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Identification of the CeRNA axis of circ_0000006/miR-483-5p/KDM2B in the progression of aortic aneurysm to aorta dissection

BMC Cardiovascular Disorders volume 25, Article number: 141 (2025) Cite this article

Abstract

Background

Aortic aneurysm (AA) and aortic dissection (AD) are serious cardiovascular disorders with a high risk of mortality. The molecular mechanisms underlying the progression from AA to AD are not well understood. This study aimed to identify the key circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory axis involved in this disease progression.

Methods

CircRNA microarray, miRNA microarray, and mRNA sequencing were performed on plasma samples from healthy controls, AA patients, and AD patients. Bioinformatics analysis integrated the expression profiles to identify dysregulated circRNA-miRNA-mRNA networks. Key molecules were validated in vascular smooth muscle cells (VSMCs) and an AD mouse model. Cell proliferation, migration, and phenotypic transition assays were conducted after modulating the identified circRNA. The impact on AD progression was evaluated in mice upon circRNA knockdown.

Results

A total of 12 circRNAs were found upregulated in AD compared to AA samples. miR-483-5p was downregulated while its targets KDM2B and circ_0000006 were upregulated in AD. Silencing circ_0000006 in VSMCs inhibited PDGF-induced phenotypic switching, proliferation, and migration by increasing miR-483-5p and decreasing KDM2B levels. In the AD mouse model, knockdown of circ_0000006 alleviated disease progression with similar molecular changes.

Conclusion

The study identified a novel circ_0000006/miR-483-5p/KDM2B axis dysregulated during AD progression. Targeting this axis, especially circ_0000006, could be a potential strategy to mitigate the transition from AA to AD by modulating VSMC phenotype and function.

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Introduction

Aortic aneurysm (AA) and aortic dissection (AD) are serious cardiovascular disorders characterized by abnormal enlargement of the thoracic aortic wall, leading to the eventual rupture of the aortic vessel [1]. Aneurysm refers to the abnormal growth of the aortic wall, which can result in the rupture of the inner layer of the aortic wall and the flow of blood into the surrounding layers [2]. Acute AD is a life-threatening condition that requires immediate treatment due to the high risk of aortic rupture [3]. Various genetic and environmental factors contribute to the development of aneurysms in the aortic wall, including cocaine use, weight lifting-induced trauma, chronic or genetic conditions (such as arterial hypertension and phaeochromocytoma), ongoing inflammation, and oxidative stress [1, 4–5]. Due to the unpredictable risk of acute AD in patients with AA, routine surveillance is recommended for individuals with thoracic aneurysms, and aneurysm removal is advised if necessary [6]. Alternative intervention strategies, such as the use of β-adrenergic blocking agents, have been proposed to slow down abnormal aneurysm progression, although recent evidence has raised doubts about their effectiveness [7–8]. Understanding the mechanisms underlying the progression from AA to AD could provide valuable insights for the development of novel treatment strategies.

A well-established pathological hallmark of patients with AA and AD is the remodeling of the aortic wall [9]. Vascular smooth muscle cells (VSMCs) play a major role in the initiation and progression of AA, and they can undergo phenotypic switch during vascular remodeling under pathological conditions [10–11]. In the process of AA formation and progression, VSMCs transition from a contractile (differentiated) phenotype to a synthetic (de-differentiated) state. This transition is accompanied by the infiltration of inflammatory immune cells, which promote the pathological growth of the aneurysm [10,11,12]. In the healthy vascular wall, contractile VSMCs can be converted into synthetic VSMCs in the presence of aortic trauma or chronic cardiovascular disease. This conversion is triggered by abnormal signaling from growth factors, such as platelet-derived growth factor (PDGF) [13–14]. The contractility of VSMCs is reduced, while the capacities of proliferation and migration are increased, leading to the de-differentiation, abnormal proliferation, and migration of VSMCs. These changes are characterized by the decreased expression of contractile markers such as α-SMA and SM22α [15–16]. The phenotypic transition of VSMCs plays a crucial role in the initiation and growth of AA, as well as the progression to AD [17–18]. Therefore, targeting the phenotypic transition of VSMCs is considered a promising approach to mitigate the progression of AA and AD [19–20]. Understanding the molecular players responsible for the phenotypic switch of VSMCs is of great significance for targeted intervention.

Given the complex molecular mechanisms underlying VSMC phenotypic switching in the progression from AA to AD, there is growing interest in understanding the role of regulatory RNA molecules in these processes. Non-coding RNAs (ncRNAs) have emerged as factors implicated in the development and progression of cardiovascular diseases, including AA and AD [21–22]. Among the various categories of ncRNAs, circular RNAs (circRNAs) are a class of stable RNA molecules with a covalently linked loop structure that are found in all eukaryotic cells [23]. The dysregulation of circRNAs significantly impacts cardiovascular diseases [24]. It has been reported that circRNAs regulate the endothelial permeability of the human aorta [25–26]. Some circRNAs, such as circMARK3, have been suggested as diagnostic markers for human acute Stanford type A AD [27]. CircRNAs are widely known as competitive endogenous RNAs (ceRNAs) that interact with and absorb miRNA targets, thereby restricting the activity of downstream miRNA targets and regulating the expression of miRNA target genes [28]. Nonetheless, the potential ceRNA regulatory module involved in the progression from AA to AD is not yet fully understood.

