = 0.76‐0.99, P = .03), and recessive model (OR = 0.86, 95% CI = 0.76‐0.97, P = .02) in total population(P < .05). However, under the dominant and recessive model, the association between miR-146a rs2910164 polymorphism and CHD risk did not accomplish statistical significance after Bonferroni correction (P < .01). There were obvious heterogeneities under all genetic models. Then we performed a subgroup analysis by ethnicity and sample size.

We did the subgroup analysis based on ethnicity within 11 studies with about 5207 cases and 6439 controls. The G allele at rs2910164 was related to meaningfully reduce the risk of CHD under the allelic model (OR = 0.86, 95% CI = 0.78‐0.94, P = .0009), homozygous model (OR = 0.74, 95% CI = 0.62‐0.88, P = .0007), dominant model (OR = 0.83, 95% CI = 0.72‐0.96, P = .01) and recessive model (OR = 0.82, 95% CI = 0.71‐0.94, P = .004) in Chinese population. Besides, the significance did not remain under the dominant model after the Bonferroni correction. However, we did not observe the association of miR-146a rs2910164 with CHD risk under the heterozygous model (OR = 0.87, 95% CI = 0.75‐0.1.01, P = .07).

Significant heterogeneities were also observed among all genetic models in the Chinese population. Therefore, we conducted another subgroup analysis based on sample size. In the large sample size, we did not examine significant heterogeneities in the homozygous model (I2 = 0%), heterozygous model (I2 = 4%), dominant model (I2 = 0%), and recessive model (I2 = 0%). At the same time, there was obvious heterogeneity in the allelic model (I2 = 73%). Besides, when the P value was Bonferroni corrected, the pooled data suggested that miR-146a rs2910164 was associated with significantly decreased CHD risk under the allelic model (OR = 0.86, 95% CI = 0.77‐0.96, P = .009), homozygous model (OR = 0.85, 95% CI = 0.76‐0.96, P = .007), heterozygous model (OR = 0.88, 95% CI = 0.80‐0.97, P = .008), and dominant model (OR = 0.88, 95% CI = 0.80‐0.96, P = .003). Whereas miR-146a rs2910164 had no remarkable relationship with CHD risk under recessive model (OR = 0.93, 95% CI = 0.84‐1.02, P = .12).

3.3. The bias of the publication and sensitivity analysis

In our meta-analysis, Begg’s funnel plot was performed to explore the possible bias of the publication. Taking the heterozygous model in the whole population as an example, the Begg’s funnel plots showed no marked asymmetry, as shown in Figure 3, meaning there was no significant publication bias risk. After omitting one article at once, there was no prominent effect on the result in the sensitivity analysis, suggesting that our meta-analysis’s present finds were reliable.

Figure 3.:
Begg’s funnel plot of publication bias for the heterozygous model of miR-146a rs2910164 polymorphism in the whole population.

3.4. Results of TSA

The results of TSA are shown in Figure 4. For miR-146a rs2910164 polymorphism and susceptibility to CHD in the total population, under the allelic model, homozygous model, and heterozygous model, the cumulative z-curve crossed the TSA boundary and the RIS, which represented that our results were robust and crucial (Fig. 4A, 4B, 4C). We could obtain the same tendency in the Chinese population under the allelic and homozygous models (Fig. 4D, 4E). Furthermore, in Figure 4F, the z-curve crossed the conventional test boundary (z = 1.96, P < .05) and did not intersect the RIS under the recessive model. Meanwhile, the z-curve was close to the TSA boundary, reflecting that the result might be a false positive. Thus, more studies might be required to certify this consequence further. As shown in Figures 4G, 4H, 4I, and 4J, the results of rs2910164 G versus C group, GG versus CC group, CG versus CC group, and GG + CG versus CC group in subgroup analysis based on sample size revealed that the z-curve did not cross the TSA boundary after reaching the conventional test boundary and the RIS, confirming that the conclusions, even with adequate sample size, were not robust enough.

Figure 4.:
Trial sequential analysis (TSA) analysis for meta-analysis of miR-146a and coronary heart disease (CHD) risk in the total population under G vs. C (A), GG vs. CC (B), and CG vs. CC (C), in the Chinese population under G vs. C (D), GG vs. CC (E), and GG vs. CG + CC (F), as well as in large sample size under G vs. C (G), GG vs. CC (H), CG vs. CC (I), and GG + CG vs. CC (J). CHD = coronary heart disease, TSA = trial sequential analysis.

3.5. Results of enrichment analysis

The results of the enrichment analysis exhibited in Table 3 that miR-146a was involved in many signaling pathways, such as the receptor activator of nuclear factor Kappa B (NF-κB) signaling pathway, which was implicated in cancer, inflammatory and autoimmune diseases, septic shock, and viral infections,[28] and epidermal growth factor receptor (EGFR) signaling pathway which might participate in cell growth, proliferation and differentiation.[29] As indicated in Table 3, several diseases, especially myocardial infarction, were associated with miR-146a. Table 4 exhibited that miR-146a rs2910164 polymorphism was also related to prostate, endometrium, lung, and central nervous system cancers.

