Academic Editor: Brian Tomlinson
Background and Aims: Epicardial adipose tissue, exosomes, and
miRNAs have important activities in atherosclerosis. The purpose of this study
was to establish miRNA expression profiles of epicardial adipose tissue-derived
exosomes in patients with coronary atherosclerosis. Methods: Biopsies of
epicardial adipose tissue were obtained from patients with and without coronary
artery disease (CAD, n = 12 and NCAD, n = 12) during elective open-heart
surgeries. Tissue was incubated with DMEM-F12 for 24 hours. Exosomes were
isolated, then nanoparticle tracking analysis, transmission electron microscopy,
and immunoblotting were performed to confirm the existence of exosomes. Total RNA
in exosomes was subjected to high-throughput sequencing to identify
differentially expressed miRNAs. MicroRNA target gene prediction was performed,
and target genes were analyzed by Gene Ontology (GO), the Kyoto Encyclopedia of
Genes and Genomes (KEGG), and mirPath to identify function. Reverse transcription
quantitative PCR was performed to confirm the differentially expressed
miRNAs. Results: Fifty-three unique miRNAs were identified
(adjusted p
Among noncommunicable diseases, cardiovascular disease is the leading cause of death worldwide; coronary artery disease contributes to most cardiovascular deaths [1, 2]. Coronary atherosclerosis, the most prominent feature of coronary artery disease, leads to lumen stenosis of coronary arteries and then myocardial ischemia [3]. Despite a great number of studies, the mechanisms of atherosclerosis are still unclear.
Epicardial adipose tissue (EAT) has emerged as a prevalent target of cardiovascular research. EAT is a visceral fat deposit between myocardium and visceral pericardium, which is mainly distributed in atrioventricular and interventricular grooves that surround the coronary arteries [4]. Although the weight of EAT varies, it can account for 20% of total heart weight in an average person [5]. More interestingly, there are no anatomical barriers between EAT and coronary arteries or myocardium because there is no fascia in between; thus, direct interaction is possible between EAT and coronary arteries or myocardium [4]. EAT is also a major source of proinflammatory cytokines such as interleukin-6, interleukin-10, and monocyte chemoattractant protein-1 as well as adipocytokines such as omentin, adiponectin, leptin, and vaspin. Thus, EAT has a significant activity in heart physiology and pathophysiology including coronary atherosclerosis [6]. In addition, EAT volume is independently associated with coronary events or major adverse cardiovascular events [7, 8]. However, still unclear are the exact mechanisms of EAT’s effects in coronary atherosclerosis.
Exosomes also have essential functions in the process of atherosclerosis. Exosomes are 30–150 nm lipid bilayer vesicles secreted by cells; exosomes contain bioactive substances such as nucleic acids, lipids, and proteins [9, 10]. Exosomes appear to function in cell-to-cell communication because they can transfer their contents between cells of different origins to participate in cellular signaling pathways [10]. Many studies provide evidence that exosomes are involved in atherosclerosis [11]. Exosomes transfer non-coding RNAs, cytokines, neutral lipids to endothelial cells, vascular smooth muscle cells, and macrophages involved in atherogenesis. Transfer of exosome materials induces apoptosis or activation or phenotypic transformation of cells, which results in atherosclerotic lesion initiation and progression [9]. It is unclear how exosomes affect cellular signal transduction during atherogenesis.
MicroRNAs (miRNAs) are crucial regulatory molecules in the pathogenesis of atherosclerosis [12]. MicroRNAs are small, single-stranded, non-coding RNAs that regulate protein synthesis by base pairing with and destabilizing their target mRNAs [12]. Parahuleva et al. [13] reported that atherosclerotic lesions and healthy arteries showed different miRNA expression profiles. Similarly, Fichtlscherer et al. [14] found different profiles of circulating miRNAs in patients with and without coronary artery disease. Lu et al. [15] reported that miRNAs influenced different cell types in contributing to atherosclerosis. Of note, according to Thomou et al. [16], adipose tissue was a main source of circulating exosomal miRNAs. Because exosomes differ between different adipose depot origins [16], and EAT shows a special transcriptomic signature [17], we anticipate that EAT has a specific exosomal miRNA profile.
The aim of this study was to profile exosomal miRNAs from EAT in patients with and without coronary artery disease and to identify candidate miRNAs for further studies on atherosclerosis. Prediction of miRNA target genes was performed by bioinformatic analysis to provide clues to signaling pathways involved in coronary atherosclerosis.
