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. 2021 Jul 8;11:14141. doi: 10.1038/s41598-021-93246-0

Exosomal hsa_circRNA_104484 and hsa_circRNA_104670 may serve as potential novel biomarkers and therapeutic targets for sepsis

Chang Tian 1, Jiaying Liu 1, Xin Di 1, Shan Cong 1, Min Zhao 1, Ke Wang 1,✉
PMCID: PMC8266806  PMID: 34238972

Abstract

In order to explore the role of exosomal circRNAs in the occurrence and development of sepsis, we looked for potential diagnostic markers to accurately identify sepsis and to lay a molecular basis for precise treatment. Ultracentrifugation was used to extract exosomes from the serum of patients with sepsis and healthy individuals. Then, changes in circRNA expression in exosomes were studied by circRNA microarray analysis. Gene ontology (GO) analysis and Kyoto City Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were used to annotate the biological functions and pathways of genes, and a circRNA-miRNA-mRNA regulatory network was constructed. In the microarray analysis, 132 circRNAs were significantly differentially expressed, including 80 and 52 that were upregulated and downregulated, respectively. RT-qPCR verified the results of microarray analysis: hsa_circRNA_104484 and hsa_circRNA_104670 were upregulated in sepsis serum exosomes. ROC analysis showed that hsa_circRNA_104484 and hsa_circRNA_104670 in serum exosomes have the potential to be used as diagnostic markers for sepsis. The circRNA-miRNA-mRNA network predicted the potential regulatory pathways of differentially expressed circRNAs. There are differences in the expression of circRNA in serum exosomes between patients with sepsis and healthy individuals, which may be involved in the occurrence and development of the disease. Among them, elevations in hsa_circRNA_104484 and hsa_circRNA_104670 could be used as novel diagnostic biomarkers and molecular therapeutic targets.

Subject terms: Biomarkers, Medical research, Pathogenesis

Introduction

Sepsis is defined as life-threatening organ dysfunction and is not a specific disease, but rather a syndrome of physiological, pathological, and biochemical abnormalities caused by the host's unregulated response to infection1. Sepsis is a heterogeneous disease state that progresses rapidly, and its early diagnosis and intervention can significantly improve prognosis2. Our diagnosis of sepsis mainly relies on Sequential Organ Failure Assessment (SOFA) scoring system, which has certain limitations; currently, there is no ‘gold standard’ for laboratory diagnosis. With the development of high-throughput sequencing technology, genomics and metabolomics analyses have found that the levels of various genes and metabolites in sepsis have changed, and that the changes occur earlier than clinical symptoms3,4. Identifying these molecular changes in sepsis is highly valuable for understanding the course of the disease, and for predicting prognosis and response to treatment. Exploring the changes in sepsis at cellular and molecular levels is helpful to explore the nature of its pathogenesis and may help to identify the causes of heterogeneity in the body's response5. Individualised therapy targeting the core molecules of the disease can improve the efficiency of the treatment and reduce toxicity. Therefore, these differentially expressed molecules may serve as diagnostic markers for sepsis and may become targets for molecular targeted therapy.

Exosomes are small extracellular vesicles derived from the endosomal system, ranging from 40 to 160 nm (about 100 nm on average) in diameter6. In sepsis, exosomes are secreted by a variety of cells (including mesenchymal stem cells and macrophages, among others), and act on recipient cells (e.g., cardiomyocytes, macrophages, vascular endothelial cells) to promote inflammation, inhibit inflammation, or regulate immunity7–9. Their contents are rich and diverse, containing a variety of proteins, DNA, RNA (e.g., mRNA, miRNA, lncRNA, circRNA), amino acids, and metabolites6. The uptake of cytoplasmic components during exosomal biogenesis is not random, but is a highly regulated and selective process, which is very important for disease identification and diagnosis10. The cell-free RNA in the blood is easily inactivated by endogenous RNase, while RNA encapsulated in exosomes can be prevented from degradation by RNase and can exist stably11. In addition, the exosomes released to the outside of cells exist in a variety of body fluids and are easy to separate and extract11,12. These characteristics give exosomes diagnostic and therapeutic potential.

CircRNA is a large class of non-coding RNAs produced by reverse splicing events13. CircRNAs are produced in the nucleus and are then transported to the cytoplasm. They have the characteristics of tissue specificity, cell specificity, high stability, and species conservation14. Some can be distributed to exosomes, where they are enriched and stably exist15,16. In disease states, the expression level of exosome circRNA changes, and it plays a regulatory role in cell proliferation, tumour metastasis, and drug resistance, among other processes17. CircRNAs are involved in the occurrence and progression of various diseases through multiple mechanisms. For example, circRNAs act as miRNA sponges to regulate gene expression and participate in the occurrence and development of tumours13; they also act as a protein sponge to mediate the immune response during viral infection18.

Numerous studies have shown that the expression of exosomal circRNAs is different between patients and healthy people, and its detection can help to identify patients. Therefore, exosomal circRNAs may be used as novel disease diagnostic markers19. To date, there have been no reports on the expression or role of exosomal circRNAs in sepsis. This study aimed to detect circRNAs in serum exosomes of patients with sepsis and to explore their value in the diagnosis of sepsis and in molecular targeted therapy.

Materials and methods

Patient samples and ethics statement

In this study, a total of 25 patients with sepsis who underwent treatment at the Second Hospital of Jilin University from September 2018 to January 2019 were included, in addition to 22 healthy individuals. Sepsis was defined according to the Sepsis-3 criteria1. All study participant’s peripheral blood samples (4–5 mL) were collected in the early phase (within 24 h) of the diagnosis of sepsis and centrifuged at 3000 rpm for 10 min to obtain the serum, which was stored at − 80 °C after being labelled. The patients’ clinical and laboratory data are shown in Table 1. This study was approved by the Ethics Committee of the Second Hospital of Jilin University. All experiments were performed in accordance with relevant named guidelines and regulations. All participants signed an informed consent form.

Table 1.

Demographic characteristics of septic patients.