The current study aimed to analyze the differential expression of circRNAs, miRNAs, and mRNAs in the progression from AA to AD. To achieve this, circRNA and miRNA microarray, as well as mRNA profiling, were conducted on plasma samples from healthy controls, AA, and AD patients. A significant circRNA-associated ceRNA axis involving circ_0000006, miR-483-5p, and KDM2B was found to be dysregulated during AD progression. The impact of this regulatory module on the phenotypic switch of VMSCs and its involvement in the AD progression of a mouse model were investigated. These findings highlight a novel circRNA-dependent ceRNA axis in the progression of AD and provide potential molecular targets for the clinical management of AD progression.

Materials and methods

Plasma sample collection

In this study, we collected plasma samples from 4 healthy controls, 4 patients with AA, and 4 patients with AD who were recruited from the Affiliated Hospital of North Sichuan Medical College. All the subjects were between 46 and 53 years old. Subjects with any of the following conditions were excluded: history of cancer, autoimmune diseases, chronic inflammatory diseases, diabetes, severe liver or kidney dysfunction, recent surgery (within 3 months), pregnancy, or use of immunosuppressive medications. Additionally, patients with genetic connective tissue disorders (such as Marfan syndrome, Ehlers-Danlos syndrome) or inflammatory arterial diseases were excluded to minimize confounding factors. The usage of human samples was approved by the medical research committee of the Affiliated Hospital of North Sichuan Medical College (Approval number 2022ER136-1), and all recruited subjects provided informed consent. Plasma was isolated through a standardized protocol of double centrifugation (2000 g for 10 min at 4 °C, followed by 12000 g for 10 min at 4 °C) to ensure complete removal of cellular debris. After isolation, samples were immediately transferred to cryogenic storage vials and stored in liquid nitrogen (-196 °C) until RNA extraction to maintain RNA integrity and prevent degradation.

CircRNA microarray

From the plasma samples total RNA was purified with TRizol Reagent based on the manufacturer’s protocol (Invitrogen, Shanghai, China), with DNase I digestion before further analysis. After quantification using NanoDrop ND-1000 spectrophotometer, 10 µg sample was digested with 10 unit of Rnase R (Epicentre Biotechnologies, Beijing, China) at 37℃ for 30 min to remove linear RNAs. Then the circRNA-enriched samples were amplified and transcribed into fluorescent circRNA using Arraystar Super RNA labeling kit (Arraystar, CA, USA). Afterwards 1 µg of labeled circRNA was fragmented in the fragmentation buffer for 30 min incubation at 60℃. Subsequently, 25 µl of 2X hybridization buffer was added to dilute 25 µl fragmented circRNA, and 50 µl of hybridization solution was dispensed to the circRNA expression microarray slide. The fragmented circRNAs were hybridized to the probes on the Arraystar Human circRNA Array V2 chip (8 × 15 K; Arraystar, CA, USA). After washing the hybridization signal was detected using the Agilent Scanner G2505C (Agilent Technologies, CA, USA).

MiRNA microarray

From the plasma samples, the miRNA was extracted using mirVana miRNA isolation kit (Ambion, TX, USA) according to the supplier’s instructions, with DNase I digestion before further analysis. After concentration determination using Qubit miRNA quantification kit (Invitrogen, Shanghai, China), the miRNA integrity was examined using Agilent Bioanalyzer 2100 (Agilent Technologies, CA, USA). 100 ng of miRNA sample was analyzed on the Agilent Human miRNA array chip (8 × 60k; ID: 070156). The miRNA was amplified, dephosphorylated, and labeled with cyanine-3-CTP (Cy3). The labeled miRNA samples were hybridized onto the miRNA array chip, and after washing the hybridization signal was detected using the Agilent Scanner G2505C (Agilent Technologies, CA, USA).

mRNA transcriptome profiling

For RNA-seq analysis, total RNA samples were extracted using TRIzol Reagent following the manufacturer’s protocol (Invitrogen, Shanghai, China), with DNase I digestion before further analysis. The samples were adjusted to a concentration of 200 ng/ml, and the optical density of the 260/280 ratio was approximately 2.0. The RNA integrity and concentration were further assessed using Qubit and Bioanalyzer 2100 (Agilent Technologies, CA, USA). For each sample, 100 ng of total RNA was utilized for mRNA library construction, employing the TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, USA). Deep sequencing was performed on the Illumina HiSeq 2500 platform.

Bioinformatics analyses

Signals from circRNA microarray images were scanned and extracted using the feature extraction software (v11.0.1.1; Agilent Technology). The limma package from the R software package (https://www.r-project.org/) was used for data normalization and relative expression analysis. CircRNAs with log2 [fold change (FC)] ≥ 1 and a P < 0.05 were deemed as the differentially expressed circRNAs between two samples (DEcircRNA). For miRNA microarray data analysis, the raw data signals were normalized using Genespring GX 12.5 software (Agilent Technologies). miRNAs with a log 2 [fold change] ≥ 1 and a P < 0.05 were considered to be the differentially expressed miRNAs (DEmiRNA). For RNA-seq data analysis, the raw reads were aligned against Homo Sapiens GRCh38 -hg38 reference genome using tophat2 v2.1.0. FPKM (Fragments Per Kilobase Million) values were derived by Cufflinks v2.2.1 from the alignments based on the same genome/annotation. Differential gene expression analysis was conducted by Cuffdiff package. FDR-adjusted p value after Benjamini-Hochberg correction was used as the statistic for multiple test. Genes with FDR < 0.05 and a log 2[Fold change (FC)] ≥ 1 were determined to be differentially expressed (DEmRNA).