We enrolled 24 patients who had undergone elective cardiac surgery. Before the
surgery, coronary angiography was performed to confirm the status of coronary
artery disease (CAD). According to the angiography results, the patients were put
into a CAD group (n = 12) and a non-CAD (NCAD) group (n = 12). The CAD group was
defined as patients undergoing off-pump coronary artery bypass grafting for three
vessel disease, two-vessel disease with lesions at proximal left anterior
descending artery or left main disease. The NCAD group was composed of patients
who underwent open-heart surgery for mitral or aortic valve replacement or aortic
arch replacement and angiography did not show significant coronary stenosis (no
stenosis more than 50%). The key exclusion criteria were the following: age
This study complied with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University. All patients provided signed informed consent.
Clinical data including demographic data, body weight, height, medical history,
and examination were recorded on admission to the hospital and were obtained from
the records of Beijing Anzhen Hospital, Capital Medical University (Beijing,
China). BMI was calculated as weight (kg) divided by the square of height
(m
EAT biopsies (average 0.4 g) were harvested near the proximal right coronary
artery before the initiation of the cardiopulmonary bypass and were transported
to the laboratory as soon as possible. The adipose tissue biopsies were washed
twice in sterilized phosphate-buffered saline (PBS) and then cut into small
pieces (no more than 4 mm
Culture supernatants were collected and centrifuged at 800 g for 5 minutes and
then centrifuged at 3000 g for 15 minutes. The supernatants were filtered through
0.22-
The ZetaView System (Particle Matrix, Meersbusch, Germany), which uses a set of
mirrors and lenses to focus a laser on the sample chamber, was used to determine
exosome size distribution. The hydrodynamic radius of a single particle was
determined by tracking its Brownian motion. An exosomal aliquot (20
Exosomal lysates from the CAD and NCAD groups and cultured EAT were prepared by
treatment with Radio-Immunoprecipitation Assay (RIPA) buffer supplemented with a
protease inhibitor mixture. Protein concentration was determined with the BCA
Protein Assay Kit (Thermo Scientific, Waltham, MA, USA) Protein (20
According to manufacturer’s recommendations, total exosomal RNA was isolated by
the Exosomal RNA isolation kit (Cat. No: E1520-R, WeiHui Biotech, Beijing,
China), which is based on the TRIzol method for RNA isolation and consists of N1,
N2, N3 and N4 solutions, RNA precipitator and RNA elution buffer. Briefly, 250
For each sample, 20 ng of total exosomal RNA was prepared as input material to construct a cDNA library of small RNAs. We used NEBNext Multiplex Small RNA Library Prep Set for Illumina (NEB, USA) to generate the sequencing libraries following manufacturer’s recommendations. Index codes were added to the RNAs. First strand cDNA synthesis was performed after ligation of 3’ and 5’ adaptors. The cDNA was amplified by PCR, and the PCR products were purified on 8% polyacrylamide gel (100V, 80 min) to obtain DNA fragments of 140–160 bp. Then the quality of the library was assessed on the Agilent Bioanalyzer 2100 system. Finally, the library preparations were sequenced on the Illumina Novaseq (6000) SE50 platform (Illumina, USA).
Transcripts per million (TPM) values were calculated to estimate miRNA
expression. We used an R package to calculate differential expression for
transcript level [18], with the p-values adjusted by the Benjamini &
Hochberg method; adjusted p value
To confirm the results obtained by high-throughput sequencing, we performed
RT-qPCR validation of the top 10 significantly upregulated miRNAs and the top 10
downregulated miRNAs. After adding 25 fmol cel-miR39 to each exosome sample,
total RNA was extracted from the exosomes and converted to cDNA (see earlier)
with primers shown in Supplementary Table 1. RT-qPCR was performed on an
ABI 7500 Fast System (Thermo, USA). Raw quantification of each sample was
normalized to cel-39 with data calculated by the 2
In the stage of functional analysis, we selected qPCR-confirmed miRNAs to perform further analysis. MicroRNA target gene prediction was performed with the TarBase 7.0 database [19], a database of experimentally validated miRNA targets. Then, Gene Ontology (GO) enrichment and KEGG pathway enrichment of the predicted target genes were performed to investigate the functions and pathways of the target genes with miRPath V.3 platform [20].
