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  • Research article
  • Open Access
  • Open Peer Review

Screening key genes for abdominal aortic aneurysm based on gene expression omnibus dataset

BMC Cardiovascular DisordersBMC series – open, inclusive and trusted201818:34

https://doi.org/10.1186/s12872-018-0766-8

  • Received: 13 October 2017
  • Accepted: 31 January 2018
  • Published:
Open Peer Review reports

Abstract

Background

Abdominal aortic aneurysm (AAA) is a common cardiovascular system disease with high mortality. The aim of this study was to identify potential genes for diagnosis and therapy in AAA.

Methods

We searched and downloaded mRNA expression data from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) from AAA and normal individuals. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, transcriptional factors (TFs) network and protein-protein interaction (PPI) network were used to explore the function of genes. Additionally, immunohistochemical (IHC) staining was used to validate the expression of identified genes. Finally, the diagnostic value of identified genes was accessed by receiver operating characteristic (ROC) analysis in GEO database.

Results

A total of 1199 DEGs (188 up-regulated and 1011 down-regulated) were identified between AAA and normal individual. KEGG pathway analysis displayed that vascular smooth muscle contraction and pathways in cancer were significantly enriched signal pathway. The top 10 up-regulated and top 10 down-regulated DEGs were used to construct TFs and PPI networks. Some genes with high degrees such as NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16 and FOXO1 were identified to be related to AAA. The consequences of IHC staining showed that CCR7 and PDGFA were up-regulated in tissue samples of AAA. ROC analysis showed that NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA had the potential diagnostic value for AAA.

Conclusions

The identified genes including NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA might be involved in the pathology of AAA.

Keywords

  • abdominal aortic aneurysm
  • gene expression
  • protein-protein interaction network
  • TFs regulatory network
  • biomarkers
  • therapy target

Background

Abdominal aortic aneurysm (AAA), defined as the aortic diameter > 3.0 cm, is a cardiovascular system disease that is characterized by aortic dilation that exceeds the normal aortic diameter by more than 50%. AAA dilatation will lead to rupture of the aorta, which results in bleeding. Generally, it is asymptomatic until the rupture event occurs [1]. Additionally, AAA is common in adult patients, especially elderly men, and leads to severe complications [24]. Up to now, the etiology of AAA remains unclear. It is noted that some clinical risk factors including smoking history, advanced age, family history, hypertension, hyperlipidaemia, atherosclerosis, chronic obstructive pulmonary disease are remarkably related to AAA [3, 57]. It is also observed that the intricate interplay of apoptosis, inflammation and matrix degradation is involved in the development of this disorder [810]. Anyway, the pathophysiology of AAA is complex, but fundamentally aneurysm comes from the vessel wall structural integrity loss and the vessel wall weakening. It is pointed out that vascular smooth muscle cells are the critical cell type involved in the development of AAA [11].

In a word, AAA is a common and late onset disease, which can rupture with a high mortality if not treated. In some clinical practice, there is no effective treatment other than surgical approaches to repair AAA [12]. And endovascular aneurysm repair has improved detection and lower mortality rates of AAA [1317]. However, morbidity and mortality after surgery are still common [18, 19]. Therefore, understanding the genetic architecture and pathological mechanism of the disease may provide valuable information for elucidation of pathogenic mechanisms and signal pathways in AAA and the discovery of potential biomarkers and drug targets in AAA diagnosis and non-surgical treatment therapy.

In this study, we tried to find differentially expressed genes (DEGs) in AAA by integrated analysis. Then, functional enrichment analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) was used to investigate the biological function of DEGs followed by transcriptional factors (TFs) network an d protein-protein interaction (PPI) network construction of top 20 DEGs (10 up-regulated and 10 down-regulated). Immunohistochemical (IHC) staining was applied to validate the expression of candidate DEGs. Finally, receiver operating characteristic (ROC) analyses was applied to analyze diagnosis ability of identified DEGs. Our study may be helpful in understanding the pathogenic mechanism and finding valuable diagnosis biomarkers and therapy drug in AAA.