Characteristics Septic patients (N = 22)
Sex
Male, n (%) 16 (73)
Female, n (%) 6 (27)
Age, years 56.73 ± 16.12
Mortality, n (%) 8 (36)
Comorbidities
Hypertension, n (%) 10 (45)
Diabetes, n (%) 7 (32)
Source of sepsis
Abdominal, n (%) 3 (14)
Lung, n (%) 19 (86)
Mean arterial pressure, mmHg 91.509 ± 10.6399
PaO2/FiO2 (mmHg) 200.535 ± 78.7067
Use of mechanical ventilation, n (%) 5 (23)
Hematologic and inflammatory data
Leukocyte, 109/L 11.20 (8.75–14.05)
Neutrophils, 109/L 9.60 (6.60–11.59)
Hemoglobin, g/dL 115.091 ± 21.5338
Platelets, 109/L 128.282 ± 82.8287
Procalcitonin, ng/mL 7.69 (2.20–24.31)
SOFA score 6.273 ± 2.9469
Positive blood culture 5 (23)
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Data are expressed as number (%), mean ± SD, or median (25th–75th percentile).

Exosome collection

We used ultracentrifugation to extract exosomes from the serum, and the whole process was completed at 4 °C. First, the serum was centrifuged at 2000 × g for 30 min to remove dead cells and was then centrifuged at 10,000 × g for 30 min to remove cell debris and impurities. Then, the exosomes were preliminarily precipitated by centrifugation at 110,000 × g for 80 min. Phosphate buffer saline (PBS) solution was added to wash the soluble protein impurities, and then the sample was centrifuged again at 110,000 × g for 80 min to obtain pure exosomes. Finally, the pellet was resuspended in PBS solution (100μL PBS solution per 1 mL of serum) and was stored in a − 80 °C freezer.

Western blotting analysis

Exosomal marker proteins were detected by immunoblotting. Protein was extracted from the same volume of exosomes, and the protein concentration of exosomes was quantified using the BCA method (Beyotime, China). Then, 20 μg of exosomal protein was separated by electrophoresis on a 12% SDS-PAGE gel and was then transferred to a PVDF membrane (Millipore, USA). Immunoblotting was performed with anti-CD63 and anti-TSG101 antibodies (Affinity, USA) at 4 °C. The primary antibodies were then detected with a horseradish peroxidase-conjugated secondary antibody (#SA00001-1 or #SA00001-2; Proteintech Group, USA). Finally, the ECL chemiluminescence agent (Thermo Fisher Scientific, USA) was used to display protein bands, and the results were recorded with photos.

Electron microscopy

For electron microscopy, 5 μl of the exosome suspension was spotted on copper mesh and dried at room temperature. The sample was then negatively stained with 5 μl of 2% (w/v) phosphotungstic acid solution. The morphology of exosomes was observed at 80 kV under a transmission electron microscope (JEM-1400, JEOL, Japan), and the results were photographed.

RNA extraction and quality control

Total RNA was extracted from the exosome suspension using the TRI Reagent BD (Molecular Research Center, Inc., USA) according to the manufacturer’s protocol. The total RNA from each exosome sample was quantified and its purity was evaluated using a NanoDrop 2000 ultra-micro spectrophotometer (Thermo Fisher Scientific, USA).

circRNA microarray analysis

CircRNA microarray analysis was performed on serum exosomes from three people with sepsis and three healthy persons. According to the manufacturer's protocol (Arraystar Inc., USA), sample labelling and microarray hybridization were performed. First, RNA was fluorescently labelled. Rnase R reagent (Epicenter, Inc., USA) was used to digest total RNA to remove linear RNA and enrich circRNAs. The enriched circRNAs were then transcribed into fluorescently labelled cRNA using a random priming method (Arraystar Super RNA Labelling Kit; Arraystar, USA). The labelled cRNAs were purified using the RNeasy Mini Kit (Qiagen, Germany). Microarray hybridisation was then performed in an Agilent Hybridisation oven. The fluorescently labelled cRNAs were cleaved into fragments and were then hybridised on the circRNA expression microarray slide. After hybridisation was completed, the hybridised microarrays were washed, fixed, and scanned using the Agilent Scanner G2505C. Agilent Feature Extraction software was used to extract raw data from the scanned images. Quantile normalisation of raw data was performed using the limma package (version 3.48.0)20 in R, and the circRNAs labelled by the software were retained for subsequent difference analysis. A t-test was used to estimate the statistical significance of the difference. Fold changes and p-values were used to screen for significant differences in the expression of circRNAs between the two groups of samples. Volcano plots and heat maps were used to display differentially expressed circRNAs.

real-time quantitative PCR (RT-qPCR) analysis

Total RNA was extracted from serum exosomes of 25 sepsis patients and 22 controls. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the experiment. The sequences of the primers used in the experiment are shown in Table 2. Total RNA was reverse transcribed into complementary DNA (cDNA) using a PrimeScript RT reagent kit (Takara, Japan) according to the manufacturer’s protocol. Real-time quantitative PCR reactions were then carried out with a real-time PCR system (LightCycler480, Roche, Switzerland) using TB Green Premix Ex Taq II (Takara, Japan). The PCR conditions were 95 °C for 30 s, followed by 40 cycles at 95 °C for 10 s, and 60 °C for 60 s. β-actin was used as a reference gene, and all qPCR reactions were repeated three times. The 2-△△CT value reflects the relative expression level of circRNAs.

Table 2.

Primers designed for qRT-PCR analysis of circRNAs.

Target ID Primer sequence, 5’–3’ Tm (°C) Product size in bp
β-actin (human) F:5' GTGGCCGAGGACTTTGATTG3' 60 73
R:5' CCTGTAACAACGCATCTCATATT3’
hsa_circRNA_104484 F:5’ TGTATTCTCTCTGTGTGTGGCTG 3’ 60 134
R:5’ GCAACATCCCAAATCGGTCT 3’
hsa_circRNA_104670 F:5’ CGCAGAAGCGTTGTCACTG 3’ 60 110
R:5’ CTTCCCCGTGTTCTTCCTGTT 3’
hsa_circRNA_101491 F:5’ AGGCTTTTGGACAAGTGGGTG 3’ 60 83
R:5’TGAGGATGTGGTGCTGTTTGTG3’
hsa_circRNA_406194 F:5’ ACAATGATGAGGCCTTAGAAGC 3’ 60 58
R:5’ CGATGGCATTCACCCTCTT 3’
hsa_circRNA_103864 F:5’ GGATGTATGGTGTAGGTGTGGA 3’ 60 90
R:5’CAAGACTATTATCCTTTATTATAACCC3’
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Functional analysis

Arraystar microRNA prediction software was used to predict miRNAs downstream of differentially expressed circRNAs. Then, the interactions between circRNA-microRNAs are explained in detail. TargetScan (http://www.targetscan.org/vert_71/), miRDB (http://www.mirdb.org/), and miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) were used to predict the potential targets of miRNAs. The common genes in the three databases were collected using Venn diagrams. The circRNA–miRNA–mRNA regulatory map was visualised using Cytoscape 3.8.0. Gene ontology (GO) analysis was used to annotate the biological functions of genes in the ceRNA network, including molecular functions (MF), biological pathways (BP), and cellular components (CC). Kyoto City Encyclopaedia of Genes and Genomes (KEGG) Enrichment Analysis was used to evaluate the biological pathways of genes21. The enrichment of MF, BP, CC, and pathways of genes were annotated with DAVID 6.8 (https://david.ncifcrf.gov/) which is an online biological tool.