Statistical analysis of the clinical baseline was accessed with SPSS 24.0 for
Windows (IBM, NY, USA). Continuous data were represented as the mean
Twenty-four patients who underwent elective open-heart surgery were enrolled in
this study for RNA high-throughput sequencing (HTS group), and 16 patients were
enrolled for RT-qPCR validation (qPCR group). Two patients in the RNA sequencing
group were eventually omitted because of insufficient exosomal RNA. Tables 1,2
show the participant baseline characteristics. CAD patients were more likely to
have been taking aspirin,
Characters | CAD (n = 11) | NCAD (n =11) | p Value |
Age, years | 64 |
62 |
0.267 |
Male, n (%) | 6 (54.5) | 6 (54.5) | 1.000 |
BMI, kg/m² | 24.4 |
25.3 |
0.519 |
Systolic blood pressure, mmHg | 130 |
124 |
0.117 |
Diastolic blood pressure, mmHg | 79 |
74 |
0.302 |
Hypertension, n, % | 3 (27.3) | 3 (27.3) | 0.635 |
Diabetes, n, % | 3 (27.3) | 2 (18.2) | 1.000 |
Leukocytes, ×10 |
6.3 |
6.3 |
0.980 |
Hemoglobin, g/L | 134 |
136 |
0.759 |
Fasting blood glucose, mmol/L | 5.39 (5.02, 8.57) | 5.45 (5.38, 5.72) | 0.922 |
Total cholesterol, mmol/L | 3.9 |
4.0 |
0.825 |
LDL-C, mmol/L | 2.2 |
2.4 |
0.523 |
HDL-C, mmol/L | 0.98 (0.91, 1.10) | 1.03 (0.94, 1.97) | 0.324 |
Triglycerides, mmol/L | 1.5 |
1.1 |
0.072 |
Uric acid | 306.9 (259.4, 406.2) | 359.6 (280.8 389.4) | 0.974 |
BUN, mmol/L | 5.28 (5.00, 6.38) | 4.87 (3.92, 6.38) | 0.393 |
Serum creatinine, |
73.2 (57.3, 83.2) | 72.0 (60.5, 88.4) | 0.768 |
LVEDD, mm | 47 |
50 |
0.312 |
LVEF, % | 64 |
64 |
0.364 |
Aspirin, n, % | 7 (63.6) | 0 (0.0) | 0.004 |
Clopidogrel, n, % | 4 (36.3) | 0 (0.0) | 0.090 |
10 (90.9) | 3 (27.3) | 0.008 | |
Statin, n, % | 9 (81.8) | 0 (0.0) | 0.000 |
Oral antidiabetic drugs, n, % | 2 (18.2) | 2 (18.2) | 1.000 |
CCB, n, % | 2 (18.2) | 2 (18.2) | 1.000 |
Diuretics, n, % | 3 (27.3) | 6 (54.5) | 0.387 |
ACEI/ARB, n, % | 1 (9.1) | 2 (18.2) | 1.000 |
Data are presented as mean BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; CCB, calcium channel blockers; ACEI, angiotensin converting enzyme inhibitors; ARB, angiotensin receptor blockers; HTS, high throughput sequencing. |
Characters | CAD (n = 8) | NCAD (n = 8) | p Value | p Value (HTS group vs. qPCR group) |
Age, years | 61 |
56 |
0.377 | 0.181 |
Male, n (%) | 6 (75.0) | 5 (62.5) | 1.000 | 0.376 |
BMI, kg/m² | 26.2 |
24.4 |
0.216 | 0.715 |
Systolic blood pressure, mmHg | 133 |
127 |
0.511 | 0.482 |
Diastolic blood pressure, mmHg | 81 |
76 |
0.421 | 0.712 |
Hypertension, n, % | 6 (75.0) | 4 (50.0) | 0.608 | 0.030 |
Diabetes, n, % | 3 (37.5) | 1 (12.5) | 0.569 | 1.000 |
Leukocytes, ×10 |
7.7 (5.0, 7.9) | 5.8 (4.8, 7.2) | 0.294 | 0.847 |
Hemoglobin, g/L | 142 |
143 |
0.904 | 0.063 |
Fasting blood glucose, mmol/L | 5.39 (4.67, 8.22) | 4.78 (4.52, 5.34) | 0.189 | 0.193 |
Total cholesterol, mmol/L | 3.9 |
4.2 |
0.454 | 0.786 |
LDL-C, mmol/L | 2.2 |
2.3 |
0.660 | 0.860 |
HDL-C, mmol/L | 0.98 (0.83, 1.14) | 1.27 (0.94, 1.34) | 0.093 | 0.988 |
Triglycerides, mmol/L | 1.7 |
1.1 |
0.041 | 0.856 |
Uric acid | 357.2 |
330.5 |
0.371 | 0.788 |
BUN, mmol/L | 6.34 |
4.85 |