Methods

Datasets

In this study, we searched datasets from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with the keywords abdominal aortic aneurysm [All Fields] AND (“gse”[Filter] AND “Homo sapiens”[Organism]). The study type was described as “expression profiling by array.” All selected datasets were genome-wide expression data of AAA group and/or normal group tissue samples. Those standardized or primary datasets were included in this study. Finally, a total of 3 datasets including GSE7084, GSE47472 and GSE57691 were screened, which was shown in Table 1.
Table 1

Three datasets in GEO

GEO accession

Author

Platform

Samples(P:N)

Year

GSE7084

Tromp G

GPL570[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array;GPL2507Sentrix Human-6 Expression BeadChip

7:8

2007

GSE47472

Biros E

GPL10558Illumina HumanHT-12 V4.0 expression beadchip

14:8

2013

GSE57691

Biros E

GPL10558Illumina HumanHT-12 V4.0 expression beadchip

49:10

2015

P patients, N normal individual

Analysis of DEGs

Raw expression data of AAA patients in this study were downloaded. Limma and metaMA packages were used to identify the DEGs. And the inverse normal method was used to combine the p value in metaMA. The false discovery rate (FDR) was performed for multiple testing corrections of raw p value through the Benjamin and Hochberg method [20, 21]. The threshold of DEGs was set as FDR < 0.01.

Functional annotation analyses of DEGs

To obtain the biological function and signaling pathways of DEGs, the Metascape software was used for Gene Ontology (GO, http://www.geneontology.org/) annotation and Kyoto Encyclopedia of Genes Genomes (KEGG, http://www.genome. jp/kegg/pathway.html) pathway enrichment of DEGs. The threshold of GO function and KEGG pathway of DEGs was all set as FDR < 0.05.

PPI network construction

It is useful for understanding the molecule mechanism of AAA to study the interactions between proteins. In order to gain insights into the interaction between proteins encoded by DEGs and other proteins, the database of BioGRID (http://thebiogrid.org) was used to retrieve the predicted interactions between top 20 proteins encoded by DEGs (10 up-regulated and 10 down-regulated) and other proteins. The PPI network was generated by the Cytoscape Software (http://cytoscape.org/). A node in the PPI network denotes protein, and the edge denotes the interactions.

Analysis of potential TFs to target DEGs

TFs play a critical role in regulating gene expression. We downloaded the TFs in the human genome and the motifs of genomic binding sites from the TRANSFAC. Moreover, the 2 KB sequence in the upstream promoter region of DEGs was downloaded from UCSC (http://www.genome.ucsc.edu/cgi-bin/hgTables). Target sites of potential TFs were then distinguished. Finally, the transcriptional regulatory network was visualized by Cytoscape software.

Immunohistochemical (IHC) staining for CCR7 and PDGFA

In this study, a patient with AAA and a normal individual was enrolled for the IHC experiment. The 5 μm slides were incubated with anti CCR7 primary rabbit anti-human polyclone antibody (1:500 dilution; abcam) and anti PDGFA primary rabbit anti-human polyclone antibody (1:500 dilution; Invitrogen) followed incubated with peroxidase conjugated goat anti-rabbit secondary antibody (1:200 dilution; Vector). For color visualization, diaminobenzidine (DAB) substrate (Vector) was applied. The staining area was analyzed by the software of Image Pro-plus 6.0 (Media Cybernetics Corporation, arrendale, PA, USA), and quantified by the IHC staining score (intensity score × positive rate score). The negative (−), positive (+), positive (++), positive (+++) of intensity scores represented 0, 1, 2 and 3, respectively. The positive rate score including negative, 1–25%, 26–50%, 51–75% and 76–100% represented 0, 1, 2, 3 and 4, respectively. IHC staining score of 0, 1~ 4, 5~ 8 and 9~ 12 represented negative, slight positive, moderate positive and strong positive, respectively.