Statistical analysis

SPSS software (version 23.0, IBM, Chicago, IL, USA) was used for statistical analysis. If the data of continuous variables were distributed normally, the data were analysed using t-tests; results are expressed as the mean ± standard deviation. If data were non-normal, the Mann–Whitney U test was used, and the data are expressed in percentile form. Data of categorical variables between groups were tested using the Chi-square test. A p value of < 0.05 means that the difference is statistically significant. The receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic ability of exosomal circRNAs for sepsis. The area under the ROC curve (AUC) was used to evaluate the diagnostic efficacy of circRNA. The Youden Index was used to determine the optimal cut-off value, sensitivity and specificity (Youden Index = Sensitivity + Specificity-1). The highest Youden index corresponds to the optimal cut-off value, sensitivity and specificity.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the second hospital of Jilin University. All participants were informed and willing to sign informed consent.

Consent for publication

All the authors read and consented to the publication of the manuscript.

Results

Characterization of circulating serum exosomes

The serum exosome was confirmed by transmission electron microscopy (TEM) and WB for CD63 or TSG101 (Fig. 1a). The exosomes are round or oval ‘cup-shaped’, with a diameter in the range of 40–160 nm. CD63 and TSG101 showed positive expression in WB (Fig. 1b).

Figure 1.

Figure 1

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(a) Electron micrographs and (b) WB results of serum exosomes.

Identification of differentially expressed circRNAs

We used circRNA microarray technology to detect changes in the circRNA expression profile of serum exosomes in sepsis. After scanning the fluorescent signal of circRNA microarray hybridisation, a total of six scanning pictures of the sepsis and control groups were obtained (Fig. 2a). The box plot shows the results of the quality control analysis of the microarray data (Fig. 2b). Volcano plots and scatter plots were used to visually show the differences in circRNA expression between the two groups. In the volcano map (Fig. 2c), the vertical lines represent 1.5 times up and down, and the horizontal lines represent p ≤ 0.05. Red dots indicate circRNAs that are significantly differently expressed, and grey dots indicate circRNAs that are not significantly differently expressed. In the scatter plot (Fig. 2d), the X-axis and Y-axis represent the normalised signal values of the two groups of samples, respectively, and the green line is the fold line. Plots distributed above the upper green line and below the lower green line represent significantly differently expressed circRNAs.

Figure 2.

Figure 2

Figure 2

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(a) The probe fluorescence signal displayed in each microarray scanning picture was uniform and clear. (A, B, C: sepsis group, a, b, c: control group). (b) Box plot: The abscissa represents each sample, and the ordinate represents the normalized intensity value. The expression of circRNAs in each sample was almost the same after normalization. (c) Volcano map: Differentially expressed circRNAs between sepsis and healthy human serum exosomes. (d) Scatter plot: Changes of circRNAs expression levels between sepsis and healthy human serum exosomes. (e) Cluster analysis: the distinguishable circRNA expression profile between sepsis and healthy human serum exosomes. The quantile normalisation and difference analysis were performed using limma package (version 3.48.0) in R. The Volcano map and Scatter plot were performed using python (version 2.7). Cluster analysis was performed using gplots package (version 3.1.1) in R.

A total of 13228 circRNAs were detected by circRNA microarray analysis, of which 6247 were upregulated and 6981 were downregulated. Among them, 132 circRNAs were differentially expressed (p < 0.05, fold change > 1.5), including 80 upregulated and 52 downregulated circRNAs. Specific details are shown in Tables 3 and 4. Then, cluster analysis was performed on the significantly differentially expressed circRNAs to visually display the differentially expressed circRNAs and to test their rationality and accuracy. As shown in the heat map (Fig. 2e), red represents highly expressed circRNAs and green represents low-expressed circRNAs. The results showed distinguishable circRNA expression profiles between the two groups of samples.

Table 3.

Differentially up-regulated circRNAs in serum exosomes of patients with sepsis.