0.112 | 0.574 |
Serum creatinine, |
86.3 |
76.2 |
0.612 | 0.522 |
LVEDD, mm | 47.5 (44.0, 48.8) | 47.0 (44.5, 49.5) | 0.873 | 0.888 |
LVEF, % | 62.5 (58.5, 65.3) | 65.0 (58.5, 65.3) | 0.490 | 0.349 |
Aspirin, n, % | 7 (87.5) | 0 (0.0) | 0.001 | 0.567 |
Clopidogrel, n, % | 6 (75.0) | 0 (0.0) | 0.007 | 0.267 |
7 (87.5) | 2 (25.0) | 0.041 | 0.861 | |
Statin, n, % | 7 (87.5) | 1 (12.5) | 0.010 | 0.578 |
Oral antidiabetic drugs, n, % | 2 (25.0) | 0 (0.0) | 0.467 | 1.000 |
CCB, n, % | 2 (25.0) | 0 (0.0) | 0.467 | 1.000 |
Diuretics, n, % | 1 (12.5) | 3 (37.5) | 0.569 | 0.307 |
ACEI/ARB, n, % | 2 (25.0) | 0 (0.0) | 0.467 | 1.000 |
Data are presented as mean BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; CCB, calcium channel blockers; ACEI, angiotensin converting enzyme inhibitors; ARB, angiotensin receptor blockers; HTS, high throughput sequencing. |
Exosomes derived from EAT were isolated by their unique size and density. Fig. 1A,B show that the exosomes derived from EAT in CAD patients and NCAD patients had a cup-like shape and were similar in size (20–150 nm). The presence of exosomal identity markers CD81, Flotillin 1, and Alix, and the absence of negative exosomal marker Calnexin, was confirmed by immunoblotting (Fig. 1C). Nanoparticle tracking analysis showed that the size of most small vesicles was 60–150 nm (Fig. 1D,E). All these verification tests confirmed that the isolated small vesicles were exosomes.
Identification of exosomes by transmission electron microscopy (TEM), immunoblotting, and nanoparticle tracking analysis. TEM showed that exosomes derived from EAT in CAD patients (A) and NCAD patients (B) had a saucer-like shape with a lipid bilayer. Arrowheads point to exosomes. Scale bar = 100 nm. (C) Immunoblot showed that exosomes from CAD and NCAD patients were positive for exosomal marker Alix, CD 81, and Flotillin 1, and negative for non-exosome markers Calnexin and GAPDH. Tissue (EAT) was used as a control for the negative markers. Concentration and size of exosomes in CAD (D) and NCAD (E) groups were analyzed by nanoparticle tracking analysis.
The raw data is available on Sequence Read Archive (SRA) platform of NCBI with access ID PRJNA698758. We found 1489 miRNAs by sequencing after low-quality reads and removal of contaminants and adaptor sequences. The clean miRNA reads of high-throughput sequencing were then compared with miRbase 20.0 to look for known miRNAs. In Supplementary Fig. 1, we list the top 30 most abundant miRNAs in EAT-derived exosomes by shinyCircos [21].
We identified exosomal miRNAs that were differentially expressed between CAD and
NCAD samples (Fig. 2). There were 53 unique miRNAs (adjusted p
Differentially expressed miRNAs in exosomes derived from EAT
from CAD and NCAD individuals. (A) Hierarchical clustering for differentially
expressed miRNAs in CAD (n = 11) versus NCAD (n = 11) (adjusted p
Fig. 3 shows the expression levels of seven confirmed miRNAs of 20 candidates. Five miRNAs were upregulated and two were downregulated in patients with CAD, a result that was consistent with sequencing results.