The circRNA IDs were unified using circBase (http://www.circbase.org), and CircAtlas ( http://circatlas.biols.ac.cn/) resource was utilized to predict the miRNA targets of DEcircRNA (AA vs. AD). The intersection of DEcircRNA target miRNAs and the DEmiRNAs (AA vs. AD) was finalized as the DEcircRNA-DEmiRNA interactions. Then, the mRNA targets of DEmiRNA (AAvs AD) were predicted using the predictive target module of the miRWalk3.0 (http://mirwalk.umm.uni-heidelberg.de/). The predicted mRNAs were then analyzed against the DEmRNA (AA vs. AD), and the intersection of DEmiRNA target genes and DEmRNAs identified by RNA-seq were used as the final list of DEmiRNA-DEmRNA interactions. The final DEcircRNA-DEmiRNA-DEmRNA interaction network was established based on the shared DEmiRNAs which were predicted as DEcircRNA downstream target and DEmRNA upstream regulator.

The enrichment analysis of biological processes (BP), cellular components (CC), and molecular functions (MF), and KEGG pathway enrichment analysis and visualization of DEmRNAs were conducted by clusterProfiler R-package, with the following functions being used: “org.Hs.eg.db”, “enrichplot”, “ggplot2”. Terms with a threshold of P-value < 0.05 and q-value < 0.05 were considered to be significantly enriched.

Cell culture and transfection

Primary human Aortic Smooth Muscle Cells (Human VSMCs) were obtained from the ATCC (PCS-100-012, Manassas, VA, USA; https://www.atcc.org/products/pcs-100-012). VSMCs were cultivated in human SMC medium (Sciencell, CA, USA) at 37℃ with 5% CO2. Experiments were performed at 3–10 passages of the cell. Cells were treated with PDGF-BB (PeproTech, NJ, USA) at 20 ng/mL for 48 h to induce the phenotypic switch. The scramble siRNA (KD-NC), siRNA targeting circ_0000006 was synthesized by RiboBio Ltd. (Guangzhou, China). The miR-483-5p mimic, and miR-NC were prepared by ZSGentech Ltd. (Tianjin, China). Cell transfection was conducted using Lipofectamine 3000 (Invitrogen, Shanghai, China) based on the supplier’s protocol. 48 h post-transfection, the cells were collected for further experiments. Sequence information: hsamiR-483-5p mimic: F: AAGACGGGAGGAAAGAAGGGAG, R:CCCUUCUUUCCUCCCGUCUUUU; miR-NC: F: UUCUCCGAAC GUGUCACGUTT, R: ACGUGACACGUUCGGAGAATT; si-NC: UUCUCCG AACGUGUCACGU; si-circ_0000006: AUAAAAAUACCUAGAUUUCUG.

qRT-PCR

RNA extraction was performed using the Beyozol total nucleic acid extraction reagent (Beyotime, Nanjing, China) according to the manufacturer’s instruction. For mRNA analysis, 1 µg of RNA sample was subjected to reverse transcription via the PrimeScript™ RT Reagent Kit (Takara Biotechnology, Otsu, Japan). Circular RNA Synthesis Kit (Beyotime, Beijing, China) was used for circRNA sample preparation. To analyze miRNA, 1 µg of RNA sample was used for cDNA synthesis using the Taqman™ microRNA reverse transcription kit (Thermo Fisher Scientific, CA, USA). qPCR analysis of expression level was conducted using SYBR premix EX TAQ II kit (Takara Biotechnology, Otsu, Japan) on the QuantStudio 3 Real-Time PCR System, with the following cycling condition: 40 cycles of denaturation (95℃, 30 s), and annealing and extension (60℃, 45 s). Relative gene expression was analyzed by 2–∆∆Ct method, with GAPDH or U6 snRNA as the internal reference for normalization. Primer sequences are summarized in Table S1.

CCK-8 proliferation assay

VSMCs were plated into a 96-well plates at the concentration of 2500 cells/well and the cells under indicated treatment conditions were cultured for 0, 24, 48, and 72 h. Then 10 µL CCK8 reagent (Beyotime, Beijing, China) was mixed with the cell culture medium for 3-hour incubation at 37℃. The absorbance (OD value) in each condition was detected at 450 nm on the Synergy H1 microplate reader (Winooski, Vermont, USA).

Wound healing assay

VSMCs were cultured in a 6-well plate until reaching 80% confluence. A sterile tip was used to remove the cells in the middle region of each well as a wound. The floating cells were removed by changing the medium, and the remaining cells were cultured at 37 ℃ for 48 h. The wound images on the cell monolayer were recorded at 0 h and 48 h using an inverted light microscope.

Dual luciferase reporter assay

The functional interaction between two molecules were analyzed via dual luciferase reporter assay. First, the sequence containing wild type binding site (WT) or the mutated binding site (MUT) was inserted into the PmirGLO firefly luciferase reporter (Promega, WI, USA). The reporter and Renilla luciferase control plasmid were co-transfected into VSMCs using Lipofectamine 3000 reagent. 48 h after the transfection, the firefly luciferase activity and Renilla luciferase activity in each sample were determined using the Dual-Luciferase Reporter Assay Kit (Promega, WI, USA) on a luminescence microplate reader.