circRNA Alias P-value FDR FC (abs) chrom circRNA_type best_transcript GeneSymbol
hsa_circRNA_066869 hsa_circ_0066869 0.022756307 0.431741635 1.5009586 chr3 Sense overlapping NM_018266 TMEM39A
hsa_circRNA_405661 0.039569341 0.431741635 1.6109265 chr18 Sense overlapping NR_033354 ZNF519
hsa_circRNA_001264 hsa_circ_0000086 0.017699179 0.431741635 1.5018914 chr1 Antisense NM_152996 ST6GALNAC3
hsa_circRNA_104400 hsa_circ_0006944 0.043350982 0.431741635 1.7182999 chr7 Exonic NM_001518 GTF2I
hsa_circRNA_101167 hsa_circ_0005916 0.024804956 0.431741635 1.9019977 chr12 Exonic NM_012174 FBXW8
hsa_circRNA_407041 0.049595412 0.431741635 1.6179568 chr8 Sense overlapping ENST00000518026 MSR1
hsa_circRNA_014551 hsa_circ_0014551 0.030830215 0.431741635 1.6101319 chr1 Exonic NM_018489 ASH1L
hsa_circRNA_407148 0.024995712 0.431741635 1.839682 chr9 Intergenic
hsa_circRNA_003101 hsa_circ_0003101 0.042393826 0.431741635 1.6219639 chr3 Exonic NM_173471 SLC25A26
hsa_circRNA_033572 hsa_circ_0033572 0.007038362 0.431741635 1.8332939 chr14 Exonic NM_138420 AHNAK2
hsa_circRNA_103389 hsa_circ_0001309 0.026886598 0.431741635 1.7955397 chr3 Exonic NM_003157 NEK4
hsa_circRNA_401068 0.049692498 0.431741635 1.5372069 chr12 Exonic NM_032814 RNFT2
hsa_circRNA_081594 hsa_circ_0081594 0.033763521 0.431741635 1.5187091 chr7 Exonic NM_016068 FIS1
hsa_circRNA_104030 hsa_circ_0001564 0.026931184 0.431741635 1.5017159 chr5 Exonic NM_001746 CANX
hsa_circRNA_104283 hsa_circ_0001667 0.027324991 0.431741635 1.7455824 chr7 Exonic NM_017802 DNAAF5
hsa_circRNA_021708 hsa_circ_0021708 0.035339271 0.431741635 1.5242451 chr11 Exonic NM_003477 PDHX
hsa_circRNA_103749 hsa_circ_0005480 0.041431749 0.431741635 1.5968689 chr4 Exonic NR_036614 DCLK2
hsa_circRNA_008026 hsa_circ_0008026 0.025086019 0.431741635 1.5726361 chr4 Exonic NM_001221 CAMK2D
hsa_circRNA_101205 hsa_circ_0006078 0.048933628 0.431741635 1.7621779 chr12 Exonic NM_023928 AACS
hsa_circRNA_007507 hsa_circ_0007507 0.023237468 0.431741635 1.8572626 chr5 Exonic NM_002890 RASA1
hsa_circRNA_103456 hsa_circ_0067127 0.027006569 0.431741635 1.6842385 chr3 Exonic NM_012190 ALDH1L1
hsa_circRNA_031720 hsa_circ_0031720 0.04767514 0.431741635 1.5353758 chr14 Exonic NM_006364 SEC23A
hsa_circRNA_075166 hsa_circ_0075166 0.025125707 0.431741635 1.5415749 chr5 Exonic NM_022455 NSD1
hsa_circRNA_001781 hsa_circ_0001781 0.048181011 0.431741635 1.9555457 chr8 Intronic ENST00000517494 CSGALNACT1
hsa_circRNA_101969 hsa_circ_0041821 0.011283402 0.431741635 1.6567011 chr17 Exonic NM_032442 NEURL4
hsa_circRNA_000947 hsa_circ_0000947 0.026960269 0.431741635 2.5782047 chr19 Sense overlapping NM_031485 GRWD1
hsa_circRNA_405717 0.036722435 0.431741635 2.010568 chr19 Intronic ENST00000301281 UBXN6
hsa_circRNA_002292 hsa_circ_0002292 0.047693181 0.431741635 1.6544175 chr5 Exonic NM_153013 NADK2
hsa_circRNA_101704 hsa_circ_0037858 0.045400879 0.431741635 2.1431944 chr16 Exonic NM_004862 LITAF
hsa_circRNA_001063 hsa_circ_0001063 0.042758831 0.431741635 2.315292 chr2 Intergenic
hsa_circRNA_102509 hsa_circ_0006446 0.034684944 0.431741635 2.2800739 chr19 Exonic NM_015578 LSM14A
hsa_circRNA_406583 0.045804491 0.431741635 1.6819656 chr5 Sense overlapping NM_018140 CEP72
hsa_circRNA_102062 hsa_circ_0007990 0.023322108 0.431741635 1.5649695 chr17 Exonic NM_033419 PGAP3
hsa_circRNA_405781 0.031827149 0.431741635 1.7484564 chr19 Intronic ENST00000221419 HNRNPL
hsa_circRNA_000746 hsa_circ_0000746 0.001572925 0.431741635 2.0290976 chr17 Antisense NM_004475 FLOT2
hsa_circRNA_000435 hsa_circ_0000435 0.022928053 0.431741635 1.5484743 chr12 Intronic ENST00000549893 C12orf75
hsa_circRNA_001714 hsa_circ_0001714 0.010198598 0.431741635 5.0265939 chr7 Exonic NM_032408 BAZ1B
hsa_circRNA_040206 hsa_circ_0040206 0.036836602 0.431741635 1.5041225 chr16 Exonic NM_007242 DDX19B
hsa_circRNA_001226 hsa_circ_0001226 0.002126463 0.431741635 2.3072386 chr22 Antisense NM_002473 MYH9
hsa_circRNA_000134 hsa_circ_0000134 0.049036785 0.431741635 1.7256715 chr1 Antisense NM_000565 IL6R
hsa_circRNA_087800 hsa_circ_0087800 0.043563969 0.431741635 1.6403757 chr9 Exonic NM_018376 NIPSNAP3B