All patients provided written informed consent with the approval of the ethics committee of the First Affiliated Hospital of Wenzhou Medical University (2017147).

Receiver operating characteristic analyses

By using pROC package in R language we performed the receiver operating characteristic (ROC) analyses to assess the diagnostic value of DEGs (NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA) in AAA. The area under the curve (AUC) was calculated and the ROC curve was generated.

Results

DEGs analysis

Raw expression profiles of AAA patients were downloaded from the data portal of the GEO database. A total of 1199 DEGs were identified as the threshold at FDR < 0.01, consisting of 188 up-regulated genes and 1011 down-regulated genes. The top 10 up- and down-regulated DEGs are listed in Table 2. The heat map of the top 50 DEGs is shown in Fig. 1.
Table 2

Top 10 up- and down-regulated DEGs

Gene ID

Gene Symbol

FDR

Combined.ES

Gene ID

Gene Symbol

FDR

Combined.ES

Up-regulated genes

 

Down-regulated genes

 

115,362

GBP5

2.46E-07

1.718728703

5154

PDGFA

2.01E-10

−2.16796797

3043

HBB

5.91E-07

1.779962188

2063

NR2F6

3.18E-09

−2.07109705

3040

HBA2

1.45E-06

1.734233872

1459

CSNK2A2

3.18E-09

−2.009947802

3560

IL2RB

4.69E-06

1.434746719

28,999

KLF15

6.12E-09

−2.026442955

4753

NELL2

8.52E-06

1.395326

7220

TRPC1

7.99E-09

−1.919770839

5743

PTGS2

1.01E-05

1.378162107

81,493

SYNC

2.20E-08

−1.980299756

84,658

ADGRE3

1.03E-05

1.423979214

7704

ZBTB16

2.31E-08

−2.023343723

8972

MGAM

1.37E-05

1.372006422

116,151

FAM210B

4.86E-08

−1.869732488

54,504

CPVL

1.49E-05

1.4454994

2308

FOXO1

4.94E-08

−1.841528166

1236

CCR7

1.66E-05

1.335317111

58,499

ZNF462

5.47E-08

−2.004150438

FDR false discovery rate, Combined.ES combined effect size

Fig. 1
Fig. 1

The heat map of top 50 DEGs. The diagram presents the result of a two-way hierarchical clustering of the top 50 DEGs and samples. The clustering is constructed using the complete-linkage method together with the Euclidean distance. Each row represents a DEG and each column, a sample. The DEG clustering tree is shown on the right. The colour scale illustrates the relative level of DEG expression: red, below the reference channel; green, higher than the reference

Functional and pathway enrichment analyses of DEGs

To investigate the biological function of the identified DEGs in AAA, GO term and KEGG pathway enrichment analyses was performed. In GO term and KEGG pathway enrichment analyses, circulatory system development, muscle structure development and translational initiation were the most significant enrichment in biological process (Fig. 2); Oxidoreductase activity, electron carrier activity, protein domain specific binding were the most notable enrichment in molecular function (Fig. 3); Mitochondrial part, focal adhesion and intracellular ribonucleoprotein complex were the most significant enrichment in cellular component (Fig. 4). The top 10 GO terms of DEGs are shown in Table 3, and the KEGG enrichment signal pathways of DEGs shown in Table 4. The vascular smooth muscle contraction and pathways in cancer that were significantly related to AAA are shown in Fig. 5 and Fig. 6, respectively.
Fig. 2
Fig. 2

Significantly enriched biological processes of DEGs. The x-coordinate axis presents the FDR value. FDR value is more highly, the colour of the bar is more deeply

Fig. 3
Fig. 3

Significantly enriched molecular functions of DEGs. The x-coordinate axis presents the FDR value. FDR value is more highly, the colour of the bar is more deeply

Fig. 4
Fig. 4

Significantly enriched cellular components of DEGs. The x-coordinate axis presents the FDR value. FDR value is more highly, the colour of the bar is more deeply