RT-qPCR results of seven miRNAs. * 0.01
To assess possible regulatory mechanisms of exosomal miRNA, we used Tarbase 7.0
to select target genes of the confirmed miRNAs that were differentially expressed
between the CAD and NCAD patients. The results were used for functional analysis
with the GO and KEGG databases and pathway analysis by miRPath. The detailed
results are presented in Figure. By GO analysis (Fig. 4A–C), we investigated
biological processes (BP), molecular functions (MF) and cellular components (CC).
KEGG analysis (Fig. 4D) indicated that significantly enriched pathways were axon
guidance, pathways in cancer, PI3K-Akt signaling, and the FoxO signaling pathway
(FDR
GO (A–C) and KEGG (D) analysis. (A) Biological processes (BP), (B) molecular functions (MF), (C) cellular components (CC). The x-axis displays enriched GO biological process terms or KEGG pathways, and the y-axis refers to miRNA targets. The color of each square represents the significance of enrichment. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Because of its unique anatomical features and active biological characteristics, EAT shows a close relationship with coronary atherosclerosis. Exosomes have been considered relevant in coronary artery disease, and exosomes derived from visceral adipose tissue showed a proatherogenic effect [22]. Our study appears to be the first to describe microRNA expression profiles of EAT-derived exosomes in CAD. We identified 53 differently expressed miRNAs, 21 that were downregulated, and the remainder upregulated. Seven miRNAs were confirmed in qPCR validation.
Many studies confirmed that exosomes from EAT could target vessel cells. Xie and colleagues [22] and our team [23] both confirmed intake of adipose-derived exosomes in macrophages. Briefly, Xie et al. [22] found that visceral adipose-derived exosomes from high-fat diet-induced obese mice promoted macrophage polarization and foam cell formation. We showed previously that perivascular adipose tissue-derived exosomes could reduce macrophage foam cell formation by regulating expression of cholesterol transporters. Shaihov-Teper et al. [24] showed that extracellular vesicles from EAT targeted endothelial cells and facilitated angiogenesis.
In this study, miR-200 family members were the most differentially expressed miRNAs among the upregulated miRNAs, especially miR-141-3p, miR-200a-5p and miR-429 validated by qPCR. The miR-200 family, miR-200a, miR-200b, miR200c, miR-141, and miR-429, exerts a pro-inflammatory function in the process of atherosclerosis [25]. The miR-200 family can be upregulated during oxidative stress of endothelial cells [26], and Zhang et al. [27] reported that increased miR-429 may target Bcl-2 and induce endothelial cell apoptosis, which can be associated with atherosclerosis. In mice with type 2 diabetes, miR-429, miR-200b and miR-200c expression levels were elevated in vascular smooth muscle cells (VSMCs), and miR-200 mimics promoted monocyte-binding of VSMCs, which was reversed by miR-200 inhibitors [25]. In addition, Gong et al. [28] reported that miR-141-3p/miR-200a-3p might accelerate atherosclerosis by targeting Coiled-Coil Domain Containing 80 (CCDC80) in VSMCs. Another validated upregulated exosomal miRNA in our study was miR-205-5p. Meng et al. [29] found that miR-205-5p promoted unstable atherogenesis and suppressed cholesterol efflux, which could result in susceptibility to free cholesterol-triggered macrophage apoptosis. Son et al. [30] reported that murine-specific miR-712 could promote endothelial inflammation and accelerate the process of atherosclerosis; interestingly, miR-205 shares the most sequence with miR-712 and might be a homologue of miR-712.
In our study, miR-485-3p and miR-382-5p were confirmed to be less prevalent in
EAT-derived exosomes from CAD patients. MiR-485-5p was found to decrease the
expression of target gene MMP14 to inhibit epithelial-mesenchymal transition
(EMT) [31], and, interestingly, EMT commonly occurs in atherosclerotic plaques
and may induce plaque instability [32]. Hu et al. [33] reported that
overexpression of miR-382-5p inhibited nuclear factor IA expression and
proinflammatory cytokines levels including IL-6, IL-1
In GO analysis, focal adhesion was one of the significant terms among predicted target genes of selected miRNAs. This finding suggested that focal adhesion has a vital function in atherosclerosis. Focal adhesion is the adhering junction between cell and extracellular matrix (ECM), providing communication between cytoskeleton and ECM and making possible interactions between vascular wall and extracellular environment [34]. Tsai et al. [35] showed that deficiency of Galectin-1 attenuated FA formation by VSMCs, disclosing that Galectin-1 promotes focal adhesion turnover which results in restriction of the motility of VSMCs that suppresses neointimal formation after vascular injuries. Focal adhesion kinase (FAK) is a key constituent of focal adhesion [34], and inhibition of FAK catalytic activity induces its nuclear enrichment which blocks neointimal hyperplasia and VMSC proliferation by modulating the GATA-binding protein 4-cyclin D1 signal pathway in vascular injury [36].