hsa_circRNA_400101 hsa_circ_0092328 0.037702213 0.431741635 1.8812764 chr9 Intronic ENST00000315731 RPL7A
hsa_circRNA_001308 hsa_circ_0001308 0.013850614 0.431741635 3.3527247 chr3 Exonic NM_003157 NEK4
hsa_circRNA_100659 hsa_circ_0003168 0.049793865 0.431741635 1.5291681 chr10 Exonic NM_144588 ZFYVE27
hsa_circRNA_404449 0.023726017 0.431741635 1.8863782 chr1 Exonic NM_032409 PINK1
hsa_circRNA_102774 hsa_circ_0055412 0.044551823 0.431741635 1.5443449 chr2 Exonic NM_001747 CAPG
hsa_circRNA_102446 hsa_circ_0049356 0.017117814 0.431741635 1.8012178 chr19 Exonic NM_199141 CARM1
hsa_circRNA_403556 0.00783705 0.431741635 2.0363025 chr6 Exonic uc010jpp.1 LINC00340
hsa_circRNA_000230 hsa_circ_0000765 0.019997256 0.431741635 1.7827514 chr17 Intronic ENST00000225916 KAT2A
hsa_circRNA_007326 hsa_circ_0007326 0.046543498 0.431741635 1.9909955 chr14 Exonic NM_014169 CHMP4A
hsa_circRNA_404807 0.02819908 0.431741635 2.5888983 chr10 Exonic NM_020682 AS3MT
hsa_circRNA_001389 hsa_circ_0000729 0.027885902 0.431741635 1.5995622 chr16 Intronic ENST00000268699 GAS8
hsa_circRNA_404818 0.048809072 0.431741635 2.0947754 chr10 Exonic NM_000274 OAT
hsa_circRNA_001547 hsa_circ_0001874 0.034742413 0.431741635 2.1924449 chr9 Intronic ENST00000356884 BICD2
hsa_circRNA_001241 hsa_circ_0000508 0.029378216 0.431741635 2.0517604 chr13 Intronic ENST00000326335 CUL4A
hsa_circRNA_104671 hsa_circ_0001819 0.043208655 0.431741635 1.8112929 chr8 Exonic NM_015902 UBR5
hsa_circRNA_102442 hsa_circ_0049271 0.044592332 0.431741635 2.611047 chr19 Exonic NM_012289 KEAP1
hsa_circRNA_003907 hsa_circ_0003907 0.038311645 0.431741635 1.833842 chr13 Intronic ENST00000319562 FARP1
hsa_circRNA_038516 hsa_circ_0038516 0.039811555 0.431741635 1.7176617 chr16 Exonic NM_018119 POLR3E
hsa_circRNA_405872 0.031980564 0.431741635 1.6275643 chr2 Exonic uc002ruu.3 PRKCE
hsa_circRNA_101458 hsa_circ_0034044 0.021127405 0.431741635 1.7423746 chr15 Exonic uc001ytg.3 HERC2P3
hsa_circRNA_405443 0.003224918 0.431741635 2.1653199 chr16 Intronic ENST00000342673 NDE1
hsa_circRNA_004077 hsa_circ_0004077 0.037688065 0.431741635 4.1270503 chr16 Exonic NM_020927 VAT1L
hsa_circRNA_103852 hsa_circ_0072665 0.013650168 0.431741635 2.2677625 chr5 Exonic NM_197941 ADAMTS6
hsa_circRNA_023461 hsa_circ_0023461 0.000918303 0.431741635 2.3023746 chr11 Exonic NM_015242 ARAP1
hsa_circRNA_103864 hsa_circ_0005730 0.027626518 0.431741635 2.7818978 chr5 Exonic NM_001799 CDK7
hsa_circRNA_001653 hsa_circ_0001568 0.016902603 0.431741635 6.1554028 chr6 Intronic ENST00000344450 DUSP22
hsa_circRNA_001405 hsa_circ_0001167 0.042757718 0.431741635 2.7907614 chr20 Intronic ENST00000371941 PREX1
hsa_circRNA_043943 hsa_circ_0043943 0.017629978 0.431741635 1.9805323 chr17 Exonic uc010cyw.1 VAT1
hsa_circRNA_045799 hsa_circ_0045799 0.027973896 0.431741635 1.7012317 chr17 Exonic NM_022066 UBE2O
hsa_circRNA_406295 0.039669886 0.431741635 1.5046538 chr3 Sense overlapping NR_109992 SUCLG2-AS1
hsa_circRNA_104484 hsa_circ_0082326 0.035552427 0.431741635 4.3097053 chr7 Exonic NM_016478 ZC3HC1
hsa_circRNA_100329 hsa_circ_0006352 0.04670856 0.431741635 1.598139 chr1 Exonic NM_012432 SETDB1
hsa_circRNA_007771 hsa_circ_0007771 0.028286903 0.431741635 1.6641182 chr6 Exonic NM_032832 LRP11
hsa_circRNA_101491 hsa_circ_0034762 0.039240976 0.431741635 4.4110245 chr15 Exonic NM_014994 MAPKBP1
hsa_circRNA_020622 hsa_circ_0020622 0.035376567 0.431741635 1.6406534 chr11 Exonic NM_006435 IFITM2
hsa_circRNA_102481 hsa_circ_0003253 0.016603437 0.431741635 1.7146811 chr19 Exonic NM_014173 BABAM1
hsa_circRNA_103444 hsa_circ_0008797 0.028562586 0.431741635 2.5886681 chr3 Exonic NM_002093 GSK3B
hsa_circRNA_104670 hsa_circ_0001818 0.021625832 0.431741635 3.9778781 chr8 Exonic NM_015902 UBR5
hsa_circRNA_406126 0.023124964 0.431741635 1.757962 chr20 Intronic ENST00000244070 PPP4R1L
hsa_circRNA_000911 hsa_circ_0001184 0.023141682 0.431741635 1.5147777 chr21 Intronic ENST00000290219 IFNGR2
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FDR: false discover rate; FC: fold change.

