Dysregulation of microRNA Modulatory Network in Abdominal Aortic Aneurysm

Abstract

Abdominal artery aneurysm (AAA) refers to abdominal aortic dilatation of 3 cm or greater. AAA is frequently underdiagnosed due to often asymptomatic character of the disease, leading to elevated mortality due to aneurysm rupture. MiRNA constitute a pool of small RNAs controlling gene expression and is involved in many pathologic conditions in human. Targeted panel detecting altered expression of miRNA and genes involved in AAA would improve early diagnosis of this disease. In the presented study, we selected and analyzed miRNA and gene expression signatures in AAA patients. Next, generation sequencing was applied to obtain miRNA and gene-wide expression profiles from peripheral blood mononuclear cells in individuals with AAA and healthy controls. Differential expression analysis was performed using DESeq2 and uninformative variable elimination by partial least squares (UVE-PLS) methods. A total of 31 miRNAs and 51 genes were selected as the most promising biomarkers of AAA. Receiver operating characteristics (ROC) analysis showed good diagnostic ability of proposed biomarkers. Genes regulated by selected miRNAs were determined in silico and associated with functional terms closely related to cardiovascular and neurological diseases. Proposed biomarkers may be used for new diagnostic and therapeutic approaches in management of AAA. The findings will also contribute to the pool of knowledge about miRNA-dependent regulatory mechanisms involved in pathology of that disease.

1. Introduction

Abdominal aortic aneurysms (AAA) are segmental dilatations of the abdominal aorta measuring 50% greater than the proximal normal segment, or >3 cm in maximum diameter [1,2]. Screening programs launched in different populations reported the prevalence of AAA between 4% and 8% in general population of men aged 65–80 years [3] and lower (0.45%) in Asians [4]. AAA rupture is responsible for 0.3–0.4% of all death cases and approximately 1% of deaths among men above 65 years globally, causing 130,000 to 180,000 fatalities per year [5]. Although mortality of AAA is decreasing in the 21st Century in many countries including United States and United Kingdom (mostly due to introduction of more advanced endovascular and open surgery repair techniques and better risk factor management), in other countries (Hungary, Romania), AAA mortality is still increasing [6,7,8,9].

The specific mechanism initiating and leading to progression of AAA has not yet been elucidated; however, AAA development has been associated with a variety of infections like brucellosis, salmonellosis and tuberculosis, trauma and connective tissue disorders, Takayasu disease and Marfan syndrome. Identified risk factors for aneurysm development include older age, male gender, cigarette smoking, obesity, dysregulation of lipid levels, hypertension [2,10,11,12] and genetic predisposition [13,14,15,16].

Patients with AAA may report nonspecific symptoms like abdominal and back pain; however, in many cases disease progress is asymptomatic. The prolonged course of asymptomatic phase of AAA provides a relatively long diagnostic window before rupture [17]. Despite many studies targeting circulatory biomarkers of AAA, there is still lack of robust molecular methods able to classify affected individuals with satisfactory precision [18,19,20].

MiRNA-dependent regulation of gene expression emerged as a new tool providing novel opportunities in diagnosis of AAA. MiRNAs are approximately 18–25 nucleotides long, non-coding and single-stranded RNAs, which exhibit gene expression regulating effect by binding to mRNA. The pairing effect of miRNA-mRNA interactions predominantly inhibits gene expression by repression of translation, destabilization and cleavage of mRNA [21,22]. Currently miRNAs are a particularly intensively studied with promising preliminary results opening door to novel diagnostic and treatment approaches [23].

Large studies comparing miRNA and gene expression patterns in patients with AAA and healthy individuals may provide novel biomarkers with good discriminative value improving our diagnostic capability of detecting aneurysm, its rates of progression and complications; however, their introduction to clinical practice requires further investigations [24,25,26,27].

Although differential expression of miRNAs in human AAA cases was reported in abdominal aortic tissue, whole blood, serum and plasma samples, deregulation of miRNA expression in PBMCs (peripheral blood mononuclear cells) was not extensively studied.

In the present study, we applied next generation sequencing to analyze miRNA and gene expression in PBMCs of AAA patients and healthy volunteers with a goal to find the most capable miRNA and gene expression biomarkers of AAA and to research a potential role of identified biomarkers in pathogenesis of AAA.

The study design, methodology, article structure and language have been inspired by our previous studies regarding deregulation of miRNA regulatory network in lower extremities arterial disease [28] and chronic venous disease [29].

2. Materials and Methods

2.1. Study Participants Characteristics

The study was performed in accordance with the Declaration of Helsinki. The study design was approved by the Ethics Committee of Medical University of Lublin (decision No. K × 10−254/341/2015). Inclusion was carried out between February 2016 and May 2017. Twenty eight patients hospitalized due to intrarenal true AAA in Independent Public Clinical Hospital No. 1 in Lublin were included in the AAA group. All patients underwent pre-operative aneurysm surveillance, which included duplex ultrasonography and contrast enhanced spiral computed tomography with volume-rendered reconstructions.

Nineteen healthy and non-smoking volunteers were included in the control group. Control subjects have not shown presence of abdominal aorta dilatation and abnormalities during duplex ultrasound scanning.

Informed and signed consent was obtained from all study participants. All participants were asked about smoking habits and medical history to establish exclusion criteria, which included presence of inflammatory aneurysm, false aneurysm, thoracic aorta aneurysm, isolated popliteal or iliac artery aneurysm, aortic and/or arterial dissection, stroke, transient ischemic attack (TIA), myocardial infarction, diabetes mellitus type I, symptomatic peripheral arterial disease (ankle brachial index < 0.8), connective tissue disorders including rheumatoid disease, impaired hepatic or renal function, corticoid therapy, infection within previous 6 weeks, recent deep venous thrombosis (less than 1 year), pulmonary embolism, inflammatory and/or infectious disease and cancer. Detailed characteristics of case and control group are presented in

Table 1. Characteristics of 28 patients with abdominal aortic aneurysm (AAA) and 19 controls included to the study.

Characteristic	AAA Population (n = 28)	Control Population (n = 19)	p
Age	66.39 ± 4.52 1 57–76 2	36.58 ± 9.97 1 24–55 2	8.30 × 10−9
Body Mass Index	25.08 ± 3.30 1 18.03–31.25 2	23.12 ± 3.93 1 19.33–32.6 2	4.05 × 10−2
Current smoking	9 (32.1%)	0 (0%)	6.69 × 10−3
Sex: Male	25 (89.3%)	9 (47%)	2.63 × 10−3
Sex: Female	3 (10.7%)	10 (53%)
Abdominal aneurysm measurements
Maximum aneurysm diameter (cm)	6.389 ± 0.633 1 5.6–7.8 2	NA
Thrombus volume (cm3)	9.782 ± 3.296 1 2.9–16.5 2	NA
Aneurysm neck length (cm)	0.925 ± 0.219 1 0.5–1.3 2	NA
Risk factors and cardiovascular comorbidities
Coronary artery disease	7 (25.0%)	NA
Diabetes type 2	6 (21.4%)	NA
Hypertension	19 (67.9%)	NA
Clinical parameters
Red blood cells (M/µL)	4.94 ± 0.21 1 4.56–5.50 2	NA
White blood cells (K/µL)	5.66 ± 0.70 1 4.44–6.90 2	NA
Platelets (K/µL)	419.93 ± 123.98 1 211 – 756 2	NA
Hemoglobin (g/dL)	14.02 ± 0.51 1 13.34–15.00 2	NA
Hematocrit (%)	40.75 ± 1.30 1 38–43 2	NA
Creatinine (mmol/L)	54.18 ±11.53 1 39–87 2	NA
Urea (mmol/L)	4.66 ± 0.67 1 3.45–5.88 2	NA
Medication
Statins	13 (46.4%)	NA
Acetylsalicylic acid	27 (96.4%)	NA
Clopidogrel	3 (10.7%)	NA
Beta-adrenergic blockers	16 (57.1%)	NA
Angiotensin Converting Enzyme Inhibitor	4 (14.3%)	NA
Ca2+ channel blockers	2 (7.14%)	NA
Fibrates	2 (7.14%)	NA
Metformin	3 (10.7%)	NA
Gliclazide	4 (14.3%)	NA
Treatment
Open surgery	2 (7.14%)	NA
Stent graft	26 (92.9%)	NA

¹ Mean ± SD, ² range. Statistical significance (p) of differences between AAA and control group in age and body mass index were determined using two-sided Mann–Whitney U test, and in sex and smoking habits were determined using two-sided Fisher’s exact test. AAA—Abdominal Aortic Aneurysm, “NA”—not applicable.

. Application of exclusion criteria enabled to include healthy individuals to control group; however, statistically significant differences in age, body mass index (BMI), smoking habits, and sex distribution were noticed when compared to AAA group (Table 1). Construction of AAA and control groups is described in detail in Appendix A.

2.2. Study Material Preparation and Sequencing

The procedure of study material preparation and sequencing was conducted as previously described in [28].

Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood specimens using density gradient centrifugation with Gradisol L reagent (Aqua-Med, Łódź, Poland). Proportions of white blood cells subpopulations in AAA group were obtained from venous blood morphology analysis results and were presented in Figure S1.

Small RNA fractions (for miRNA expression analysis) were isolated from PBMCs specimens of twenty eight AAA patients and nineteen control subjects using MirVana microRNA Isolation Kit (Ambion, Austin, TX, USA).

Total RNA specimens (for transcriptome analysis) were isolated from PBMCs samples of seven randomly selected AAA patients and seven randomly selected controls using TRI Reagent Solution (Applied Biosystems, Foster City, CA, USA).

Small RNA and transcriptome libraries were prepared using Ion Total RNA-Seq Kit v2, Magnetic Bead Cleanup Module kit, Ion Xpress RNA-Seq Barcode 01-16 Kit and sequenced on Ion 540 chips (all Life Technologies, Carlsbad, CA, USA) using Ion S5 XL System (Thermo Fisher Scientific, Waltham, MA, USA). Raw sequences of small RNA and transcriptomic libraries were aligned to 2792 human miRNAs from miRBase v21 (http://www.mirbase.org) and to 55,765 genes of hg19 human genome, respectively.

2.3. Statistical and Bioinformatical Analysis

Detailed description of methodology applied to statistical and bioinformatical analysis was provided in our previous study [28].

The differences of AAA and control groups in age and BMI were evaluated using two-sided Mann–Whitney U test (wilcox.test function in R), and in sex and smoking using Fisher’s exact test (fisher.test function in R).

Statistical analysis of miRNA expression data (resulted from sequencing of small RNA libraries) and gene expression data (resulted from sequencing of transcriptome libraries) was performed using R environment (version 3.5.2, https://www.r-project.org). Analysis was conducted on biological replicates. Differential expression analysis was performed using DESeq2 and UVE-PLS (uninformative variable elimination by partial least squares) [30] methods implemented in DESeq2 1.18.1 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html) [31] and plsVarSel 0.9.3 (https://cran.r-project.org/web/packages/plsVarSel/index.html) [32] packages, respectively. MiRNA and gene transcripts found by DESeq2 method with p value < 0.05 after adjustment by Benjamini–Hochberg false discovery rate were considered as statistically significant. UVE-PLS analysis was performed for miRNA and gene expression data using 3 and 2 PLS components, respectively. UVE-PLS analysis was executed with 1,000 iterations and default cut-off threshold.

Visualizations including Venn diagrams, heat-maps and PCA (principal component analysis) plots were prepared using VennDiagram 1.6.20 (https://cran.r-project.org/web/packages/VennDiagram/index.html) [33], pheatmap 1.0.10 (https://cran.r-project.org/web/packages/pheatmap/index.html) and ggplot2 3.2.1 (https://cran.r-project.org/web/packages/ggplot2/index.html) packages, respectively.

Receiver operating characteristics (ROC) analysis was performed using pROC package version 1.12.1 (https://cran.r-project.org/web/packages/pROC/index.html) [34]. Spearman rank correlation test implemented in Hmisc package 4.4-0. (https://cran.r-project.org/web/packages/Hmisc/index.html) was used to perform correlation analysis.

In order to evaluate the diversity of cell subpopulation in PBMCs specimens, the deconvolution of gene expression data was performed using “quanTIseq” [35] and “MCPcounter” [36] methods implemented to immunedeconv 2.0.0 package (https://rdrr.io/github/grst/immunedeconv/) [37].

Interactions between selected miRNAs and genes were identified using multiMiR package 1.2.0 (https://bioconductor.org/packages/release/bioc/html/multiMiR.html) [38]. Obtained interactions formed a regulatory network, which was presented using Cytoscape v3.5.1 software (https://cytoscape.org/) [39].

Functional analysis was performed for genes included in the network using DAVID (Database for Annotation, Visualization and Integrated Discovery) 6.8 database (https://david.ncifcrf.gov/) [40,41] and it’s supporting resources: KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway maps, the Reactome Database of Signaling Pathways, GAD (Genetic Association Database) and GO (Gene Ontology). As a background, default whole Homo sapiens genome was applied. All associated terms of KEGG, Reactome and GAD categories were harvested as well as associated GO terms with Expression Analysis Systematic Explorer (EASE) score < 0.05.

3. Results

The summary of research process is presented on Figure 1.

3.1. Study Population Analysis

Study population characteristics (28 patients with AAA and 19 controls) are presented in Table 1. Statistically significant differences between AAA and control groups were noticed in relation to age (p = 8.30 × 10⁻⁹), BMI (p = 4.05 × 10⁻²), gender (p = 2.63 × 10⁻³) and smoking history (p = 6.69 × 10⁻³), resulting from inclusion of healthy, AAA-negative individuals in control group (Table 1, Figure S2).

3.2. Primary Results

Results of libraries assessments, Ion Sphere Particles enrichment quality control and results of sequencing data primary analysis are shown in Tables S1 and S2. To assess sequencing data quality, MA plot, boxplot of Cook’s distances across samples and histogram of p values frequency were performed for miRNA (Figure S3) and transcriptome (Figure S4) sequencing results.

3.3. Differential Expression Analysis of miRNA

Differential expression analysis of miRNA between 28 AAA patients and 19 non-AAA controls was performed using DESeq2 and UVE-PLS methods.

DESeq2 comparative analysis of the miRNA expression signatures revealed 1107 differentially expressed miRNA transcripts in AAA group. Altered expression of 187 miRNA transcripts was characterized by statistical significance (p < 0.05) after adjustment by the Benjamini–Hochberg false discovery rate. To limit false positive results, for further comparison with UVE-PLS results we selected 36 differentially expressed miRNA transcripts (for 32 mature miRNAs) with adjusted p < 0.0001 (Table S3).

UVE-PLS analysis has returned 75 informative miRNA transcripts (Table S4). The arrangement of prediction error and PLS components as well as cross-validated predictions versus measured values were presented on Figure S5.

In the next step, the set of 36 differentially expressed miRNA transcripts (p < 0.0001) identified by DESeq2 method and the set of 75 differentially expressed miRNA transcripts identified by UVE-PLS method as informative were compared on Venn diagram (Figure 2a). The comparison disclosed 33 miRNA transcripts selected by both methods (Figure 2a). Differential expression of these 33 miRNA transcripts is visualized on heat-map with Euclidean clustering and PCA plot (Figure 2b,c, respectively).

Discriminative value of altered expression in selected 33 miRNA transcripts was assessed using ROC analysis. Calculated areas under the curve ranged between 0.981 and 0.795, indicating good ability of AAA classification (

Table 2. Set of 33 miRNA transcripts, which significance of differential expression was confirmed by DESeq2 analysis with p < 0.0001 and by uninformative variable elimination by partial least squares (UVE-PLS) analysis in patients with abdominal aortic aneurysm in comparison to controls.