Western blot

The RIPA lysis buffer (Beyotime, Beijing, China) was used for cell lysis on ice for 15 min. After collecting the supernatant, the sample concentration was measured via a BCA assay kit (Beyotime, Beijing, China). 20 µg of protein samples were loaded into 10% SDS-PAGE gel for separation, which was followed the transfer onto the PVDF membrane. To detect the protein targets, the membrane was incubated for 24 h at 4℃ with the following primary antibodies: α-SMA (1:1000, ab210557, Abcam), SM22α (1:1000, ab14106, Abcam), MYH11 (1:1000, ab224804, Abcam), beta-actin (1:2000, ab216070, Abcam), and KDM2B (1:1000, ab5199, Abcam). After washing, the membrane was further labeled with HRP-linked secondary antibody (1:3000; Cell signaling Technologies, MA, USA) for 1 h at ambient temperature. Signal development was conducted using ECL Western blot reagent (Biovision, Beijing, China). The protein bands were photographed using the GelDoc imager system (Bio-Rad, CA, USA). The relative intensity of protein bands was quantified via Image J software (Bethesda, MD, USA).

Mouse AD model

BALB/c mice (4 weeks old, male) were purchased from Beijing HFK Bioscience Co., Ltd. (Beijing, China). BALB/c mice were chosen for this study due to their well-characterized cardiovascular responses and relatively stable progression from AA to AD, allowing for better survival rates compared to acute AD models that typically result in high mortality. The mice were numbered sequentially from 1 to 60 and randomly assigned into 4 groups (n = 15 in each group, 60 animals in total) using block randomization (block size of 4) with a computer-generated random sequence (generated by Microsoft Excel’s RAND function). Each group was housed in different cages. The randomization was performed by a researcher who was not involved in the subsequent experiments to ensure allocation concealment. The following 4 groups were included: (1) control group: mice were fed with regular chow diet and drinking water; (2) model group: mice were fed with regular diet and drinking water with 0.1 g/kg/day of 3-aminopropionitrile fumarate salt (BAPN) for 28 days, followed by subcutaneous injection of angiotensin II for 16 days (dosage: 1.5 mg /kg/day on 1–7 days, 0.75 mg/kg/day on 8–14 days, and 0.375 mg /kg/day on 15–16 days) [29–30]. (3) model + sh-NC: in the model group mice were administered with 0.2 mL Adeno-associated virus (AAV) carrying scramble shRNA (UUCUCCGAACGUGUCACGU) on day 36; (4) model + sh-circ_0000006 group: in the model group, mice were administered with 0.2 mL AAV carrying circ_0000006 shRNA (AAAUCAAACCAUCAAUCUCUC) on day 36. The AAV were produced at 1000 pfu/mL by Genechem Ltd. (Shanghai, China), and were injected around thoracic aorta arch three times. Of note, for the model, model + sh-NC, and model + sh-circ_0000006 groups, a total of 15 mice was included in each group for aortic aneurysm induction. On day 36, 6 mice in each group with confirmed aortic aneurysm growth were then included for the final experiment to observe the further aortic dissection. For the in vivo detection of aortic aneurysm, 100 µl ExiTron nano 12,000/25 g mouse was injected and the visualization of aortic aneurysm was conducted by Micro-CT. Two weeks after AAV administration, all the mice were anesthetized through intraperitoneal injection of pentobarbital at 50 mg/kg, and sacrificed by cervical dislocation at the end of the experiment. Thoracic aortas of the AA formation site were dissected and subjected to histological analysis, including Hematoxylin-eosin (H&E) staining and Verhoeff-Van Gieson (VVG) staining. The animal protocol was approved by the animal use and welfare Committee of Affiliated Hospital of North Sichuan Medical College (No. 2023ER-114-1). The relevant experiments comply with the Declaration of Helsinki and the ARRIVE guidelines (see Table 1).

Table 1 Animal experimental condition and treatment
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Statistics

All the data analyses were conducted with SPSS 20.0 software (IBM SPSS, NY, USA). The results were expressed as the mean ± standard deviation. Data normality was assessed using the Shapiro-Wilk test, and homogeneity of variance was evaluated using Levene’s test. For normally distributed data, comparison between two conditions was analyzed by unpaired Student’s t test. Multiple comparisons among different conditions were conducted using one-way analysis of variance (ANOVA), followed by the Tukey’s post-hoc test. Data at multiple time points in different groups were examined using two-way ANOVA. P < 0.05 was considered to be statistically different.

Results

Identification of 12 upregulated circrnas in AD samples in comparison to AA and the control samples

To identify circRNAs with dysregulation in the progression from AA to AD, we performed circRNA microarray analysis using RNA samples extracted from the plasma of healthy controls, patients diagnosed with AA, and AD. By focusing on molecules that are commonly dysregulated across all comparison pairs, we can identify key players that are relevant to both the initial disease state and its progression, making them potentially more reliable as therapeutic targets or biomarkers for the AA-to-AD continuum. The volcano plots of two sample comparisons revealed that 1 circRNA was significantly upregulated and 4 circRNAs were down-regulated in AA samples compared to controls. In AD samples, 2 circRNAs were upregulated and 2 were down-regulated compared to controls. Interestingly, we found that 12 circRNAs were upregulated in AD samples compared to AA samples (Fig. 1A). Using a Venn diagram, we observed that none of the 12 dysregulated circRNAs were found to be dysregulated between AA and control samples. Additionally, only one DEcircRNA was shared between the AD vs. AA and AD vs. Control pairs (Fig. 1B). Notably, all 12 dysregulated circRNAs were highly upregulated in AD samples (Fig. 1C), as shown in the heatmap which displayed their high-expression pattern in AD samples and low expression levels in both control and AA samples (Fig. 1D). These findings were further validated by qRT-PCR analysis using a new batch of clinical samples for verification (Fig. 1E). Collectively, our data suggest that these dysregulated circRNAs may play a specific role in the progression from AA to AD.