Table 3

Top 10 GO terms of DEGs

GO ID

GO term

List in term

Log p

Biological process

 GO:0072359

circulatory system development

101/913

−10.6462

 GO:0061061

muscle structure development

67/563

−8.4846

 GO:0006413

translational initiation

40/268

−7.9403

 GO:0008285

negative regulation of cell proliferation

70/643

−7.2506

 GO:0060548

negative regulation of cell death

89/910

−6.8694

 GO:0003170

heart valve development

12/34

−6.8435

 GO:0007169

transmembrane receptor protein tyrosine kinase signaling pathway

89/928

−6.4948

 GO:0070372

regulation of ERK1 and ERK2 cascade

34/238

−6.3893

 GO:0006935

chemotaxis

81/860

−5.6514

 GO:0060485

mesenchyme development

32/237

−5.5128

Molecular function

 GO:0016491

oxidoreductase activity

75/719

−6.9626

 GO:0009055

electron carrier activity

21/112

−6.0854

 GO:0019904

protein domain specific binding

61/623

−4.9011

 GO:0008092

cytoskeletal protein binding

74/810

−4.7540

 GO:0032403

protein complex binding

82/928

−4.6775

 GO:0003735

structural constituent of ribosome

27/210

−4.3707

 GO:1,901,681

sulfur compound binding

27/232

−3.6356

 GO:0016453

C-acetyltransferase activity

3/4

−3.1832

 GO:0016634

oxidoreductase activity, acting on the CH-CH group of donors, oxygen as acceptor

4/9

−3.0208

 GO:0008565

protein transporter activity

14/98

−2.9919

 GO:0016491

oxidoreductase activity

75/719

−6.9626

Cellular component

 GO:0044429

mitochondrial part

109/943

−12.6878

 GO:0005925

focal adhesion

55/391

−9.6768

 GO:0030529

intracellular ribonucleoprotein complex

70/710

−5.6423

 GO:0005759

mitochondrial matrix

45/404

−5.1349

 GO:0015629

actin cytoskeleton

46/442

−4.4798

 GO:0005901

caveola

14/76

−4.1752

 GO:0030663

COPI-coated vesicle membrane

6/17

−3.6750

 GO:0044455

mitochondrial membrane part

22/173

−3.6079

 GO:0090665

glycoprotein complex

6/21

−3.1162

 GO:0044451

nucleoplasm part

59/708

−2.9012

List in term: the number of DEGs on the total number of genes in GO term

Log p logarithm processing of p value

Table 4

The KEGG enrichment signal pathways of DEGs

KEGG ID

KEGG term

List in term

Log p

Gene list

hsa03010

Ribosome

22/135

−6.5000

FAU,RPL7,RPL9,RPL24,RPL27,RPL30,RPL35A,RPS6,RPS21,UBA52,MRPL33,MRPL19,MRPL18,MRPL22,MRPS16,RSL24D1,MRPL20,MRPS15,MRPS6,MRPS5,MRPL1,MRPL24

hsa00640

Propanoate metabolism

10/32

−5.9097

ACAT1,ACAT2,LDHA,LDHB,ALDH6A1,MUT,PCCA,SUCLG2,HIBCH,ACSS2,ALDH2,ALDH3A2,HADH,HMGCL,ACO1,GCSH,HOGA1,ESD,PFKM,PRPS2,PHGDH,L2HGDH

hsa04510

Focal adhesion

23/202

−4.1028

ACTN1,CAPN2,CAV2,COL4A1,FLNC,HRAS,ITGA7,LAMA5,LAMC1,PPP1R12A,PDGFA,PDGFRB,MAPK3,PTEN,ROCK1,THBS2,ITGA10,ROCK2,ITGA11,PARVA,PDGFC,TLN2,SHC4,FGF13,MYH10,WASL,ARPC1A,ARHGEF12,GNG12,PIP4K2C