No.	miRNA Transcript	miRNA ID*	p	Fold Change	PLS Coefficient	ROC-AUC
Upregulated miRNA transcripts
1	hsa-mir-21_hsa-miR-21-5p	hsa-miR-21-5p	9.19 × 10−12	1.356	1.61 × 10−2	0.953
2	hsa-mir-21_hsa-miR-21-3p	hsa-miR-21-3p	1.73 × 10−9	1.704	2.77 × 10−2	0.919
3	hsa-mir-34a_hsa-miR-34a-5p	hsa-miR-34a-5p	5.61 × 10−9	2.188	4.04 × 10−2	0.927
4	hsa-mir-454_hsa-miR-454-3p	hsa-miR-454-3p	2.74 × 10−8	1.216	1.15 × 10−2	0.940
5	hsa-mir-574_hsa-miR-574-5p	hsa-miR-574-5p	1.13 × 10−6	1.364	1.65 × 10−2	0.898
6	hsa-mir-424_hsa-miR-424-3p	hsa-miR-424-3p	2.03 × 10−6	1.872	2.61 × 10−2	0.861
7	hsa-mir-450b_hsa-miR-450b-5p	hsa-miR-450b-5p	2.76 × 10−6	1.834	2.54 × 10−2	0.872
8	hsa-mir-24-2_hsa-miR-24-3p	hsa-miR-24-3p	8.59 × 10−6	1.143	6.77 × 10−3	0.874
9	hsa-mir-34a_hsa-miR-34a-3p	hsa-miR-34a-3p	1.42 × 10−5	2.357	2.42 × 10−2	0.867
10	hsa-mir-542_hsa-miR-542-3p	hsa-miR-542-3p	4.14 × 10−5	1.666	1.86 × 10−2	0.852
11	hsa-mir-503_hsa-miR-503-5p	hsa-miR-503-5p	6.92 × 10−5	1.781	1.99 × 10−2	0.821
12	hsa-mir-7847_hsa-miR-7847-3p	hsa-miR-7847-3p	7.00 × 10−5	2.270	2.45 × 10−2	0.861
13	hsa-mir-548d-1_hsa-miR-548d-3p	hsa-miR-548d-3p	7.10 × 10−5	1.493	9.31 × 10−3	0.848
14	hsa-mir-122_hsa-miR-122-5p	hsa-miR-122-5p	7.94 × 10−5	1.790	1.88 × 10−2	0.795
15	hsa-mir-3591_hsa-miR-3591-3p	hsa-miR-3591-3p	7.94 × 10−5	1.789	1.88 × 10−2	0.795
16	hsa-mir-424_hsa-miR-424-5p	hsa-miR-424-5p	9.56 × 10−5	1.579	1.79 × 10−2	0.810
Downregulated miRNA transcripts
17	hsa-mir-31_hsa-miR-31-5p	hsa-miR-31-5p	4.18 × 10−12	0.344	−4.97 × 10−2	0.981
18	hsa-mir-31_hsa-miR-31-3p	hsa-miR-31-3p	4.18 × 10−12	0.329	−5.27 × 10−2	0.970
19	hsa-mir-874_hsa-miR-874-5p	hsa-miR-874-5p	7.39 × 10−11	0.429	−3.33 × 10−2	0.934
20	hsa-mir-361_hsa-miR-361-3p	hsa-miR-361-3p	8.26 × 10−10	0.683	−1.81 × 10−2	0.945
21	hsa-mir-342_hsa-miR-342-3p	hsa-miR-342-3p	1.22 × 10−7	0.592	−1.94 × 10−2	0.923
22	hsa-mir-138-1_hsa-miR-138-5p	hsa-miR-138-5p	3.65 × 10−7	0.368	−4.28 × 10−2	0.852
23	hsa-mir-125b-2_hsa-miR-125b-5p	hsa-miR-125b-5p	1.32 × 10−6	0.552	−2.56 × 10−2	0.868
24	hsa-mir-150_hsa-miR-150-5p	hsa-miR-150-5p	1.88 × 10−6	0.581	−2.04 × 10−2	0.906
25	hsa-mir-3607_hsa-miR-3607-5p	hsa-miR-3607-5p	2.03 × 10−6	0.532	−2.86 × 10−2	0.880
26	hsa-mir-769_hsa-miR-769-5p	hsa-miR-769-5p	5.36 × 10−6	0.813	−9.44 × 10−3	0.874
27	hsa-let-7g_hsa-let-7g-3p	hsa-let-7g-3p	7.34 × 10−6	0.750	−1.16 × 10−2	0.887
28	hsa-mir-125b-1_hsa-miR-125b-5p	hsa-miR-125b-5p	7.34 × 10−6	0.560	−2.35 × 10−2	0.857
29	hsa-mir-138-2_hsa-miR-138-5p	hsa-miR-138-5p	2.47 × 10−5	0.397	−3.78 × 10−2	0.863
30	hsa-mir-339_hsa-miR-339-3p	hsa-miR-339-3p	4.31 × 10−5	0.770	−1.00 × 10−2	0.868
31	hsa-mir-5585_hsa-miR-5585-3p	hsa-miR-5585-3p	4.64 × 10−5	0.396	−1.91 × 10−2	0.801
32	hsa-mir-99a_hsa-miR-99a-3p	hsa-miR-99a-3p	6.92 × 10−5	0.481	−2.38 × 10−2	0.853
33	hsa-mir-766_hsa-miR-766-3p	hsa-miR-766-3p	8.72 × 10−5	0.808	−1.63 × 10−2	0.852

¹ According to miRBase 22 (http://www.mirbase.org/). Presented 33 miRNA transcripts give 31 mature miRNAs (miRNA IDs). The table presents p (after Benjamini–Hochberg false discovery rate correction) and fold change values resulted from DESeq2 analysis, PLS (partial least squares) coefficients resulted from UVE-PLS (uninformative variable elimination by partial least squares) analysis and areas under ROC (receiver operating characteristics) curves (ROC-area under curves (AUC)) resulted from ROC analysis. MiRNA transcripts were divided into upregulated and downregulated groups and ordered according to increasing p value.

and Table S5, Figure S6). Therefore, a set of 31 mature miRNAs (16 upregulated and 15 downregulated) encoded by selected 33 miRNA transcripts was proposed as the most promising miRNA biomarkers of AAA (Table 2).

3.4. Differential Expression Analysis of Genes

Transcriptomic analysis was performed for randomly selected 7 AAA patients and 7 non-AAA controls. Differential expression analysis of genes was performed using DESeq2 and UVE-PLS methods.

DESeq2 analysis revealed 26,816 differentially expressed genes in AAA group when compared to controls. Altered expression of 2238 genes resulted with statistical significance p < 0.05, after adjustment by Benjamini–Hochberg false discovery rate. To limit false positive results, a set of 155 differentially expressed genes with adjusted p < 0.0001 was chosen for further comparison with UVE-PLS results (Table S6).

UVE-PLS analysis disclosed 91 informative genes, which expression differentiated AAA and control groups (Table S7). Figure S7 presents the arrangement of prediction error and PLS components and also cross-validated predictions versus measured values.

The comparison between the set of 155 differentially expressed genes revealed by DESeq2 (p < 0.0001) and the set of 91 informative genes selected by UVE-PLS disclosed 51 genes common for both methods (Figure 3a). A potential of these 51 genes to differentiate AAA and control groups was evaluated by PCA analysis and Canberra clustering (Figure 3b,c, respectively).

ROC analysis showed strong discriminative value of changed expression of these 51 genes with an area under the ROC curve varying from 0.939 to 1 (

Table 3. Set of 51 differentially expressed genes, which significance of differential expression was confirmed by DESeq2 analysis with p < 0.0001 and by uninformative variable elimination by partial least squares (UVE-PLS) analysis in patients with abdominal aortic aneurysm in comparison to controls.