Fig. 1
figure 1

Identification of 12 DEcircRNAs upregulated in AD samples in comparison to AA and the control samples. (A). Volcano plots showing the differentially expressed circRNAs (DEcircRNAs) among AA vs. control, AD vs. control and AD vs. AA groups. (B). Venn diagram of the interaction of DEcircRNAs among AA vs. control, AD vs. control and AD vs. AA pairs. (C). List of 12 DEcircRNAs being upregulated in AD samples in comparison to AA samples. (D). Heatmap displaying the relative expression levels of 12 DEcircRNAs in the control, AA and AD samples. (E) qRT-PCR validation of DEcircRNA expressions in the control, AA and AD samples (n = 4 in each group). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001

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Identification of key DEmiRNAs dysregulated in AD samples

miRNA microarray analysis was performed on the control, AA, and AD samples. Volcano plots revealed that in the AA samples compared to the control, 5 miRNAs were upregulated and 1 miRNA was down-regulated. Similarly, in the AD samples compared to the control, 4 miRNAs were upregulated and 2 miRNAs were down-regulated. Additionally, in the AD samples compared to the AA samples, 3 miRNAs were upregulated and 3 miRNAs were down-regulated (Fig. 2A). A Venn diagram demonstrated that the majority of the differentially expressed miRNAs (DEmiRNAs) were not shared between the pairwise comparisons (Fig. 2B), indicating distinct regulatory pathways in AA and AD progression. Notably, hsa-miR-126-3p, hsa-let-7e-5p, and hsa-miR-483-5p were down-regulated in AD samples, while hsa-miR-221-3p, hsa-miR-25-3p, and hsa-miR-92a-3p were upregulated in AD samples (Fig. 2C and D). The expression patterns of these DEmiRNAs in the control, AA, and AD samples were further confirmed by qRT-PCR analysis (Fig. 2E).

Fig. 2
figure 2

Identification of key DEmiRNAs dysregulated in AD samples. (A). Volcano plots showing the differentially expressed miRNAs (DEmiRNAs) among AA vs. control, AD vs. control and AD vs. AA groups. (B). Venn diagram of the interaction of DEmiRNAs among AA vs. control, AD vs. control and AD vs. AA pairs. (C). List of DEmiRNAs dysregulated in AD samples in comparison to AA samples. (D). Heatmap displaying the relative expression levels of DEmiRNAs in the control, AA and AD samples. (E) qRT-PCR validation of DEmiRNA expressions in the control, AA and AD samples (n = 4 in each group). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001

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Transcriptome profiling of healthy control, AA and AD samples

mRNA-seq analysis was conducted on the control, AA, and AD samples. The analysis revealed that, at the mRNA level, 133 genes were upregulated in the AA samples compared to the control, while 59 genes were down-regulated. Similarly, in the AD samples, 172 genes were upregulated and 195 genes were down-regulated compared to the control. Furthermore, when comparing the AD samples to the AA samples, 109 genes showed up-regulation and 233 genes showed down-regulation (Fig. 3A and B). The Venn diagram demonstrated that there were a large number of differentially expressed mRNAs (DEmRNAs) identified in each individual pair, but there were no DEmRNAs shared by all three group comparisons (Fig. 3C). These findings collectively indicate a divergence in the transcriptome profile during the progression of AA and AD.

Fig. 3
figure 3

Transcriptome profiling of healthy control, AA and AD samples. (A). Volcano plots showing the differentially expressed mRNAs (DEmRNAs) among AA vs. control, AD vs. control and AD vs. AA groups. (B). Summary of DEmRNAs being dysregulated in each pairwise comparison. (C). Venn diagram of the interaction of DEmRNAs among AA vs. control, AD vs. control and AD vs. AA pairs

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To gain further insights into the changes of gene expression in AA and AD progression, we conducted gene ontology (GO) analysis and KEGG pathway analysis on the DEmRNAs in AA vs. control and AD vs. AA. This is to dissect the distinct molecular mechanisms involved in disease initiation (AA vs. control) versus disease progression (AD vs. AA). In AA progression (AA vs. control), the top ranked biological processes affected were response to organic nitrogen, response to amine stimuli, and membrane lipid metabolism. On the other hand, in the comparison of AD vs. AA, a significant number of genes involved in general cell metabolism were predominantly affected. Additionally, calcium ion transport and hemopoietic progenitor cell differentiation were also found to be affected (Fig. 4A). The signaling pathways implicated in AA progression included salivary secretion, actin skeleton regulation, and PI3K-AKT pathway. Conversely, transcription misregulation in cancer, sulfur relay system, RNA degradation, riboflavin metabolism, and protein processing in endoplasmic reticulum were the major pathways affected in AD progression (Fig. 4B).