In KEGG analysis, the PI3K/Akt signaling pathway was a significant KEGG pathway of predicted target genes of selected miRNAs. The PI3K/Akt pathway is common in atherogenesis because many signals are transduced by this pathway in atherosclerosis. The PI3K/Akt pathway has an important function in macrophage proliferation, survival migration and polarization that might impact atherosclerosis [37]. The PI3K/Akt pathway was also detected in endothelial cell inflammation and injury [38] and could induce VSMC foam cell formation and lipid accumulation [39], which indicated the widespread effects of the PI3K/Akt signaling pathway on pathology of atherosclerosis.
FoxO (the O subfamily of forkhead) signaling pathway was another enriched KEGG
pathway of predicted target genes. The FoxO family, FoxO1, 3, 4, and 6, is
involved in homeostasis in endothelial cells [40]. FoxO1 is coupled with
metabolic status and survival of cells in the cardiovascular system [41], and its
regulation is of vital importance because an imbalance can be detrimental [41].
Wilhelm et al. [42] reported that FoxO1 inhibited Myc, resulting in
quiescence of endothelial cells, which might support endothelial survival.
However, in vascular endothelial cells of FoxO knock-out mice, inflammatory
cytokines IL-1
Exosomes can be a source of miRNAs; the differentially expressed miRNA in our study have already been found to express in extracellular vesicles (EVs). However, this could not indicate that these findings were useless. We know that the exosomes or EVs in serum and other body fluids are from all types of tissues and cells, with most exosomes stemming from adipose tissue. But different tissues can release different exosomes, and even adipose tissue from different positions can release different exosomes; thus, we cannot tell the origin of exosomes without specific research [16]. Exosomal communication is more likely to take occur by paracrine regulation, whereas circulatory exosomes do not have this potential. Because many miRNAs were found to take part in atherosclerosis in many cell types, such as macrophages, smooth muscle cells, and endothelial cells, it might be helpful to find the miRNAs’ origins because they might be targets for cures of atherosclerosis and markers for disease.
This study had some limitations. First, we could not select completely healthy controls because of ethical and practical issues; thus, we might not have ruled out all the confounding factors. Second, the enrolled patients were Chinese, and our findings may or may not extend to other ethnicities. Third, the sequencing results were too numerous to validate each one by RT-qPCR. Fourth, because the enriched terms and pathways were analyzed by bioinformatics, we could not provide direct detailed pathways in atherosclerosis; additional experimental testing should be performed.
We obtained exosomes from EAT in CAD patients and NCAD controls and used high-throughput sequencing to acquire differential expression profiles for miRNAs. CAD patients showed different EAT exosomal miRNA expression profiles compared with NCAD patients. GO and KEGG analysis of predicted miRNA target genes was performed. The results provided clues for further studies of exosomal mechanisms of atherosclerosis.
YXZ, JXL and CPH designed the study. JXL, YS, DZ performed the tissue culture. YLiu, JXL, YD, XZL and YS performed the isolation of exosomes and exosomal RNAs. YS and YD performed sequencing. JXL performed bioinformatic analysis and wrote this manuscript. HYH, TSL, JMZ, RD, YJZ and YXZ supervised the study. AG, YZ, YLi, SJX and CHZ contributed to statistics and method review. All the Authors contributed equally to collection of clinical data and all the Authors read and approved this manuscript.
The Ethics Committee of Beijing Anzhen Hospital, Capital Medical University approved this study (No. 2020092X). All patients provided signed informed consent.
We appreciate the help of Tiandi Wei and Jing Gong from Shandong University for their assistance in our consult of analysis. The authors also thank all the peer reviewers for their opinions and suggestions.
This study was funded by the grant from National Key Research and Development Program of China (2017YFC0908800), Beijing Municipal Science and Technology Commission (NO. Z171100000417042), Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20150601) and Mission plan (SML20180601), Beijing Municipal Health Commission “Project of Science and Technology Innovation Center” (PXM2019_026272_000006) (PXM2019_026272_000005).
The authors declare no conflict of interest.
The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.