No.	Gene Symbol	Gene Name	P	Fold Change	PLS Coefficient	ROC-AUC
Upregulated Genes
1	CPT1A	carnitine palmitoyltransferase 1A	1.70 × 10−10	2.487	1.567 × 10−3	1.000
2	GGT1	gamma-glutamyltransferase 1	1.11 × 10−8	1.973	9.424 × 10−4	1.000
3	UPF1	UPF1 RNA helicase and ATPase	2.43 × 10−8	1.321	4.369 × 10−4	1.000
4	AC092620.2	Unmatched	8.85 × 10−7	2.867	1.239 × 10−3	1.000
5	UBE4B	ubiquitination factor E4B	5.72 × 10−6	1.300	3.839 × 10−4	1.000
6	HTT	huntingtin	1.12 × 10−5	1.388	4.526 × 10−4	1.000
7	NBEAL2	neurobeachin like 2	1.38 × 10−5	1.517	5.590 × 10−4	1.000
8	GIT2	GIT ArfGAP 2	2.08 × 10−5	1.449	5.331 × 10−4	1.000
9	THOC5	THO complex 5	2.72 × 10−5	1.319	4.027 × 10−4	1.000
10	ZZEF1	zinc finger ZZ-type and EF-hand domain containing 1	2.82 × 10−5	1.291	3.325 × 10−4	1.000
11	ANKRD13D	ankyrin repeat domain 13D	3.01 × 10−5	1.428	4.791 × 10−4	1.000
12	SUFU	SUFU negative regulator of hedgehog signaling	4.11 × 10−5	1.482	5.397 × 10−4	1.000
13	RN7SKP89	RN7SK pseudogene 89	4.45 × 10−5	2.764	8.740 × 10−4	0.980
14	ZSWIM8	zinc finger SWIM-type containing 8	5.52 × 10−5	1.355	4.127 × 10−4	1.000
Downregulated genes
15	SNORA60	small nucleolar RNA, H/ACA box 60	1.19 × 10−11	0.547	−1.082 × 10−3	1.000
16	MIRLET7F2	microRNA let-7f-2	4.89 × 10−10	0.285	−1.620 × 10−3	1.000
17	SNHG5	small nucleolar RNA host gene 5	5.05 × 10−10	0.433	−1.296 × 10−3	1.000
18	SNORD20	small nucleolar RNA, C/D box 20	7.75 × 10−10	0.235	−2.069 × 10−3	1.000
19	SNORA72	small nucleolar RNA, H/ACA box 72	3.72 × 10−9	0.358	−1.464 × 10−3	1.000
20	SNORD117	small nucleolar RNA, C/D box 117	1.11 × 10−8	0.457	−1.228 × 10−3	1.000
21	SNORD82	small nucleolar RNA, C/D box 82	1.17 × 10−8	0.357	−1.448 × 10−3	1.000
22	SNORD94	small nucleolar RNA, C/D box 94	5.10 × 10−8	0.387	−1.642 × 10−3	1.000
23	SNORD101	small nucleolar RNA, C/D box 101	5.43 × 10−8	0.330	−1.558 × 10−3	1.000
24	RNA5SP355	RNA, 5S ribosomal pseudogene 355	8.24 × 10−8	0.053	−1.178 × 10−3	1.000
25	SNORD103C (SNORD85)	small nucleolar RNA, C/D box 103C	1.34 × 10−7	0.342	−1.352 × 10−3	0.980
26	RPL3P9	ribosomal protein L3 pseudogene 9	1.87 × 10−7	0.260	−1.237 × 10−3	0.980
27	RP11-16F15.2	Unmatched	2.06 × 10−7	0.344	−1.054 × 10−3	1.000
28	RP11-302F12.1	Unmatched	2.25 × 10−7	0.194	−1.588 × 10−3	1.000
29	SNORA12	small nucleolar RNA, H/ACA box 12	4.92 × 10−7	0.631	−7.161 × 10−4	1.000
30	SNORA33	small nucleolar RNA, H/ACA box 33	6.82 × 10−7	0.633	−7.151 × 10−4	1.000
31	ZRANB2	zinc finger RANBP2-type containing 2	7.81 × 10−7	0.710	−5.094 × 10−4	1.000
32	SNORD91B	small nucleolar RNA, C/D box 91B	9.72 × 10−7	0.324	−1.497 × 10−3	1.000
33	RP11-253E3.1	Unmatched	1.32 × 10−6	0.315	−1.007 × 10−3	1.000
34	SNORD103B	small nucleolar RNA, C/D box 103B	2.34 × 10−6	0.338	−1.417 × 10−3	1.000
35	SNORD127	small nucleolar RNA, C/D box 127	3.36 × 10−6	0.511	−1.002 × 10−3	1.000
36	SNORD103A	small nucleolar RNA, C/D box 103A	4.06 × 10−6	0.354	−1.379 × 10−3	1.000
37	SCARNA13	small Cajal body-specific RNA 13	4.13 × 10−6	0.689	−4.895 × 10−4	1.000
38	SNORA14B	small nucleolar RNA, H/ACA box 14B	4.66 × 10−6	0.592	−7.310 × 10−4	1.000
39	KIAA1549L	KIAA1549 like	5.44 × 10−6	0.178	−1.223 × 10−3	0.980
40	SNORD119	small nucleolar RNA, C/D box 119	5.58 × 10−6	0.427	−1.048 × 10−3	1.000
41	PDCD4	programmed cell death 4	9.40 × 10−6	0.654	−5.442 × 10−4	1.000
42	MIR181A1	microRNA 181a-1	9.42 × 10−6	0.112	−1.680 × 10−3	0.980
43	SCARNA9	small Cajal body-specific RNA 9	1.14 × 10−5	0.539	−8.278 × 10−4	1.000
44	RP1-102E24.1	Unmatched	1.19 × 10−5	0.315	-1.006 × 10−3	0.939
45	PRDM13	PR/SET domain 13	2.46 × 10−5	0.140	−1.674 × 10−3	0.959
46	SNORD19	small nucleolar RNA, C/D box 19	3.45 × 10−5	0.541	−7.859 × 10−4	1.000
47	SNORA26	small nucleolar RNA, H/ACA box 26	3.67 × 10−5	0.425	−1.156 × 10−3	1.000
48	RNU2-36P	RNA, U2 small nuclear 36, pseudogene	4.80 × 10−5	0.401	−9.650 × 10−4	0.959
49	SNORA50A (SNORA50)	small nucleolar RNA, H/ACA box 50A	4.84 × 10−5	0.475	−9.165 × 10−4	0.959
50	SNORA40	small nucleolar RNA, H/ACA box 40	5.31 × 10−5	0.394	−1.075 × 10−3	0.959
51	SNORD1B	small nucleolar RNA, C/D box 1B	8.82 × 10−5	0.333	−1.285 × 10−3	0.959

The table presents p (FDR with Benjamini–Hochberg correction) and fold change values received from DESeq2 analysis, PLS coefficients received from UVE-PLS analysis and areas under receiver operating characteristics (ROC) curves (ROC-AUC) received from ROC analysis. Genes were divided into upregulated and downregulated groups and ordered according to increasing p value. Gene symbols without assigned gene names by a Human Genome Organization (HUGO) Multi-Symbol Checker (https://www.genenames.org/tools/multi-symbol-checker/) were termed as “unmatched”. Gene symbols in brackets are synonyms or previous gene symbols.

and Table S8, Figure S8). Therefore, these 51 genes was considered as a panel of transcriptomic biomarkers of AAA (Table 3).

Deconvolution procedure revealed estimated proportions of 11 cell subpopulations in PBMCs specimens subjected to transcriptome analysis. The “quanTIseq” method enables to obtain comparisons between cell types and specimens (Figure S9) and “MCPcounter” method provides further information in differences between samples (Figure S10). Certain differences in proportions of 11 cell subpopulations between samples were noticed; however, further statistics suggests that cell subpopulations composition in PBMCs specimens had no significant influence on the study outcome.

3.5. Correlation Analysis

Demographical characteristics (age, BMI), clinical parameters (maximum aneurysm diameter, thrombus volume and aneurysm neck length) and expression data of 34 selected miRNA transcripts and 51 selected genes of AAA group were included to the correlation analysis (

Table 4. Correlation analysis between maximum aneurysm diameter, thrombus volume, aneurysm neck length, age, body mass index (BMI) and expression of 33 selected miRNA transcripts and 51 selected genes identified as potential abdominal aortic aneurysm signatures. MiRNA transcripts and genes with at least one statistically significant correlation (p < 0.05) were presented. All correlations results are provided in Table S9 and S10 in Supplementary File.

miRNA Transcript/Gene	Maximum Aneurysm Diameter		Thrombus Volume		Aneurysm Neck Length		Age		BMI
	R	p	R	p	R	p	R	p	R	p
hsa-mir-122_hsa-miR-122-5p	0.10	0.619	0.27	0.160	−0.05	0.782	0.10	0.618	−0.38 1	0.045
hsa-mir-125b-1_hsa-miR-125b-5p	0.02	0.926	−0.19	0.341	0.45 1	0.015	0.08	0.692	−0.01	0.954
hsa-mir-125b-2_hsa-miR-125b-5p	0.12	0.560	−0.08	0.686	0.40 1	0.037	0.09	0.662	0.02	0.901
hsa-mir-34a_hsa-miR-34a-5p	0.47 1	0.011	0.32	0.096	−0.04	0.852	0.26	0.183	-0.01	0.961
hsa-mir-3591_hsa-miR-3591-3p	0.10	0.616	0.27	0.160	−0.05	0.781	0.10	0.617	−0.38 1	0.045
hsa-mir-574_hsa-miR-574-5p	0.16	0.421	0.49 1	0.007	−0.03	0.896	0.26	0.180	0.16	0.414
hsa-mir-769_hsa-miR-769-5p	−0.22	0.252	−0.04	0.832	0.36	0.061	−0.41 1	0.032	0.01	0.973
hsa-mir-7847_hsa-miR-7847-3p	0.33	0.089	−0.01	0.944	0.13	0.521	0.53 1	0.003	-0.03	0.884
AC092620.2	0.32	0.482	−0.39	0.389	0.69	0.085	0.81 1	0.028	0.19	0.688
GIT2	0.20	0.666	−0.37	0.415	0.64	0.120	0.27	0.563	0.81 1	0.027
PDCD4	−0.14	0.768	0.03	0.945	−0.07	0.885	−0.81 1	0.026	−0.18	0.699
RP1-102E24.1	0.30	0.508	0.38	0.396	0.08	0.864	−0.18	0.703	−0.78 1	0.039
RPL3P9	0.05	0.911	0.14	0.757	−0.42	0.344	−0.17	0.713	−0.76 1	0.046
SNHG5	−0.01	0.976	0.32	0.490	−0.22	0.633	−0.80 1	0.030	−0.19	0.685
SUFU	0.48	0.278	−0.24	0.597	0.48	0.274	0.77 1	0.041	0.41	0.357
ZRANB2	−0.35	0.448	−0.19	0.679	−0.21	0.649	−0.79 1	0.036	0.08	0.857

R—Spearman correlation coefficient, ¹ correlations statistically significant (p < 0.05).

, broaden results are provided in Table S9 and S10). Among demographical and clinical characteristics, statistically significant and weak positive correlation was indicated between age and maximum aneurysm diameter (R = 0.42, p = 0.025), as well as between BMI and thrombus volume (R = 0.38, p = 0.045) (Table S9). Two upregulated miRNAs: hsa-miR-34a-5p and hsa-miR-574-5p were positively correlated with maximum aneurysm diameter and thrombus volume, respectively, what make them potential targets for AAA prognosis. Hsa-miR-769-5p and hsa-miR-7847-3p are associated with age, pointing them as possible age-associated risk factors of AAA. Statistically significant correlations between genes and age (AC092620.2, PDCD4, SNHG5, SUFU, ZRANB2) as well as BMI (GIT2, RP1-102E24.1, RPL3P9) were also revealed (Table 4). Relatively low number of samples does not allow to make categorical conclusions, therefore further studies with larger populations should be performed to confirm these results. To evaluate effects of smoking, coronary artery disease, diabetes mellitus type 2 and hypertension presence in AAA group on miRNA and gene expression, DESeq2 method was applied to find differentially expressed miRNAs and genes in AAA subjects with these conditions in comparison to AAA subjects without them and any of analyzed miRNAs and genes was statistically significantly differentially expressed. This result suggests, that miRNAs and genes identified as potential biomarkers of AAA do not depend on smoking, coronary artery disease, diabetes mellitus type 2 and hypertension; however, this finding should be confirmed in further studies.