Fig. 4
figure 4

Functional enrichment analysis of DEmRNAs in AA vs. control, and AD vs. AA comparison. (A). The significantly enriched biological processes (BP), cellular components (CC) and molecular functions (MF) in each pairwise comparison. (B). The significantly enriched KEGG pathways in each pairwise comparison

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CeRNA network analysis reveals circ_0000006/miR-483-5p/KDM2B axis as a key regulator in the progression of AA to AD

To investigate the progression of AA to AD, we aimed to identify key ceRNA axes. Our approach involved predicting the miRNA targets of the DEcircRNAs and the mRNA targets of the DEmiRNAs in AD vs. AA samples. We then selected the intersection of DEmiRNA mRNA targets and DEmRNAs (AD vs. AA) identified by RNA-seq, resulting in a list of DEmiRNA-DEmRNA interactions. The final DEcircRNA-DEmiRNA-DEmRNA interaction network was generated by considering the shared DEmiRNA predicted as a DEcircRNA target and the upstream regulator of DEmRNAs. Through these analyses, we identified the circ_0000006- miR-483-5p-KDM2B (ENSG00000089094) axis as the ceRNA module. In AD samples, circ_0000006 and KDM2B were upregulated, while miR-483-5p showed down-regulation (Fig. 5A and B). To confirm their functional interactions, we conducted a dual luciferase reporter assay using a luciferase vector containing predicted wild type (WT) or mutated (MUT) binding sites. The introduction of miR-483-5p mimic significantly increased miR-483-5p expression level in human vascular smooth muscle cells (VSMCs) (Figure S1A). Further, the transfection of miR-483-5p mimic inhibited the luciferase activity for both circ_0000006-WT and KDM2B-WT reporters. When these binding sites were mutated, the inhibitory effect was no longer observed in the MUT reporter (Fig. 5C and D), indicating their interaction through the predicted wild type binding sequences.

We next examined the role of circ_0000006 in regulating the functional phenotype in VSMCs, which are crucial cell components in the aortic wall. As platelet derived growth factor (PDGF) is known to induce VSMC de-differentiation and vascular remodeling [31–32], we used PDGF treatment to induce the phenotypic switch as a cell model. Furthermore, we applied a siRNA which could suppress the expression of circ_0000006 in VSMCs (Figure S1B). Our findings revealed that PDGF treatment increased the expression of circ_0000006 in VSMCs, and this up-regulation was suppressed by transfection of circ_0000006 siRNA. Furthermore, we observed that circ_0000006 knockdown reversed the down-regulation of miR-483-5p and the up-regulation of KDM2B caused by PDGF treatment (Fig. 5E). Western blot analysis of the contractile markers (α-SMA, SM22α, and MYH11) demonstrated that PDGF treatment reduced their expression, indicating the de-differentiation of VSMCs. However, the silencing of circ_0000006 restored their expression (Fig. 5F). Additionally, circ_0000006 knockdown significantly suppressed PDGF-induced cell proliferation and migration in VSMCs (Fig. 5G and H). These results suggest that the up-regulation of circ_0000006 may contribute to PDGF-induced phenotypic switch, proliferation, and migration in VSMCs. Furthermore, circ_0000006 regulates the expression of miR-483-5p and KDM2B in the PDGF-mediated phenotypic switch of VSMCs.

Fig. 5
figure 5

CeRNA network analysis reveals circ_0000006/miR-483-5p/KDM2B axis as a key regulator in the progression of AA to AD. (A). DEcircRNA-DEmiRNA- DEmRNA interaction network analysis revealed circ_0000006-miR-483-5p-KDM2B (ENSG00000089094) axis as the ceRNA module in the progression of AA to AD. (B). qRT-PCR analysis revealed that circ_0000006 and KDM2B were upregulated in AD samples, while miR-483-5p showed down-regulation (n = 4 samples in each group). (C-D). Dual luciferase reporter analysis of WT or MUT reporter of circ_0000006 and KDM2B in the presence of miR-NC or miR-483-5p mimic. (E). Human vascular smooth muscle cells (VSMCs) was treated with PDGF as a cell model of AD, and in the model group, VSMCs were transfected with control siRNA (KD-NC) or siRNA targeting circ_0000006 (circ_0000006 KD). qRT-PCR analysis of the expressions of circ_0000006, miR-483-5p and KDM2B in each experimental condition. (F). Western blot analysis of the contractile markers (α-SMA, SM22α, and MYH11) in each experimental condition. (G). CCK-8 proliferation assay in each experimental condition. (H) Wound healing assay of the migratory ability in each experimental condition. N = 3 independent experiments. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001

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Silencing circ_0000006 ameliorates AD progression in mouse model

We next sought to evaluate the potential effect of circ_0000006 in modulating AD progression in animal model. The AA mouse model was established through 3-aminopropionitrile (BAPN) administration for 28 days and the subcutaneous injection of angiotensin II for 16 days. The transition from aortic aneurysm to aortic dissection may be due to the administration of AngII after BAPN treatment. Ang II can cause biophysical changes of the aortic cell wall by increasing the blood pressure [29–30]. For the model, model + sh-NC, and model + sh-circ_0000006 groups, a total of 15 mice was initially included in each group for AA induction. In the time course, we recorded two animal deaths after AA confirmation. This may be due to that the AD animal model from AA is not a strong or acute AD model, while acute AD induction could lead to high mortality rate. We confirmed that AD is from AA since we initially visualized AA formation in vivo through the injection of 100 µl ExiTron nano 12,000 per 25 g mouse on day 36, and AA formation in the aorta was confirmed by Micro-CT. On day 36, 6 mice in each group with confirmed aortic aneurysm growth were then included for the final experiment to observe the development of aortic dissection. All the AA mice developed AD histological features after AngII induction. The observation was consistent with the previous findings that BAPN and angiotensin II administration could induce a very high incidence of AD in the mouse model (nearly 100%) [29–30]. At the end of the experiment, the tissue sections in thoracic aorta which corresponds to the original AA formation site detected by Micro-CT were dissected and subjected to histological analysis for AD confirmation.