Table 3 - Enrichment analysis of miR-146a about pathway and disease.
Pathway Disease
Description P value Description P value
RANKL/RANK Signaling Pathway .0016 myocardial infarction, susceptibility to myocardial infarction 2.432E-05
AGE/RAGE pathway .0013 juvenile myelomonocytic leukemia .0003387
BDNF signaling pathway .0005 leukemia, acute myeloid 7.70E-09
TGF-beta Signaling Pathway 7E-05 mitochondrial complex I deficiency .0003843
ERK Pathway in Huntington’s Disease .0012 lung canceralveolar cell carcinoma 1.083E-05
miR-509-3p alteration of YAP1/ECM axis .001 tracheoesophageal fistula with or without esophageal atresia 4.254E-05
The effect of progerin on the involved genes in Hutchinson-Gilford Progeria Syndrome .0003 pulmonary fibrosis, idiopathic .0003387
EGF/ EGFR Signaling Pathway .0001 pheochromocytoma 7.272E-05
ErbB Signaling Pathway .0009 hypogonadotropic hypogonadism 7 with or without anosmia 2.852E-06
Estrogen signaling pathway .0005 breast cancer 1.50E-09
AGE = advanced glycation end products, BDNF = brain-derived neurotrophic factor, ECM = extracellular matrix, EGF = epidermal growth factor, EGFR/ErbB = epidermal growth factor receptor, ERK = extracellular regulated protein kinases, RANKL = receptor activator of nuclear factor Kappa B ligand, RANK = receptor activator of nuclear factor Kappa B, RAGE = receptor of advanced glycation end products, TGF = transforming growth factor, YAP = yes-associated protein.

Table 4 - Disease-related variation in miR-146a.
Name Mutation ID Position Ref/Alt Disease Region Exp. Source
hsa-mir-146a COSN20075281 chr5:160485353 C/T Prostate; carcinoma (PMID:26000489) pre-miRNA Up COSMIC
hsa-mir-146a COSN26984466 chr5:160485356 T/C Large intestine; carcinoma (PMID:27149842) pre-miRNA Up COSMIC
hsa-mir-146a COSN20075271 chr5:160485365 T/A Prostate; carcinoma (PMID:26000489) pre-miRNA Up COSMIC
hsa-mir-146a COSN20075268 chr5:160485366 C/A Prostate; carcinoma (PMID:26000489) pre-miRNA Down COSMIC
hsa-mir-146a COSN1083746 chr5:160485375 G/T Endometrium; carcinoma Seed Down COSMIC
hsa-mir-146a COSN8206750 chr5:160485375 G/T Pancreas; carcinoma Seed Down COSMIC
hsa-mir-146a COSN24408986 chr5:160485394 G/T Lung; carcinoma pre-miRNA Up COSMIC
hsa-mir-146a COSN28692950 chr5:160485411 C/G Lung; carcinoma Seed Up COSMIC
hsa-mir-146a COSN1083745 chr5:160485413 G/T Endometrium; carcinoma Seed Down COSMIC
hsa-mir-146a COSN8635147 chr5:160485429 G/C Central nervous system; glioma Mature Down COSMIC
Down = expression change decreases, miRNA = microRNA, Up = expression increases.

4. Discussion

Numerous studies have proved that miR-146a is closely related to cardiovascular diseases. For instance, the expression of miR-146a could affect the proliferation, apoptosis, and migration of vascular smooth muscle cells (VSMC), taking effect on the progress of cardiovascular disease, including atherosclerosis.[30] Besides VSMC, miR-146a was highly expressed in the peripheral blood mononuclear cell (PBMC) of patients who suffered from acute coronary syndrome.[31] Meanwhile, the miR-146a could maintain the stability of atherosclerotic plaques by inhibiting the expression of interleukin-1 receptor-associated kinase 1 (IRAK-1) and tumor necrosis factor receptor-associated factor 6 (TRAF-6) in animal models.[32] Moreover, miR-146a was also meaningfully upgraded in patients’ atherosclerotic plaques.[33]