3.6. In Silico Identification of miRNA:Gene Interactions

Identification of validated and predicted miRNA:gene interactions between 31 miRNAs and 51 genes revealed as potential biomarkers of AAA was processed by multiMiR package. In the analysis, five validated miRNA:gene pairs (Table S11) and 60 top 10% predicted miRNA:gene pairs (Table S12) were returned. Received interactions were visualized on regulatory network generated using Cytoscape 3.5.1 software (Figure 4).

3.7. Functional Analysis of miRNA Targets

Functional analysis of 18 target genes (ANKRD13D, CPT1A, GGT1, GIT2, HTT, KIAA1549L, NBEAL2 PDCD4, PRDM13, SNORA60, SNORD94, SUFU, THOC5, UBE4B, UPF1, ZRANB2, ZSWIM8, and ZZEF1) included in the regulatory network was performed using DAVID 6.8 tools and resulted associations are presented in

Table 5. Functional analysis of eighteen networked miRNA targets.

Functional Analysis of 12 Upregulated Genes (ANKRD13D, CPT1A, GGT1, GIT2, HTT, NBEAL2, SUFU, THOC5, UBE4B, UPF1, ZSWIM8, and ZZEF1)
KEGG, Reactome, GAD and GAD Class
ANKRD13D	GAD: Type 2 Diabetes|edema|rosiglitazone GAD Class: pharmacogenomic
CPT1A	KEGG: fatty acid degradation, fatty acid metabolism, PPAR signaling pathway, AMPK signaling pathway, adipocytokine signaling pathway, glucagon signaling pathway, insulin resistance Reactome: RORA activates gene expression, PPARA activates gene expression, import of palmitoyl-CoA into the mitochondrial matrix, signaling by retinoic acid GAD: acquired immunodeficiency syndrome|disease progression, Alzheimer’s disease, atherosclerosis, BMI–Edema rosiglitazone or pioglitazone, diabetes, type 2 hepatic lipid content insulin, hepatitis C, chronic, hypercholesterolemia|LDLC levels, left ventricular hypertrophy, lipid metabolism, inborn errors|sudden infant death, obesity, tunica media, type 2 diabetes|edema|rosiglitazone GAD Class: cardiovascular, infection, metabolic, neurological, pharmacogenomic, unknown
GGT1	KEGG: taurine and hypotaurine metabolism, cyan amino acid metabolism, glutathione metabolism, arachidonic acid metabolism, metabolic pathways Reactome: glutathione synthesis and recycling, synthesis of leukotrienes (LT) and eoxins (EX), aflatoxin activation and detoxification, defective GGT1 causes glutathionuria (GLUTH) GAD: aging/ telomere length, alkaline phosphatase, arsenic exposure, cognitive trait, fatty liver|metabolic syndrome X, gamma-glutamyltransferase, liver enzymes, normal variation, pancreatic neoplasm|pancreatic neoplasms, plasma levels of liver enzymes, protein quantitative trait loci, sleep apnea, obstructive GAD class: cardiovascular
GIT2	KEGG: endocytosis GAD: cholesterol, HDL, E-selectin GAD Class: metabolic
HTT	KEGG: Huntington’s disease GAD: atrophy|Huntington’s disease, chronic progressive chorea|, cognitive ability, cognitive function, Huntington’s disease; ataxia (SCA), myotonic dystrophy type 1, null, Parkinson’s disease, prostatic neoplasms, psychiatric disorders, schizophrenia, sleep disorders; Tourette syndrome, suicide GAD Class: cancer, neurological, other, psych, unknown
NBEAL2	GAD: Schizophrenia GAD Class: psych
SUFU	KEGG: hedgehog signaling pathway, pathways in cancer, Basal cell carcinoma Reactome: degradation of GLI1 by the proteasome, Degradation of GLI2 by the proteasome, GLI3 is processed to GLI3R by the proteasome, hedgehog ‘off’ state, hedgehog ‘on’ state GAD: Alzheimer’s disease, head and neck neoplasms|neoplasm recurrence, local|neoplasms, second primary GAD Class: cancer, neurological
THOC5	KEGG: RNA transport Reactome: transport of mature mRNA derived from an intron-containing transcript, mRNA 3’-end processing GAD: carotid atherosclerosis in HIV infection GAD Class: cardiovascular
UBE4B	KEGG: ubiquitin mediated proteolysis, protein processing in endoplasmic reticulum GAD: arteries, carcinoma, hepatocellular|hepatitis B, chronic|LCC—liver cell carcinoma|liver neoplasms GAD Class: cancer, cardiovascular
UPF1	KEGG: RNA transport, mRNA surveillance pathway Reactome: nonsense mediated decay (NMD) independent of the exon junction complex (EJC), nonsense mediated decay (NMD) enhanced by the exon junction complex (EJC)
ZSWIM8	GAD: Alzheimer’s disease GAD Class: neurological
ZZEF1	GAD: tobacco use disorder GAD Class: chemdependency
Gene Ontology terms associated with EASE score < 0.05
GO Biological Process	Cellular catabolic process, regulation of cellular catabolic process, organic substance catabolic process, catabolic process, regulation of catabolic process, intracellular transport, positive regulation of cellular catabolic process, establishment of localization in cell, positive regulation of catabolic process, cellular response to stimulus, positive regulation of lipid catabolic process, nucleocytoplasmic transport, nuclear transport, cellular localization, cellular developmental process, regulation of lipid catabolic process, behavior, response to stimulus, single-organism intracellular transport, nitrogen compound transport, animal organ development, mRNA-containing ribonucleoprotein complex export from nucleus, mRNA export from nucleus
GO Cellular Compartment	Membrane-bounded organelle, nucleoplasm
Functional analysis of 6 downregulated gene (KIAA1549L, PDCD4, PRDM13, SNORA60, SNORD94, and ZRANB2)
KEGG, Reactome, GAD and GAD Class
KIAA1549L	GAD: alcoholism, body height, creatinine, heart rate, suicide, attempted GAD Class: cardiovascular, chemdependency, developmental, metabolic, psych
PDCD4	KEGG: proteoglycans in cancer, microRNAs in cancer GAD: Alzheimer’s disease, longevity GAD Class: aging, neurological
PRDM13	GAD: menarche, Parkinson’s disease GAD Class: neurological, reproduction
SNORA60	No information
SNORD94	No information
ZRANB2	No information
Gene Ontology terms associated with EASE score <0.05
GO Biological Process	Regulation of transcription, DNA-templated, regulation of nucleic acid-templated transcription, regulation of RNA biosynthetic process, regulation of RNA metabolic process, nucleic acid-templated transcription, RNA biosynthetic process, regulation of cellular macromolecule biosynthetic process, regulation of nucleobase-containing compound metabolic process, regulation of macromolecule biosynthetic process, regulation of cellular biosynthetic process, regulation of biosynthetic process, regulation of gene expression, nucleobase-containing compound biosynthetic process, regulation of nitrogen compound metabolic process, heterocycle biosynthetic process, aromatic compound biosynthetic process, organic cyclic compound biosynthetic process, RNA metabolic process, cellular nitrogen compound biosynthetic process, cellular macromolecule biosynthetic process, nucleic acid metabolic process, macromolecule biosynthetic process, gene expression
GO Molecular Function	Nucleic acid binding

Analysis was performed using DAVID 6.8 database and following categories: Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, Genetic Association Database (GAD), Genetic Association Database Class (GAD Class) and Gene Ontology (GO).

.

Analyzed genes were associated with cardiovascular diseases (CPT1A, THOC5, KIAA1549L), diabetes (ANKRD13D, CPT1A), lipids metabolism (CPT1A, GIT2), inflammation mediators (GGT1), glutathione metabolism (GGT1), aging (GGT1, PDCD4), cancer (HTT, SUFU, UBE4B, PDCD4), RNA transport and processing (THOC5, UPF1), proteolysis (SUFU, UBE4B), chemical dependency (ZZEF1, KIAA1549L)

Seven out of 18 genes are associated with neurological diseases: Alzheimer’s disease (CPT1A, SUFU, ZSWIM8, PDCD4), schizophrenia (HTT, NBEAL2) and Parkinson disease (PRDM13).

GO enrichment analysis assigned upregulated genes (ANKRD13D, CPT1A, GGT1, GIT2, HTT, NBEAL2, SUFU, THOC5, UBE4B, UPF1, ZSWIM8, and ZZEF1) to positive regulation of cellular catabolic process, mRNA transport and developmental processes, while downregulated genes (KIAA1549L, PDCD4, PRDM13, SNORA60, SNORD94, and ZRANB2) were associated with RNA biosynthesis and gene expression (Table 5).