In model + sh-NC and model + sh-circ_0000006 groups, AAV carrying control shRNA or circ_0000006 shRNA was administrated to examine the effect on AA to AD progression. The body weight, systolic blood pressure (SBP) and diastolic blood pressure (DBP) in each group were measured 2 weeks after AAV administration. There is no significant change in body weight among different groups. In the AD model group, both SBP and DBP showed significant increase and silencing circ_0000006 shRNA was able to partially alleviate the increase of SBP and DBP in the model group (Fig. 6A). We observed the dissection of aortic wall in the mice of model group. Silencing of circ_0000006 mitigated the formation of aortic dissection as revealed by the H&E staining in the aortic sections (Fig. 6B). Circ_0000006 knockdown also reduced the thickness of aortic wall in the model group (Fig. 6C). Besides, Verhoeff-Van Gieson (VVG) staining revealed that the elastic fibers in aortic wall were disrupted in the model group, while circ_0000006 knockdown partially protected the integrity of the elastic fibers (Fig. 6D). qRT-PCR analysis of aortic tissues revealed increased levels of circ_0000006 and KDM2B, along with reduced expression of miR-483-5p in the model group. Silencing circ_0000006 also reduced KDM2B expression and increased miR-483-5p levels (Fig. 6E). Western blot analysis of contractile markers (α-SMA, SM22α, and MYH11) showed down-regulation in the model group compared to the control, but silencing circ_0000006 restored their expression in the model group (Fig. 6F). These findings suggest that circ_0000006 over-expression is involved in the progression of AD by modulating the miR-483-5p/KDM2B axis.

Fig. 6
figure 6

Silencing circ_0000006 ameliorates AD progression in mouse model. BALB/c mice were administrated with 3-aminopropionitrile fumarate salt (BAPN) for 28 days, and with angiotensin II for 16 days to induce AD in the animal model. In the model group, Adeno-associated virus (AAV) carrying scramble shRNA (sh-NC), or circ_0000006 shRNA was injected into the mice on the Day 36 (n = 6 in each group). (A) The measurement of body weight, systolic blood pressure (SBP) and diastolic blood pressure (DBP) in each experimental group. (B) H&E staining of the aorta wall tissue sections in each experimental group. (C) The quantification of aortic wall thickness in each group. (D) Verhoeff-Van Gieson (VVG) staining of the elastic fibers in aortic wall. (E) qRT-PCR analysis of circ_0000006, miR-483-5p and KDM2B expression in the aorta wall tissues. (F) Western blot analysis of contractile markers (α-SMA, SM22α, and MYH11) in the aorta wall tissues. *p < 0.05; **p < 0.01; ***p < 0.001; ****P < 0.0001

Full size image

Discussion

Considerable progress has been made in understanding the pathogenesis of large vessel disease. However, there is still a lack of knowledge regarding the molecular mechanism underlying the progression from AA to AD, as well as the molecular players dictating the phenotypic switch of VSMCs. In this study, an integrative analysis of circRNA, miRNA, and mRNA profiling was conducted to focus on understanding the progression from AA to AD. The analyses revealed significant differences in the profiles of DEcircRNA, DEmiRNA, and DEmRNA between the AA vs. control comparison and the AD vs. AA pair. These findings indicate that distinct regulatory mechanisms underpin the development of AA and the progression from AA to AD. For example, 12 DEcircRNAs that were upregulated in AD samples compared to AA samples showed no difference between AA and control samples. Additionally, most of the DEmiRNAs were unique to specific pairwise comparisons. Future efforts should aim to recruit a large cohort of patients and healthy controls to evaluate whether some of the DEcircRNAs could serve as predictive biomarkers for AD. These biomarkers could provide valuable indications for the clinical management of AA patients. We acknowledge that confounding factors such as medications, gender, ethnicity, and comorbidities could influence circRNA expression profiles, and future studies with larger cohorts should include multivariate analyses to account for these potential confounders and validate the robustness of these molecular signatures across diverse patient populations.

Recent studies have implicated the dysregulation of circular RNAs (circRNAs) in the progression of AA or AD. For instance, Tian et al. identified 506 differentially expressed circRNAs in acute Stanford type A aortic dissection (AAAD) [27]. Another study found 156 upregulated and 106 down-regulated circRNAs in human type A thoracic aortic dissection (TAD) tissues [25]. A comprehensive analysis of public datasets established a ceRNA regulatory network in aortic dissection (AD) samples compared to healthy controls [33]. However, none of these studies investigated the changes in non-coding RNAs (ncRNAs) during the progression from AA to AD.

Our data revealed that circ_0000006 is a significantly upregulated factor in the progression from AA to AD. While circ_0000006 has been studied in other conditions [34–35], its role in the progression from AA to AD has not been previously reported. Our study showed that circ_0000006 was significantly upregulated in the AD samples compared to both the control and AA clinical samples. Through in vitro cell model and AD animal model studies, we observed that silencing circ_0000006 suppressed the phenotypic transition of human VSMCs and alleviated AD development in the mouse model. Based on these findings, we conclude that circ_0000006 may play a detrimental role in mediating the progression from AA to AD. In the study, we also adopted PDGF-induced phenotypic switch in human VSMCs. The adoption of human cell model could reflect the phenotypic switch and the role of ceRNA network in human condition [36–37]. Furthermore, there is clear clinical evidence that PDGF levels are increased in AA/AD patients, which may be due to the activation and recruitment of macrophages to injured sites [38–39]. These evidence is related to our cell model of PDGF-induced phenotypic switch in human VSMCs.