Table 4.

Differentially down-regulated circRNAs in serum exosomes of patients with sepsis.

circRNA Alias P-value FDR FC (abs) chrom circRNA_type best_transcript GeneSymbol
hsa_circRNA_006750 hsa_circ_0006750 0.037575777 0.431741635 1.5167592 chr10 Exonic NM_015188 TBC1D12
hsa_circRNA_008289 hsa_circ_0008289 0.007861232 0.431741635 1.5038783 chr6 Exonic NM_012454 TIAM2
hsa_circRNA_072654 hsa_circ_0072654 0.004150655 0.431741635 3.1968303 chr5 Exonic NM_005869 CWC27
hsa_circRNA_009554 hsa_circ_0009554 0.044334492 0.431741635 1.5604032 chr1 Exonic NM_007262 PARK7
hsa_circRNA_030788 hsa_circ_0030788 0.047261899 0.431741635 1.6207698 chr13 Exonic NM_052867 NALCN
hsa_circRNA_400850 0.036097077 0.431741635 1.650349 chr11 Exonic NM_016146 TRAPPC4
hsa_circRNA_404459 0.002634492 0.431741635 1.6303638 chr1 Exonic NM_022778 CEP85
hsa_circRNA_102912 hsa_circ_0058055 0.019467222 0.431741635 1.5068981 chr2 Exonic NM_000465 BARD1
hsa_circRNA_032891 hsa_circ_0032891 0.031939282 0.431741635 1.5637739 chr14 Exonic NM_145231 EFCAB11
hsa_circRNA_401829 0.032698187 0.431741635 1.5255687 chr17 Exonic NM_178509 STXBP4
hsa_circRNA_400511 0.023801242 0.431741635 1.6454873 chr10 Exonic NM_014142 NUDT5
hsa_circRNA_100726 hsa_circ_0002456 0.025458471 0.431741635 1.5928692 chr10 Exonic NM_001380 DOCK1
hsa_circRNA_405372 0.039065216 0.431741635 1.5208354 chr15 Sense overlapping NR_040051 IQCH-AS1
hsa_circRNA_007352 hsa_circ_0007352 0.032409473 0.431741635 4.6954462 chrX Exonic NM_005088 AKAP17A
hsa_circRNA_104639 hsa_circ_0084669 0.048813158 0.431741635 1.6255475 chr8 Exonic NM_024790 CSPP1
hsa_circRNA_406194 0.003824786 0.431741635 2.0373362 chr22 Sense overlapping NM_013365 GGA1
hsa_circRNA_406445 0.039630011 0.431741635 1.5055446 chr4 Intronic ENST00000264956 EVC
hsa_circRNA_405571 0.038880048 0.431741635 1.9452313 chr17 Exonic ENST00000589153 TADA2A
hsa_circRNA_405791 0.016540118 0.431741635 1.5537398 chr19 Exonic NM_006663 PPP1R13L
hsa_circRNA_104964 hsa_circ_0006502 0.031313741 0.431741635 1.6161558 chr9 Exonic NM_138778 DPH7
hsa_circRNA_100631 hsa_circ_0006148 0.012110784 0.431741635 2.1672149 chr10 Exonic NM_144660 SAMD8
hsa_circRNA_405746 0.023710234 0.431741635 1.8437062 chr19 Exonic NM_032207 C19orf44
hsa_circRNA_101461 hsa_circ_0034072 0.016991154 0.431741635 1.8499723 chr15 Exonic NM_014608 CYFIP1
hsa_circRNA_063280 hsa_circ_0063280 0.046069864 0.431741635 1.5904218 chr22 Exonic NM_012407 PICK1
hsa_circRNA_405477 0.02927257 0.431741635 1.7238343 chr16 Intronic ENST00000264005 LCAT
hsa_circRNA_400042 hsa_circ_0092302 0.025102341 0.431741635 1.5460887 chr19 Intronic ENST00000325327 LMNB2
hsa_circRNA_040203 hsa_circ_0040203 0.028512125 0.431741635 1.5408761 chr16 Exonic NM_001605 AARS
hsa_circRNA_076057 hsa_circ_0076057 0.047636875 0.431741635 1.571403 chr6 Exonic NM_017754 UHRF1BP1
hsa_circRNA_001729 hsa_circ_0000691 0.048652258 0.431741635 1.7920519 chr16 Antisense NM_014699 ZNF646
hsa_circRNA_004738 hsa_circ_0004738 0.043002838 0.431741635 1.6720137 chr5 Exonic NM_022897 RANBP17
hsa_circRNA_100559 hsa_circ_0000219 0.014298038 0.431741635 1.5281119 chr10 Exonic NM_024948 FAM188A
hsa_circRNA_002773 hsa_circ_0002773 0.029869133 0.431741635 1.5045762 chr11 Exonic NM_002906 RDX
hsa_circRNA_104004 hsa_circ_0074930 0.021445503 0.431741635 1.9530485 chr5 Exonic NM_003062 SLIT3
hsa_circRNA_100317 hsa_circ_0008390 0.04490215 0.431741635 2.1464941 chr1 Exonic NM_022359 PDE4DIP
hsa_circRNA_100707 hsa_circ_0020313 0.029667199 0.431741635 1.6620556 chr10 Exonic NM_022126 LHPP
hsa_circRNA_102461 hsa_circ_0003935 0.013483506 0.431741635 1.5061068 chr19 Exonic NM_000068 CACNA1A
hsa_circRNA_060123 hsa_circ_0060123 0.028890929 0.431741635 1.5685863 chr20 Exonic uc002xdn.1 CPNE1
hsa_circRNA_404686 0.012768084 0.431741635 1.9349548 chr1 Exonic NM_003272 GPR137B
hsa_circRNA_101321 hsa_circ_0002928 0.042321436 0.431741635 1.611344 chr14 Exonic NM_006109 PRMT5
hsa_circRNA_100536 hsa_circ_0005379 0.041730172 0.431741635 1.9452874 chr10 Exonic NM_001494 GDI2
hsa_circRNA_400994 0.011009991 0.431741635 1.5005858 chr12 Exonic uc001syj.2 ZDHHC17
hsa_circRNA_103291 hsa_circ_0006673 0.040743075 0.431741635 1.6483582 chr3 Exonic NM_025265 TSEN2
hsa_circRNA_102116 hsa_circ_0003258 0.005918665 0.431741635 1.5865527 chr17 Exonic NM_014897 ZNF652
hsa_circRNA_102950 hsa_circ_0058794 0.043872376 0.431741635 1.7071378 chr2 Exonic NM_014914 AGAP1
hsa_circRNA_020962 hsa_circ_0020962 0.039359099 0.431741635 1.6353777 chr11 Exonic uc001mai.1 HBG2
hsa_circRNA_003508 hsa_circ_0003508 0.035035101 0.431741635 1.9070829 chr17 Exonic NR_036474 GPATCH8
hsa_circRNA_008609 hsa_circ_0008609 0.037088726 0.431741635 1.5778959 chr2 Exonic NR_028356 MRPL30
hsa_circRNA_100632 hsa_circ_0018905 0.044102213 0.431741635 5.3789756 chr10 Exonic NM_144660 SAMD8
hsa_circRNA_406475 0.042571045 0.431741635 1.5153569 chr4 Intronic ENST00000264319 FRYL
hsa_circRNA_401299 0.04743786 0.431741635 1.6724819 chr14 Exonic NM_145231 EFCAB11
hsa_circRNA_102025 hsa_circ_0007542 0.04630629 0.431741635 1.5477632 chr17 Exonic NM_000267 NF1
hsa_circRNA_001101 hsa_circ_0001101 0.020138729 0.431741635 1.6929037 chr2 Exonic NM_020830 WDFY1
hsa_circRNA_012123 hsa_circ_0012123 0.046218436 0.431741635 1.7972517 chr1 Exonic uc001clf.3 ATP6V0B
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FDR: false discover rate; FC: fold change.

RT-qPCR validation of the differentially expressed circRNAs

RT-qPCR was used to verify the differentially expressed circRNAs in sepsis. We selected five circRNAs that are most likely to be related to sepsis for verification based on the fold changes in microarray analysis: hsa_circRNA_406194, hsa_circRNA_104670, hsa_circRNA_104484, hsa_circRNA_103864, and hsa_circRNA_101491. Because the microarray analysis may contain false positive results, we first verified in 3 sepsis patients and 3 healthy volunteers that had been tested by microarray to confirm the accurate expression of circRNAs. The expression levels of hsa_circRNA_406194 (0.95 ± 0.32 to 1.05 ± 0.37; p = 0.751), hsa_circRNA_104670 (2.37 ± 0.19 to 1.02 ± 0.23; p = 0.001), hsa_circRNA_104484 (1.98 ± 0.08 to 1.01 ± 0.15; p = 0.001), hsa_circRNA_103864 (1.62 ± 0.68 to 1.04 ± 0.36; p = 0.265), and hsa_circRNA_101491 (1.18 ± 0.55 to 1.03 ± 0.28; p = 0.699) (Fig. 3). Among these five circRNAs, only hsa_circRNA_104484 and hsa_circRNA_104670 were significantly increased.