4. Discussion

Searching for precise and robust biomarkers of early AAA is crucial due to very subtle symptoms of the disease and high mortality of ruptured aneurysm. Examining deregulations in miRNA network and consequential effects on gene expression appears as an interesting research tactics for finding novel biomarkers of AAA [25,26,27].

In the presented study, we performed integrated analysis of miRNAome and transcriptome expression in PBMC specimens obtained from patients with AAA and healthy controls. The PBMCs pool is involved in inflammation, an important element of AAA pathology, and therefore, should provide an abundance of information about condition and disorders of vascular system. Moreover, high accessibility of PBMCs facilitates translation of obtained results into clinical practice.

Application of next generation sequencing and multi-stage statistical methodology allowed to select 31 miRNAs (Table 2) and 51 genes (Table 3) as the most favorable candidates for detection of AAA (Figure 1). Selection of proposed biomarkers was preceded by control of potential false positive results through adopting the higher threshold of statistical significance (p < 0.0001, adjusted by Benjamini–Hochberg false discovery rate) and eliminating uninformative variables using UVE-PLS. High diagnostic value of proposed biomarkers was confirmed in ROC analysis (Table 2 and Table 3, Tables S5 and S8, Figures S6 and S8). Such stringent criteria applied for biomarkers selection were introduced due to the inability to predict in advance the number of miRNA/genes that should be validated by qPCR, thus allowing us not to design proper experiment accordingly.

There is a limited number of studies regarding miRNA expression investigations by next generation sequencing in PBMC samples in AAA individuals [26]. The comparison of our results and findings obtained in similar studies is presented in

Table 6. The most relevant studies regarding differentially expressed miRNAs in abdominal aortic aneurysm (AAA), with results overlapping findings of the current study.

Ref.	Cases vs Controls	Material	Method (Number of Differentially Expressed miRNAs)	MiRNAs Overlapping with miRNA Biomarkers Proposed in the Current Study
[42]	6 AAA subjects vs 6 controls	Abdominal aorta tissues	qPCR (59)	let-7g-3p, miR-454-3p, -24-3p, -31-5p, -125b-5p, -150-5p, -99a-3p
[43]	169 AAA subjects vs 48 controls	Plasma	qPCR (103)	miR-454-3p, -122-5p, -424-5p, -766-3p
[44]	15 AAA subjects vs 10 non-AAA controls	Whole blood samples	qPCR (29)	miR-125b-5p, -138-5p
[45]	10 AAA subjects vs 10 controls	Plasma	Microarray (151)	miR-21-5p, -574-5p, -24-3p, -122-5p, -31-5p, -342-3p, -150-5p, -125b-5p, -339-3p
[46]	5 AAA subjects vs 5 controls	Infrarenal aortic tissues	Microarray (8)	miR-21-5p

The table presents studies on miRNAs in AAA. Differentially expressed miRNAs are revealed from AAA subjects and control groups. MiRNAs overlapping with biomarkers proposed in the current study are shown. For more comprehensive review of this topic please refer to [26].

. Relatively poor overlap with literature data and our miRNAs list could be explained by differences in methodology applied and biological material subjected to experiments.

In depth discussion regarding functions of DEMs (differentially expressed miRNAs) of this research vastly exceeds capacity of the current paper.

Table A1. Selected most prominent targets and processes affected by differentially expressed miRNAs from presented study, drawn from literature analysis.

MiRNAs Reported in the Present Study as Upregulated in AAA
miRNA	Remarks
hsa-miR-21	Function in atherogenesis [28,46,49] and AAA [48], targets PTEN [50,51,52].
hsa-miR-24	Downregulated in plasma of AAA patients and murine AAA models [67].
hsa-miR-34a	Was deregulated in abdominal aorta tissue of AAA animal models [67].
hsa-miR-122	Role in Alzheimer’s disease through regulation of genes involved in lipid metabolism [68].
hsa-miR-424	Negative regulator of EGFR expression in tumor cells [60], targets Rictor (mTOR complex 2 signaling element), promotes tumor progression [69], affects MAPK and focal adhesion signaling pathways in esophageal squamous cell carcinoma [70].
hsa-miR-450b	Affects MAPK and focal adhesion signaling pathways in esophageal squamous cell carcinoma [70].
hsa-miR-454	Directly targets PTEN [71], promotes cancer progression [71,72], inhibits Wnt/β-catenin signaling [72].
hsa-miR-503	Targets Rictor (mTOR complex 2 signaling element), promotes tumor progression [69], promotes ESCC cell proliferation, migration, and invasion by targeting cyclin D1 [73], negative regulator of proliferation in primary human cells [74].
hsa-miR-542	Upregulated in AAA patients [42].
hsa-miR-548d	Associated with schizophrenia [75].
hsa-miR-574	Circulating marker of TAA [76], repressor of VEGFA [77], promotes VSMCs growth in CAD [78].
hsa-miR-3591	Lower extremities arterial disease-associated miRNA [28].
MiRNAs reported in the present study as downregulated in AAA
hsa-let-7g	Increases viability of lung cancer and osteosarcoma cells via downregulation of HOXB1 and activation of NF-kB pathway [79,80].
hsa-miR-31	Knockdown of this miRNA inhibits expression of Collagen I and III and Fibronectin in hypertrophic scar formation [81], regulator of senescence in cancer cells [82].
hsa-miR-99a	Significantly decreased in patients with AMI [83], regulates cell migration and cell proliferation by targeting PI3K/AKT and mTOR in wound healing model [84].
hsa-miR-125b	Associated with immune response of patients with ruptured intracranial aneurysms [85], upregulated in AAA subjects [44], suppresses bladder cancer development by targeting SIRT7 and MALAT1 [86].
hsa-miR-138	Promotes glioma angiogenesis through miR-138/HIF-1α/VEGF axis [87], upregulated after the induction of myocardial infarction [88].
hsa-miR-150	Inactivates VEGFA/VEGFR2 and the downstream Akt/mTOR signaling pathway in colorectal cancer [89], marker for early diagnosis of AMI [90], underexpression of this miRNA promotes proliferation and metastasis of gastric cancer [91].
hsa-miR-339	Overexpression of this miRNA can inhibit HCC cell invasion [92].
hsa-miR-342	Marker of T2D patients with high risk for developing CAD [93], in hUCMSCs enhances osteogenesis by targeting SUFU, induces TGF-β expression [94], regulates cell proliferation and apoptosis in hepatocellular carcinoma through Wnt/β-catenin signaling pathway [95].
hsa-miR-361	Overexpression in cutaneous leishmaniosis lesions, impairs epidermal barrier function by filaggrin-2 repression [96].
hsa-miR-766	Indirectly inhibits of NF-κB signaling causing anti-inflammatory response [97].
hsa-miR-769	Expression is significantly correlated with the presence of pronounced coronary atherosclerosis [98], inhibits colorectal cancer cell proliferation and invasion by targeting HEY1 (downstream effector of NOTCH signaling pathway) [99], negatively correlated with EGFR expression [100].
hsa-miR-874	Decreased expression was associated with poor overall survival of ESCC patients, targets STAT3 [101].
hsa-miR-5585	Regulates cell cycle progression in human colorectal carcinoma cells, decreases expression of TGFβ-R1, TGFβ-R2, SMAD3, and SMAD4 [102].

AAA—aortic abdominal aneurysm, AMI—acute myocardial infraction, CAD—coronary artery disease, EGFR—endothelial growth factor receptor, ESCC—esophagal squamous cells carcinoma, HCC—human colorectal cancer, HEY1—hairy/enhancer-of-split related with YRPW motif protein 1, HIF-1α—hypoxia induced factor 1α, HOXB1—homeobox B1, hUCMSCs—human umbilical cord mesenchymal stem cells, MALAT1—metastasis associated lung adenocarcinoma transcript 1, MAPK—mitogen activated protein kinase, mTOR—mammalian target of rapamycin, NF-κB—necrotic factor κB, NOTCH—translocation-associated protein, PI3K/AKT—phosphoinositide 3-kinases/protein kinase B, PTEN—phosphatase and tensin homolog deleted on chromosome ten, T2D—type 2 diabetes, TAA—thoracic aortic aneurysm, TGF-β—tumor growth factor β, TGFβ-R1—tumor growth factor β receptor 1, TGFβ-R2—tumor growth factor β receptor 2, SIRT7—sirtuin 7, SMAD3—decapentaplegic homolog 3, SMAD4—decapentaplegic homolog 4, STAT3—signal transducer and activator of transcription 3, SUFU—suppressor of fused homolog, Wnt—wingless-type MMTV integration site family of genes, VEGF—vascular endothelial growth factor, VEGFA—vascular endothelial growth factor A, VEGFR2—vascular endothelial growth factor receptor 2, VSMCs—vascular smooth muscle cells.

in Appendix B focuses on most important information regarding possible mechanisms of presented miRNAs actions in AAA. The set of revealed miRNAs clearly points to deregulation of numerous signaling pathways like mTOR, PI3K/AKT, TGF-β, NOTCH, MAPK, and NF-κB. Those affect general processes like cell adhesion, proliferation and motility as well as more detailed ones like wound healing and vascular growth. Taken together, this may point to processes engaged in AAA onset and development like vascular wall remodeling, hypoxia, subsequent revascularization and hemorrhage. On the other hand it is worth to draw attention to selected miRNAs, due to their engagement in regulation of genes, occurring in our sequencing data.