The ceRNA interaction analysis revealed the circ_0000006/miR-483-5p/KDM2B axis as a regulator of AA to AD progression. In clinical samples, circ_0000006 and KDM2B were upregulated in AD compared to AA samples, while miR-483-5p was down-regulated. MiR-483-5p has known roles in cardiovascular disorders, including coronary artery disease, carotid artery stenosis, and hypertension [40–41]. Notably, its reduced expression is associated with idiopathic pulmonary arterial hypertension, while its over-expression improves pulmonary hypertension in rat models [42]. Although KDM2B’s role in AA or AD remains unexplored, it is highly expressed in endothelial cell cancers and regulates malignant hematopoiesis [43–44]. These findings suggest that KDM2B over-expression, regulated by the circ_0000006/miR-483-5p axis, may contribute to endothelial cell transformation in AA and AD progression, warranting further investigation.

VSMCs play a crucial role in controlling the contraction and blood flow in the aorta wall [45]. The contractile nature of VSMCs is regulated by cytoskeleton machinery such as α-SMA and SM22α. However, when contractile proteins are down-regulated and matrix metalloproteinases are upregulated, VSMCs undergo a phenotypic transition from a contractile state to a synthetic phenotype [46–47]. This transition leads to increased proliferation and migration of VSMCs, contributing to the growth of abdominal aortic aneurysm (AA) and an increased risk of aortic dissection (AD) [48]. In our study, we found that circ_0000006, which is upregulated in VSMCs treated with PDGF, plays a role in the de-differentiation, proliferation, and migration of VSMCs. Silencing circ_0000006 attenuated these effects. Additionally, in an animal model, AAV-mediated silencing of circ_0000006 suppressed the development of AD. Silencing circ_0000006 in VSMCs and the animal model was associated with increased expression of miR-483-5p and reduced levels of KDM2B. miR-483-5p is known to regulate angiogenesis [49], and high levels of miR-483-3p have been shown to protect against hypertension [50]. These findings support the protective effect of miR-483-5p in endothelial cells.

Several key questions remain unresolved in our study. Firstly, it is necessary to clarify the mechanism by which circ_0000006 becomes over-expressed in the progression from AA to AD. Future research should investigate whether the dysregulation of circ_0000006 is triggered by the hypoxia condition or inflammatory responses during AA formation. Furthermore, further investigation is needed to understand the functional role of KDM2B in regulating the phenotypic switch of VSMCs. Of note, one mechanism could not fully explain the pathogenesis of AA to AD, and currently no single mechanism could be directly translated into a curable therapy [51,52,53,54]. Therefore, future study should aim to investigate whether the circ_0000006/miR-483-5p/KDM2B axis also regulates inflammatory processes or endothelial cells, which could provide additional insights into the broader vascular pathology. In real-world, the AA actually does not equal to AD. In some acute aortic dissection patients, there is no dilatation of aorta. Our findings were based on the histological analysis showing the formation of aortic dissection in the model group. By gaining a better understanding of the roles and mechanisms of these molecular players, novel intervention opportunities to suppress the progression from AA to AD may be identified.

Conclusions

Taken together, our data uncovered a key ceRNA regulatory module in dictating the phenotypic switch of VSMCs in the progression of AA to AD. Circ_0000006 was found to be specifically upregulated in AD samples, and its over-expression mediates the loss of contractile state in VSMCs. Circ_0000006 serves as an upstream factor to negatively regulate miR-483-5p and promote KDM2B expression. Further investigations are required to evaluate the potential of Circ_0000006 as a biomarker for AD progression and to elucidate the mechanism through which KDM2B regulates the phenotypic plasticity of VSMCs.

Data availability

The data supporting this study’s findings are available from the corresponding author, and were uploaded to Sequence Read Archive(SRA) data repository (accession number: PRJNA1010728, https://www.ncbi.nlm.nih.gov/Traces/study/?query_key=9%26;WebEnv=MCID_6657ecf929d21a51ea6c000f%26;o=acc_s%3Aa).

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Acknowledgements

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Funding

This work was supported by:

1. Chinese Academy of Sciences(CAS)”Light of West China” Program (NO: [2020]82).

2. Science and Technology Department of Sichuan Province (NO: [2022YFS0365]).

3. Scientific research and development foundation of North Sichuan Medical College (NO: LYBS).

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  1. Yong Liu, Xiong Tan, Liang Wang, and Weitao Jin are Co-first authors.

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  1. Department of Cardiovascular Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China

    Yong Liu, Xiong Tan, Liang Wang, Weitao Jin, Yangchen He, Yu yan, Kai Hu, Hao Wang, Chaohu Xiang, Ming Hou & Yinglong Lai

  2. No. 63, Culture Road, Nanchong, Sichuan, 637000, China

    Yong Liu

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Yong Liu was responsible for project planning, financial support, and writing the manuscript; Xiong Tan, Liang Wang, and Weitao Jin completed the main experimental procedures and participated in writing the manuscript; Yangchen He and Yu Yan participated in the experimental procedures and in discussions. Kai Hu, Hao Wang, Chaohu Xiang, Ming Hou and Yinglong Lai were responsible for analyzing the data and participating in the discussions. All authors contributed to the article and approved the submitted version.

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Liu, Y., Tan, X., Wang, L. et al. Identification of the CeRNA axis of circ_0000006/miR-483-5p/KDM2B in the progression of aortic aneurysm to aorta dissection. BMC Cardiovasc Disord 25, 141 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04598-8

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04598-8

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

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