Figure 3.

Figure 3

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RT-qPCR verification of five circRNAs in microarray samples. The drawings were performed using GraphPad Prism software (version 8.0, https://www.graphpad.com/scientific-software/prism/).

We further verified the expression levels of hsa_circRNA_104484 and hsa_circRNA_104670 in the serum exosomes of 22 patients with sepsis and 19 controls collected subsequently. As shown in Fig. 4, the expression of hsa_circRNA_104484 (1.829 ± 0.718 to 1.124 ± 0.506; p = 0.005) and hsa_circRNA_104670 (2.045 [1.319–3.049] to 0.948 [0.684–1.639]; p = 0.003) in serum exosomes of patients with sepsis increased, and the expression differences were statistically significant, which was consistent with the results of microarray analysis.

Figure 4.

Figure 4

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Expression of hsa_circRNA_104484 and hsa_circRNA_104670 in the serum exosomes of 22 patients with sepsis and 19 controls.

ROC analysis of serum exosomal hsa_circRNA_104484 and hsa_circRNA_104670 in sepsis

The results of qPCR were used to construct the ROC curve to evaluate the diagnostic value of exosomal hsa_circRNA_104484 and hsa_circRNA_104670 in sepsis (Fig. 5). Compared with healthy subjects, the AUC of hsa_circRNA_104484 in sepsis exosomes was 0.782 (95% confidence interval [CI]: 0.643–0.921; p < 0.05), the sensitivity and specificity were 0.545 and 0.947, respectively. The highest Youden index was 0.492 and the corresponding optimal cut-off value was 31.901. The AUC of hsa_circRNA_104670 was 0.775 (95% CI: 0.632–0.919; p < 0.05), and the sensitivity and specificity were 0.591 and 0.895, respectively. The highest Youden index was 0.486 and the corresponding optimal cut-off value was 1.357. The results indicate that hsa_circRNA_104484 and hsa_circRNA_104670 have a medium diagnostic value and have the potential to be used as diagnostic markers in sepsis.

Figure 5.

Figure 5

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ROC curve for hsa_circRNA_104484 and hsa_circRNA_104670.

Identification of circRNA‐targeting miRNAs and construction of circRNA‐miRNA‐mRNA networks

Arraystar microRNA prediction software was used to predict the miRNAs targeted by hsa_circRNA_104484 and hsa_circRNA_104670. The results showed that the miRNAs targeted by hsa_circRNA_104484 were hsa-miR-34b-5p, hsa-miR-508-3p, hsa-miR-378a-3p, hsa-miR-378d, and hsa-miR-30c-2-3p. Further, the miRNAs targeted by hsa_circRNA_104670 were hsa-miR-17-3p, hsa-miR-433-3p, hsa-miR-367-5p, hsa-miR-335-3p, and hsa-miR-642a-5p. The interaction between circRNA-microRNA is annotated in detail, and the results are shown in Fig. 6a. The ceRNA network was used to visually show the relationship between hsa_circRNA_104484 and hsa_circRNA_104670, miRNAs, and target genes (Fig. 6b).

Figure 6.

Figure 6

Figure 6

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Prediction of circRNA-miRNA-mRNA regulatory relationship. (a) Annotation of detailed regulatory relationship between hsa_circRNA_104484, hsa_circRNA_104670 and miRNAs. (b) circRNA‐miRNA‐mRNA network established using hsa_circRNA_104484 and hsa_circRNA_104670.

Prediction of the potential functions of target genes

GO analysis results showed that the biological process and molecular functions of target genes were concentrated in several aspects, such as ‘negative regulation of transcription from the RNA polymerase II promoter’, ‘transcription’, ‘positive regulation of transcription’, ‘negative regulation of transcription’, ‘positive regulation of transcription from the RNA polymerase II promoter’, ‘protein binding’, ‘DNA binding’, ‘transcriptional activator activity’, ‘RNA polymerase II transcription factor activity’, ‘transcription factor activity’, and ‘transcriptional repressor activity’ (Fig. 7a). Most of them were related to the transcriptional regulation of gene expression. Therefore, hsa_circRNA_104484 and hsa_circRNA_104670 might participate in the process of sepsis by regulating transcription.

Figure 7.

Figure 7

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Functional analysis of circRNA. (a) Gene Ontology Analysis. (b) KEGG pathway Enrichment Analysis. The drawings were performed using Microsoft Excel (version 16.43, https://www.microsoft.com/zh-cn/microsoft-365/excel).

KEGG pathway analysis results show that the target gene-related signalling pathways are the PI3K-Akt signalling pathway, signalling pathways regulating the pluripotency of stem cells, the MAPK signalling pathway, hepatitis B, viral carcinogenesis, osteoclast differentiation, hepatitis C, HTLV-I infection, TNF signalling pathway, and the insulin signalling pathway, among others (Fig. 7b). Among them, the PI3K-Akt signalling pathway22, MAPK signalling pathway23, and the TNF signalling pathway have been confirmed by several studies to be related to sepsis.

Discussion

In recent years, despite significant advances in antimicrobial treatment and organ support technologies, sepsis remains the leading cause of death in patients with severe infections24. This may be related to the lack of specificity of clinical manifestations, the complexity of pathophysiological processes, and the heterogeneity of sepsis5. Unfortunately, despite the continuous exploration of its mechanism, our understanding of it is still far from being sufficient. In fact, there are currently no laboratory testing methods to accurately identify sepsis and there are no individualised therapies to cure it. Therefore, researchers are committed to developing a precision medicine method that aims to classify patients into different types based on transcriptomic signatures and other biological and clinical data, thus providing a molecular basis for precision targeted therapy. Improving the identification and diagnosis of sepsis, exploring its pathogenesis, classification, and individualised therapy can maximise the efficacy and improve prognosis.