Expression levels of miR-21 may vary between types of biological material. In AAA aortic tissue is significantly upregulated [46] whereas in plasma of AAA patients it may be downregulated [47]. We observed exhibited upregulation of miR-21, suggesting that PBMCs may better reflect processes ongoing in affected aortic tissue. Higher levels of miR-21 were observed in low inflammatory state abdominal aneurysms compared to high inflammation phase aneurysm [48]. On the other hand, elevated level of miR-21 in macrophages promote apoptosis and vascular inflammation in atherogenesis [49] and was shown as a biomarker of low extremities arterial disease [28]. Those facts suggests more detailed assessment of miR-21 function in inflammation, atherogenesis and AAA in the future.

Overexpression of miR-21 leads to downregulation of PDCD4 and PTEN inducing cell proliferation, decreasing apoptosis in the aortic wall, alleviating aneurysm expansion and protect against cell injury caused by hydrogen peroxide exposure [50,51,52]. PDCD4 is involved in atherosclerosis pathology probably through enhancing levels of IL-6 and IL-8 and promoting apoptosis of VSMC in animal models of coronary atherosclerosis [53]. In our study, both upregulation of miR-21-5p and downregulation of PDCD4 were observed in PBMCs samples of AAA subjects, suggesting pro-inflammatory and antiapoptotic effects in AAA.

We confirmed findings of Lenk et al. [54] demonstrating upregulation of GGT1 in patients with AAA. This phenomenon was also reported as a signature of low extremities arterial disease (LEAD) [28], suggesting non-specific character of GGT1 deregulation.

There was no overlap between our findings and gene expression biomarkers of AAA found in some other studies [55,56,57]. The differences in results may stem from dissimilarities in study material, criteria for participants inclusion and methodological approaches.

Integration of miRNA and gene expression analysis enabled identification of miRNA:gene regulatory pairs in AAA, presented on regulatory network (Figure 4). Elevated expression of UBE4B may be caused by many miRNAs selected as biomarkers of AAA (Figure 4) and lead to aneurysm expansion through inhibition of endothelial growth factor receptor (EGFR)-mediated proliferation of vascular cells by enhancing EGFR degradation [58]. Upregulation of ANKRD13D may affect endocytic trafficking of EGFR by inhibition of its ubiquitinated form from the cell surface, attenuating pro-proliferative signaling of internalized EGFR [59], potentially aggravating AAA. EGFR signaling might be compromised also by upregulation of miR-424-5p, which is a negative regulator of EGFR expression, as observed in tumor cells [60]. According to bioinformatic analysis, this miRNA might be a regulator of both UBE4B and ANKRD13D, affecting also EGFR expression [60]. This net of reciprocal regulations may be considered as a part of a mechanism decreasing cell proliferation in AAA through modulation of EGFR signaling.

The preliminary functional analysis of genes regulated by miRNAs revealed terms closely related to vascular pathology, including lipid metabolism, inflammation, atherosclerosis and aging (Table 5). Interestingly, there were seven genes associated with neurological disorders, including Alzheimer’s disease (CPT1A, SUFU, ZSWIM8, PDCD4), schizophrenia (HTT, NBEAL2) and Parkinson’s disease (PRDM13). For further comment on neurological relationships of our findings please refer to Appendix C.

The highly enriched terms associated with genes regulated by biomarker miRNAs like RNA biosynthesis and transport, positive regulation of catabolic processes and developmental processes suggest more general mechanisms also involved in control of gene expression in AAA.

We are aware of limitations of our study design. It was not established, whether alterations in expression of miRNAs and genes proposed as biomarkers were predictive or responsive to AAA development. Although proposed biomarkers were characterized by high predictive value, supported by high level of statistical significance, multistage selection and ROC confirmation, the clinical application requires confirmation in studies with larger cohorts. PBMCs consists of lymphocytes and monocytes subpopulations, varying in miRNA and gene expression patterns. Nuances in proportions of these subpopulations may affect diagnostic value of indicated biomarkers, thus this effect should be further investigated.

Co-existing diseases may bias evaluation of PBMCs expression profiles on a systemic scale. For this reason many conditions were established as exclusion criteria, as mentioned in experimental section. Such strict evaluation helped us to find expression patterns potentially reflecting the local changes in AAA; however, it entailed statistically significant differences in demographic characteristics between AAA and control groups (Table 1). Differences in gender, age and smoking habits might have potentially influenced the study outcome.

The AAA group in our study has men overrepresentation (89.3%, 25 patients) whereas control group is more sex-balanced and include 47% of healthy men (9 subjects) (Table 1). Those characteristics may potentially introduce a gender-associated bias into our data. Some of proposed miRNA biomarkers of AAA have already been connected to sex differences in humans (

Table A2. Associations of miRNAs proposed in our study as abdominal aortic aneurysm (AAA) biomarkers with gender and aging, found in the most relevant literature.

miRNAs	Previous studies reported association with gender	Previous studies reported association with aging	Previous studies reported association with smoking
miRNAs reported in the present study as upregulated in AAA
hsa-miR-21	[105]	[106]
hsa-miR-24		[106,107]
hsa-miR-34a		[108]
hsa-miR-122		[106]
hsa-miR-424	[63,105]	[106]
hsa-miR-450b
hsa-miR-454	[63]
hsa-miR-503		[106]
hsa-miR-542
hsa-miR-548d		[106]
hsa-miR-574			[109]
hsa-miR-3591
miRNAs reported in the present study as downregulated in AAA
hsa-let-7g		[106,107]
hsa-miR-31
hsa-miR-99a	[63]	[110]
hsa-miR-125b		[106]
hsa-miR-138		[106,107]	[111]
hsa-miR-150	[63,105]
hsa-miR-339	[63]
hsa-miR-342	[105]
hsa-miR-361	[63]
hsa-miR-766		[106]	[112]
hsa-miR-769	[63]
hsa-miR-874		[106]
hsa-miR-5585

in Appendix D), suggesting that deregulation of these miRNAs may reflect gender differences occurring between AAA and control group. In the case of gene expression, comparison of Deegan et al. paper [61] draw only five overlapping genes, suggesting minor gender bias, while from [62] there were no such ones (

Table A3. Associations of genes proposed in our study as AAA biomarkers with gender and smoking, found in the most relevant literature.

No.	Gene Symbol	Association with Gender		Association with Smoking
		[61] 1	[62]	[113] 2	[114] 3	[115] 4	[116] 5	[117] 5	[118] 5	[119]	[120]
Upregulated genes
1	CPT1A	no	no	no	no	no	no	no	no	no	no
2	GGT1	no	no	no	no	no	no	no	no	no	no
3	UPF1	yes, f	no	no	no	no	no	no	no	no	no
4	AC092620.2	no	no	no	no	no	no	no	no	no	no
5	UBE4B	yes, f	no	no	no	no	no	no	no	no	no
6	HTT	no	no	no	no	no	no	no	no	no	no
7	NBEAL2	no	no	no	no	no	no	no	no	no	no
8	GIT2	no	no	no	no	no	no	no	no	no	no
9	THOC5	no	no	no	no	no	no	no	no	no	no
10	ZZEF1	no	no	no	no	no	no	no	no	no	no
11	ANKRD13D	no	no	no	no	no	no	no	no	no	no
12	SUFU	no	no	no	no	no	no	no	no	no	no
13	RN7SKP89	no	no	no	no	no	no	no	no	no	no
14	ZSWIM8	no	no	no	no	no	no	no	no	no	no
Downregulated genes
15	SNORA60	no	no	no	no	no	no	no	no	no	no
16	MIRLET7F2	no	no	no	no	no	no	no	no	no	no
17	SNHG5	no	no	no	no	no	no	no	no	no	no
18	SNORD20	no	no	no	no	no	no	no	no	no	no
19	SNORA72	no	no	no	no	no	no	no	no	no	no
20	SNORD117	no	no	no	no	no	no	no	no	no	no
21	SNORD82	yes, m	no	no	no	no	no	no	no	no	no
22	SNORD94	no	no	no	no	no	no	no	no	no	no
23	SNORD101	no	no	no	no	no	no	no	no	no	no
24	RNA5SP355	no	no	no	no	no	no	no	no	no	no
25	SNORD103C (SNORD85)	no	no	no	no	no	no	no	no	no	no
26	RPL3P9	no	no	no	no	no	no	no	no	no	no
27	RP11-16F15.2	no	no	no	no	no	no	no	no	no	no
28	RP11-302F12.1	no	no	no	no	no	no	no	no	no	no
29	SNORA12	no	no	no	no	no	no	no	no	no	no
30	SNORA33	no	no	no	no	no	no	no	no	no	no
31	ZRANB2	yes, m	no	no	no	no	no	no	no	no	no
32	SNORD91B	no	no	no	no	no	no	no	no	no	no
33	RP11-253E3.1	no	no	no	no	no	no	no	no	no	no
34	SNORD103B	no	no	no	no	no	no	no	no	no	no
35	SNORD127	no	no	no	no	no	no	no	no	no	no
36	SNORD103A	no	no	no	no	no	no	no	no	no	no
37	SCARNA13	no	no	no	no	no	no	no	no	no	no
38	SNORA14B	no	no	no	no	no	no	no	no	no	no
39	KIAA1549L	no	no	no	no	no	no	no	no	no	no
40	SNORD119	no	no	no	no	no	no	no	no	no	no
41	PDCD4	yes, m	no	no	no	no	no	no	no	no	no
42	MIR181A1	no	no	no	no	no	no	no	no	no	no
43	SCARNA9	no	no	no	no	no	no	no	no	no	no
44	RP1-102E24.1	no	no	no	no	no	no	no	no	no	no
45	PRDM13	no	no	no	no	no	no	no	no	no	no
46	SNORD19	no	no	no	no	no	no	no	no	no	no
47	SNORA26	no	no	no	no	no	no	no	no	no	no
48	RNU2-36P	no	no	no	no	no	no	no	no	no	no
49	SNORA50A (SNORA50)	no	no	no	no	no	no	no	no	no	no
50	SNORA40	no	no	no	no	no	no	no	no	no	no
51	SNORD1B	no	no	no	no	no	no	no	no	no	no