MiR-146a precursor with C allele sequence could downgrade the level of mature miR-146a by influencing the secondary structure compared with the G allele sequence.[34] Then, reducing the miR-146a level would impact its target gene expression and interfere with some biomolecular processes. In consideration of miR-146a relating to cancer, neurological disorders, and cardiovascular diseases, we did this meta-analysis to explore the role of miR-146a on the susceptibility of CHD. In our current meta-analysis, we acquire that miR-146a rs2910164 carrying the G allele has lower CHD risk in the allelic model (OR = 0.86), homozygous model (OR = 0.79), heterozygous model (OR = 0.89) after Bonferroni correction in total population. From the subgroup analysis, the subjects containing the G allele and GG genotype are associated with a lower risk of CHD in the Chinese population, which is not observed in those carrying the GG + CG genotype and CG genotype. In the large sample size, we discover that miR-146a rs2910164 correlates with a lower risk of CHD under allelic, homozygote, heterozygous, and dominant models when the P value is Bonferroni corrected. Some previous meta-analyses were completed to explore the correlation of miR-146a rs2910164 and susceptibility to CHD. However, the result was mutually contradictory. For example, Zhou et al found an opposite conclusion compared with ours that rs2910164 polymorphism was related to higher CAD risk.[35] The influence of miR-146a rs2910164 polymorphism on disease risk was obviously nonuniform, which might be prompted by disease heterogeneity, sample size, and racial difference. For example, the frequency of the rs2910164 G allele in Europeans is 0.76868 and in Asians is 0.411, based on HapMap data (

Some risk factors, including genetic and environmental elements, inflammation, immunology, and others referring to atherosclerosis, interact to facilitate the formation of CHD.[36–38] Thus, anti-inflammation treatment might be one way to decrease the incidence and mortality of CHD. Recently, miR-146a was reported to be a potential regulator in many mechanisms referring to oxidative stress, metabolism, immunoreaction, inflammation et al, and several diseases like cerebrovascular diseases and cardiovascular diseases.[39,40] MiR-146a has multiple SNP sites involved in several signaling pathways that generate different functions in different diseases, including CHD.[41] Y. Zhu et al reported that one of the SNP sites, rs2910164, was associated with CHD.[42] We conducted the target gene prediction and enrichment analysis to explore the potential mechanism between miR-146a rs2910164 polymorphism and CHD. The results demonstrated that miR-146a might participate in epidermal growth factor receptor (EGFR), NF-κB, and transforming growth factor (TGF)-β signaling pathways, which regulated inflammation and immune response, including innate immune response. Previous studies have demonstrated that miR-146a was involved in regulating innate immune response.[43] Taking the NF-κB pathway as an example, miR-146a participated in the immune response through negative regulation of the target gene of miR-146a, IRAK1, and TRAF6.[44] Moreover, miR-146a coacted with NF-κB to take part in immune cell proliferation.[45] The miR-146a was also relevant to the pathophysiological processes of myocardial infarction and susceptibility to myocardial infarction. The promoter of the miR-146a gene had some NF-κB binding sites, which then induced the expression of interleukin-1b and tumor necrosis factor-alpha.[46] NF-κB participated in the inflammation process via IRAK-1 and tumor TRAF-6.[47] Ramkaran et al investigated that miR-146a was involved in inflammation by downregulating the expression of IRAK-1 and TRAF-6 in CAD patients.[48] They proposed that miR-146a might be a target to decelerate the inflammatory reaction in CHD. Therefore, we could speculate that rs2910164 might contribute to lower susceptibility to CHD by regulating downstream genes, specifically those involved in inflammation via the NF-κB signaling pathway. On the other side, miR-146a might be concerned with cancer development, like lung cancer, prostate cancer, and endometrial cancer.

Due to the following limitations, the results of our present meta-analysis should be discreetly interpreted. Our results of subgroup analysis based on race indicated that the differences in geographical regions and genotypic milieu might affect the consequences, meaning that genetic and environmental factors both participated in the pathophysiological process of CHD. The interactions between gene-gene and gene-environment could impact the role of the miR-146a rs2910164. When we evaluated the association between miR-146a and CHD risk based on sample size, the comparatively small number of patients might influence the conclusions. Therefore, further research with more sample size based on ethnicity, more detailed molecular mechanisms, and more clinical data, such as smoking, lifestyle, age, and sex, is needed to explore the potential function of rs2910164 in CHD patients. Fan et al investigated the correlation between Caveolin-1 polymorphism and the risk of urinary cancer through silico analysis and linkage disequilibrium analysis, which indicated how polymorphisms affected mRNA expression.[49] Some studies utilized the target gene expression between cancer tissue and matched normal tissue from several databases by silico analysis, which could better study the impact of polymorphisms on diseases.[50,51] One of the shortcomings of this study was the lack of proper research on the target gene of miR-146a. We only performed predictive analysis on miR-146a target genes. Thus, more research on the mechanism of the target gene is necessary, which is also our following research direction. Furthermore, this meta-analysis included published studies, which might lead to publication bias. And all included studies were retrospective research prone to information bias.

In conclusion, our meta-analysis indicated that miR-146a rs2910164 carrying the G allele might reduce the CHD risk. Consequently, we predicate that rs2910164 might be a potential factor that plays a protective role in the susceptibility of CHD.

Author contributions

Conceptualization: Qinxue Bao, Rui Li.

Data curation: Qinxue Bao, Chengfeng Wang, Shan Wang, Minli Cheng, Chunhua Pu, Lei Zou, Chao Liu.

Software: Minli Cheng, Chunhua Pu.

Supervision: Rui Li.

Writing – original draft: Qinxue Bao.

Writing – review & editing: Rui Li.


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