In recent years, exosomes have been extensively studied as a new form of intercellular signal transduction. Studies have shown that circRNAs are specifically enriched and stable in exosomes and can be detected in a variety of bodily fluids17. This means that exosomal circRNA has the potential to diagnose diseases as a biomarker5,19. They are also involved in the pathogenesis of various diseases, such as tumours25,26, cardiovascular diseases27–29, neurological disorders30–32, infections, and immune-related diseases30,33,34, indicating that they may be used as targets for precise treatment. To date, the expression and function of exosomal circRNAs in sepsis have not been reported. In order to clarify their regulatory role in the pathophysiology of sepsis, it is necessary to explore the changes in circRNA expression levels in serum exosomes and their regulatory pathways.

By comparing and analysing the results of microarrays, molecules with fold changes > 1.5 and p values < 0.05 were considered statistically significant. Then, we selected five circRNA molecules for experimental verification, including hsa_circRNA_101491, hsa_circRNA_103864, hsa_circRNA_104484, hsa_circRNA_104670, and hsa_circRNA_406194. These circRNA molecules were then verified by RT-qPCR among the 3 septic patients and 3 healthy volunteers that had been tested by microarray to determine the reliability of the microarray results. Among these five circRNA molecules, the expression of two circRNA molecules (hsa_circRNA_104484 and hsa_circRNA_104670) were significantly upregulated, consistent with the microarray results, but the other three circRNA molecules (hsa_circRNA_101491, hsa_circRNA_103864, and hsa_circRNA_406194) were not significantly different between the two groups. This indicates that microarray results contain false positives, thus, only differential circRNA molecules qualified by RT-qPCR are considered reliable. We continued to verify hsa_circRNA_104484 and hsa_circRNA_104670 in small clinical samples, and the results are consistent with those of previous studies. To the best of our knowledge, this study is the first report the expression of hsa_circRNA_104484 and hsa_circRNA_104670 in sepsis serum exosomes.

At present, ceRNA is the most common circRNA regulation mechanism. CircRNA targets miRNAs and indirectly regulates the expression of miRNA target genes and plays an important role in the occurrence and development of diseases35. Studies have found that circulating miRNAs are differentially expressed in inflammation-related diseases and can target the tumour necrosis factor pathway (TLR/NF-κB signalling pathway), acting as inflammation regulators36,37. Therefore, we speculate that circRNA may indirectly regulate the expression of inflammation-related genes by targeting miRNAs in sepsis. The annotation of the circRNA-miRNA regulatory axis and the construction of the ceRNA network showed that five miRNAs and several targeted mRNAs interacted with hsa_circRNA_104484 and hsa_circRNA_104670, respectively.

Among them, hsa_circRNA_104484 is a sponge molecule of hsa-miR-378a-3p/hsa-miR-378d. In recent experimental studies, miR-378 has been found to act directly or indirectly as a regulator of inflammation and participates in the processes of inflammation and immune regulation. Platelet-derived exosomal miR-378a-3p directly targets PDK1, resulting in the inhibition of the Akt/mTOR pathway and promoting the formation of neutrophil extracellular traps (NET) in sepsis38. A study by Caserta et al.36 showed that miR-378a-3p is differentially expressed in systemic inflammatory response syndrome (SIRS) and correlated with its severity. miR-378a can directly target ZBTB20, which plays a role in cell growth and apoptosis39. ZBTB20 is a transcriptional repressor that inhibits the transcription of the IκBα gene and positively regulates the activation of NF-κB, triggering an innate immune response40,41. This is consistent with the results of the GO analysis. In addition, miR-378 negatively regulates nuclear respiratory factor-1 (NRF-1), AMP-activated protein kinase γ2 (AMPKγ2), and phosphoinositide 3-kinase (PI3K), inhibits energy metabolism processes, and activates the NF-κB-TNFα pathway, which may be related to SIRS and sepsis42–44. Similarly, hsa_circRNA_104670 is a sponge molecule of hsa-miR-17-3p. Jiang and Li et al.45 found that lipopolysaccharide (LPS) and TNF-α can regulate the expression of miR-17-3p. miR-17-3p directly targets intercellular adhesion molecule 1 (ICAM-1) and inhibits its expression in LPS-induced acute lung injury (ALI)46. ICAM-1 is an important inflammatory mediator, and its expression is upregulated in sepsis, which enhances inflammatory cell infiltration and organ damage47,48. Therefore, we speculate that hsa_circRNA_104484 and hsa_circRNA_104670 may be involved in the pathogenesis of sepsis.

Conclusions

Our study compared the differences in the expression levels of circRNAs in serum exosomes between sepsis and healthy people, and initially evaluated the clinical application value of hsa_circRNA_104484 and hsa_circRNA_104670. The results provide a basis for mechanistic research. However, our research sample is relatively small; in the future, the sample size will be enlarged. We will further explore the biological functions of hsa_circRNA_104484 and hsa_circRNA_104670 through cell and animal experiments. Currently, the pathogenesis of sepsis is still unclear. As such, there is no effective therapeutic intervention; the exploration of the circRNA regulatory mechanism in sepsis will have great clinical translation research value.

Supplementary Information

Supplementary Information. (279KB, pdf)

Acknowledgements

We thank all patients and volunteers who participated in this study.

Abbreviations

RT-qPCR

Real-time quantitative polymerase chain reaction

ROC

Receiver operating characteristic

GO

Gene ontology

KEGG

Kyoto City Encyclopaedia of Genes and Genomes

circRNAs

Circular RNA

miRNA

MicroRNA

PBS

Phosphate buffer saline

cDNA

Complementary DNA

MF

Molecular functions

BP

Biological pathways

CC

Cellular components

AUC

Area under the ROC curve

TEM

Transmission electron microscopy

CI

Confidence interval

NET

Neutrophil extracellular traps

SIRS

Systemic inflammatory response syndrome

NRF-1

Nuclear respiratory factor-1

AMPKγ2

AMP-activated protein kinase γ2

PI3K

Phosphoinositide 3-kinase

LPS

Lipopolysaccharide

ICAM-1

Intercellular adhesion molecule 1

ALI

Acute lung injury

SOFA

Sequential organ failure assessment

Author contributions

C.T. and K.W. conceived and performed the study. J.Y.L. and M.Z. participated in the collection of blood samples and patient characteristics. S.C. analysed the data and made the pictures and graphs. C.T. drafted and revised the manuscript. X.D. revised the manuscript. All the authors have read and approved the final manuscript.

Funding

This study was supported by the Department of Finance of Jilin Province (Grant ZXWSTZXEY019), the Department of Science and Technology of Jilin Province (Grant 20200708083YY, Grant 20191102012YY, Grant 20191313162SF).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-021-93246-0.

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