¹ Heart tissue gender bias only, ² xenobiotic metabolism genes only, ³ CpG island methylation only, full data comparison, ⁴ genes associated with smoking behavior, ⁵ full data comparison, f—female biased, m—male biased, no—there is no association between presented data and literature, yes—there is an association between presented data and literature.

in Appendix E). Interestingly, Cui et al. discovered that blood is poor material for distinguishing sex associated transcriptome patterns due to relative invariability between genders [63].

The AAA and control groups were age-unmatched (66.39 ± 4.52 years and 36.58 ± 9.97 years, respectively) (Table 1). Literature analysis revealed that some of proposed miRNA biomarkers of AAA may also be considered as senescence indicators (Table A2 in Appendix D). On the other hand, gene expression in AAA has no or little overlap with senescence/aging biomarkers. One of the proposed AAA biomarkers, SNORA33, has been previously associated with normal human aging [64]. In a comprehensive transcriptomic analysis of eight senescence in vitro models [65], 20 out of 51 genes reported in our study were also differentially expressed in senescent cells (

Table A4. Associations of genes proposed in our study as AAA biomarkers with aging, found in the most relevant literature.

No.	Gene Symbol	Associations with Aging:
		[121] 1	[122]	[64]	[123]	[65] 1	Remarks for [65]
Upregulated Genes
1	CPT1A	no	no	no	no	yes	IMR90 IR, IMR90 Rep, HUVEC IR, HAEC IR, WI38 Onc
2	GGT1	no	no	no	no	yes	WI38 IR, WI38 Onc, WI38 Dox, IMR90 IR, WI38 Rep, HUVEC IR
3	UPF1	no	no	no	no	yes	IMR90 Rep, IMR90 IR, WI38 Onc,
4	AC092620.2	no	no	no	no	yes	WI38 Onc
5	UBE4B	no	no	no	no	yes	IMR90 Rep, IMR90 IR
6	HTT	no	no	no	no	yes	IMR90 Rep, WI38 Onc, IMR90 IR
7	NBEAL2	no	no	no	no	yes	WI38 Onc, HAEC IR, WI38 Dox
8	GIT2	no	no	no	no	yes	IMR90 Rep, WI38 Dox, IMR90 IR, WI38 Onc
9	THOC5	no	no	no	no	no
10	ZZEF1	no	no	no	no	no
11	ANKRD13D	no	no	no	no	no
12	SUFU	no	no	no	no	yes	HUVEC IR
13	RN7SKP89	no	no	no	no	no
14	ZSWIM8	no	no	no	no	yes	HUVEC IR, IMR90 IR, IMR90 Rep
Downregulated genes
15	SNORA60	no	no	no	no	no
16	MIRLET7F2	no	no	no	no	no
17	SNHG5	no	no	no	no	yes	HUVEC IR
18	SNORD20	no	no	no	no	no
19	SNORA72	no	no	no	no	no
20	SNORD117	no	no	no	no	no
21	SNORD82	no	no	no	no	no
22	SNORD94	no	no	no	no	yes	WI38 Dox
23	SNORD101	no	no	no	no	yes	HAEC IR, WI38 Dox, WI38 Onc, HUVEC IR
24	RNA5SP355	no	no	no	no	no
25	SNORD103C (SNORD85)	no	no	no	no	no
26	RPL3P9	no	no	no	no	no
27	RP11-16F15.2	no	no	no	no	yes	WI38 Rep
28	RP11-302F12.1	no	no	no	no	no
29	SNORA12	no	no	no	no	no
30	SNORA33	no	no	yes	no	no
31	ZRANB2	no	no	no	no	no
32	SNORD91B	no	no	no	no	no
33	RP11-253E3.1	no	no	no	no	no
34	SNORD103B	no	no	no	no	no
35	SNORD127	no	no	no	no	no
36	SNORD103A	no	no	no	no	no
37	SCARNA13	no	no	no	no	no
38	SNORA14B	no	no	no	no	yes	WI38 Dox, WI38 Onc
39	KIAA1549L	no	no	no	no	yes	WI38 Dox, IMR90 Rep, IMR90 IR, WI38 IR, WI38 Onc
40	SNORD119	no	no	no	no	no
41	PDCD4	no	no	no	no	yes	WI38 Onc, HUVEC IR, HAEC IR, WI38 Dox, WI38 Rep
42	MIR181A1	no	no	no	no	no
43	SCARNA9	no	no	no	no	yes	WI38 Onc, HUVEC IR, IMR90 Rep, IMR90 IR
44	RP1-102E24.1	no	no	no	no	no
45	PRDM13	no	no	no	no	no
46	SNORD19	no	no	no	no	yes	WI38 Dox, WI38 Onc, HUVEC IR
47	SNORA26	no	no	no	no	yes	WI38 Dox, IMR90 Rep
48	RNU2-36P	no	no	no	no	no
49	SNORA50A (SNORA50)	no	no	no	no	no
50	SNORA40	no	no	no	no	no
51	SNORD1B	no	no	no	no	no

¹ Genes with p < 0.0001 were analyzed, HAEC—human aortic endothelial cells, HUVEC—human umbilical vein endothelial cells, Dox—doxorubicin-induced senescence, IMR90—human diploid fibroblasts from fetal lung, IR—irradiation induced senescence, Rep—replicative exhaustion induced senescence, Onc—oncogene induced senescence, WI38—human diploid fibroblasts from fetal lung, no—there is no association between presented data and literature, yes—there is an association between presented data and literature

in Appendix F). It could be then possible that senescence characteristics present in our data may be the outcome of general stress(es) due to disease process itself, not only the age.

Smoking is AAA risk factor affecting miRNA expression [66] and in the case of our studies might be another bias-introducing factor, since AAA group includes current smokers (9 persons, 32.1%), while control group is devoid of them (Table 1). Only a small number of miRNAs indicated potential bias after comparison with literature (Table A2 in Appendix D). Other research regarding signatures of smoking did not provide us with any transcriptomic patterns recurring in our data (Table A3 in Appendix E). This suggests an absence of significant smoking-associated bias, probably due to relatively low number of current smokers in AAA group.

Despite of in-depth literature analysis we were unable to exclude unambiguously any miRNAs or gene transcripts from AAA biomarker panels. MiRNAs represent a group of RNAs with pleiotropic regulatory behavior. This does not exclude particular miRNA to act in various cellular processes in different spatiotemporal context. After detailed analysis, we noticed great variability of applied methodologies across literature (refer to literature in Table A3 and Table A4). It is possible that existing discrepancies may reflect methodological rather than sex/age/smoking bias.

Taking all of this into account, it should be considered that presented data shows either real AAA biomarkers or biomarkers associated with both AAA and AAA predisposing factors.

Due to technical (data storage server capacity) and financial limitations, gene expression analysis was performed for 14 out of 47 study participants. It could be a potential source of bias affecting investigations on miRNA:gene regulatory network; however, we confirmed some previously validated interactions (Table S11) and predictive interactions with high probability (Table S12). More research with larger populations is needed to confirm our findings and to validate predictive targets.

The results obtained in our study confirm the important role of miRNA in the pathogenesis of AAA, opening a door to deeper understanding of miRNA functions and regulatory network. AAA biomarkers proposed in this research, after further validation in studies with larger and demographically matched cohorts, can be prospectively applied into clinics for differentiation, diagnosis and therapy of AAA.