Introduction
Atherosclerosis (AS) is a chronic arterial disorder and a significant determinant of vascular death [
1]. Fatty streaks in arterial walls regularly develop into characteristic plaques and atheroma [
2]. The acute rupture of these atheromatous plaques leads to local thrombosis, causing partial or total occlusion of the affected artery [
3]. AS is featured by the progressive accumulation of lipids in the intimal space of the atrial walls, which results in several complications, such as oxidative stress, endothelial dysfunction, and chronic low-grade inflammation [
4]. AS serves as an inflammatory disease that involves the accumulation of fatty components and fibrous in the intima of medium and large arteries such as the peripheral artery, carotid artery, and coronary artery, and the clinical manifestations vary with the arteries induced [
5,
6]. AS are still the leading cause of death and loss of productive life years globally, although considerable advances in diagnosis, prevention, and therapy have been made [
7]. Moreover, due to the early symptoms of AS are not obvious or even asymptomatic, early detection and early intervention can prevent the disease from continuing to develop in a more serious direction, which is extremely critical for the treatment of the disease [
8]. Consequently, there is an increased need to identify the innovative biomarkers and predicting models for the diagnosis of AS.
Aberrantly expressed genes may be served as the potential diagnostic biomarkers of AS. For instance, an analysis of gene expression profiling identifies APH1B, JAM3, FBLN2, CSAD and PSTPIP2 as the potential diagnostic biomarkers for AS [
9]. Intercellular adhesion molecule-1 expression and serum level could serve as diagnostic markers of pre-clinical AS [
10]. Meanwhile, it has been recognized that combining various biomarkers into a single model will substantially improve the diagnostic value [
11]. Moreover, microRNAs (miRNAs) were identified as short non‐coding RNAs with a length of approximately 20‐25 nucleotides, which exert significant impacts on numerous biological processes [
12]. MiRNAs might control gene expression in the post‐transcriptional levels by pairing with target mRNAs at the 3′ untranslated region (3′ UTR) [
13]. A substantial number of investigations have revealed that miRNAs are involved in the progression of AS. For example, it has been reported that miRNA-33 modulated the macrophage autophagy in AS [
14]. MiRNA-181b regulated AS and aneurysms by controlling the expression of TIMP-3 and Elastin [
15]. However, the miRNA expression-based signatures for the diagnosis of AS were still limited.
In this study, we aimed to identify the miRNA-based diagnostic signature and construct the predicting model for the diagnosis of AS by combining bioinformatics analysis and machine learning, which will benefit the development of the early diagnosis strategy of AS.
Discussion
AS is a complex multifactorial disease that, despite advances in lifestyle management and drug therapy, remains to be the major cause of high morbidity and mortality rates from cardiovascular diseases in industrialized countries [
23,
24]. Therefore, it is urgent to seek reliable diagnostic biomarkers and effective treatment alternatives to reduce its burden [
25]. MiRNAs have received most of the attention over the last decades in particular for their role in tempering gene expression [
13]. An increasing number of studies have highlighted the importance of miRNAs in the development and progression of AS [
26]. Recently, it was shown that miRNAs exert their role in the pathophysiology of AS via the regulation of AS -prone genes as well as their impact in regulating post-transcriptional gene expression [
27]. In this study, a total of 42 miRNAs and 532 genes showed the highest association with AS in the WGCNA. Moreover, it has been identified that catabolic process, neutrophil activation, and TNF signaling are involved in the modulation of the development of AS [
28‐
30]. Our GO and KEGG pathway analysis based on the 1396 potential targeted genes of the 42 miRNAs and the identified 532 genes in the WGCNA presented multiple cellular processes, such as positive regulation of catabolic process, Renal cell carcinoma, neutrophil activation, and TNF signaling pathway. Our data were consistent with the previous study that the positive regulation of catabolic process, neutrophil activation, and TNF signaling pathway participated in the modulation of AS. More importantly, overlap analysis identified 42 overlapped genes among the 532 genes in the blue module and 1396 targeted genes of the 42 miRNAs, in which these 42 overlapped genes were targeted by 12 miRNAs. These data suggest that these 12 miRNAs are potentially critical for patients with AS.
The pathogenesis of AS is complicated, and it has been identified that miRNAs are involved in the development of AS. For example, miR-654-3p is involved in the lncRNA ZFAS1-mediated inflammation responses in AS by targeting ADAM10 and RAB22A [
31]. Meanwhile, miR-212, miRNA-216a, and miRNA-377 are considered as the potential biomarkers for the diagnosis of AS [
32,
33]. In the present study, a total of 2 miRNAs, hsa-miR-654-5p and hsa-miR-409-3p were identified and the threefold cross-validation method showed that the AUC of logistic regression model based on these 2 miRNAs was 0.7308, 0.8258, and 0.7483 with an average AUC of 0.7683. As indicated above, hsa-miR-654-3p among our identified miRNAs have been reported to associate with AS. Our data, along with the previous reports further suggest that our logistic regression model can reliably predict the diagnosis of patients with AS.
Moreover, the functional enrichment analysis results illustrated that the identified genes significantly related to fluid shear stress and atherosclerosis pathway, indicating that the results might reliable. Besides, the other two pathways screened out, response to topologically incorrect protein, and response to lipopolysaccharide, were markedly associated with genes and atherosclerosis. Based on the topological data analysis of quantitative whole-heart coronary plaque characteristics, recent research suggested that varies patients has distinct plaque dynamics and clinical outcomes [
34]. In addition, several studies revealed that microbiota could influence the atherosclerosis by regulating lipopolysaccharide production and intestinal homeostasis [
35,
36]. The above researches were consistent with our results. In the future study, we will explore the regulation mechanism of critical miRNA.
In conclusion, this study identified a total of 2 miRNAs, including hsa-miR-654-5p and hsa-miR-409-3p, are identified as the potentially critical biomarkers for AS. The logistic regression model based on the identified 2 miRNAs could reliably distinguish the AS patients from normal cases. Our finding presents new insights into the miRNA-based signatures for AS and provide valuable predictive model, benefiting the diagnosis of AS patients.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (
http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.