hsa04270

Vascular smooth muscle contraction

16/120

−3.8133

ADCY3,AGTR1,CALD1,EDNRA,GNA11,KCNMB1,MYH11,MYL6,PPP1R12A,MAPK3,PTGIR,ROCK1,ROCK2,RAMP1,ARHGEF12,PPP1R14A

hsa00071

Fatty acid degradation

9/44

−3.7701

ACADL,ACAT1,ACAT2,ADH1A,ADH1B,ALDH2,ALDH3A2,ECI1,HADH,ACYP2,LDHA,LDHB,ACSS2,PFKM,PGM1

hsa03020

RNA polymerase

7/31

−3.3221

POLR2C,POLR2F,POLR2G,POLR2H,POLR2I,POLR3F,POLR3C,ADCY3,AK1,GUK1,NME3,PGM1,PRPS2,ENPP4,NME7,AK3,NUDT9,POLE4,NT5C3B,CTPS1

hsa05016

Huntington’s disease

20/193

−3.1152

COX5B,COX6C,COX7A1,COX7B,COX7C,HDAC2,NDUFA4,NDUFA8,NDUFB10,NDUFC1,POLR2C,POLR2F,POLR2G,POLR2H,POLR2I,SOD1,ATP5H,UQCRQ,NDUFA12,NDUFA4L2,UBE2G2,SNCAIP,PINK1,COX17,ATP6V1D,CAPN2,MAPK3,RYR3

hsa05200

Pathways in cancer

33/397

−2.9576

ADCY3,AGTR1,AR,COL4A1,E2F3,EDNRA,MECOM,FGF13,FOXO1,FZD2,GNA11,GSTP1,MSH6,HDAC2,HRAS,LAMA5,LAMC1,SMAD4,PDGFA,PDGFRB,MAPK3,PTEN,ROCK1,SLC2A1,TCEB1,ZBTB16,FZD3,CCDC6,ROCK2,GNB5,RALBP1,ARHGEF12,GNG12,PDGFC

hsa05412

Arrhythmogenic right ventricular cardiomyopathy (ARVC)

10/74

−2.6366

ACTN1,CACNB3,CDH2,DAG1,GJA1,ITGA7,RYR2,SGCA,ITGA10,ITGA11

hsa00520

Amino sugar and nucleotide sugar metabolism

7/48

−2.1749

CYB5R3,GMDS,PGM1,PMM1,UAP1,UGDH,UGP2

List in term: the number of DEGs on the total number of genes in GO term

Log p logarithm processing of p value

Fig. 5
Fig. 5

Significantly enriched vascular smooth muscle contraction signal pathways of DEGs. The red and green diamond represents the up and down-regulated DEGs

Fig. 6
Fig. 6

Significantly enriched pathways in cancer signal pathways of DEGs. The red and green diamond represents the up and down-regulated DEGs

Establishment of TFs-target genes regulatory network

In order to study the TFs-target genes regulatory network for AAA, we utilized TRANSFAC to identify TFs regulating the top ten up-regulated or down-regulated DEGs. In the end, we obtained transcriptional regulatory networks comprised of 190 pairs of TFs-genes involving 40 TFs (Fig. 7). In this network, the top 7 downstream genes covered by most TFs were neural EGFL like 2 (NELL2, degree = 13), C-C motif chemokine receptor 7 (CCR7, degree = 9), maltase-glucoamylase (MGAM, degree = 8), hemoglobin subunit beta (HBB, degree = 8). Five hub TFs were HNF-4 (degree = 10), Oct-1 (degree = 10), Pax-4 (degree = 8), Evi-1 (degree = 6) and Nkx2–5 (degree = 6) (Table 5).
Fig. 7
Fig. 7

The TFs networks of the top 20 DEGs. Diamonds and ellipses represent the TFs and target genes, respectively. The red and green colors represent up-regulation and down-regulation, respectively

Table 5

Top 5 TFs and target genes

TFs

Number

Target genes

Oct-1

10

CCR7, CPVL, CSNK2A2, HBB, IL2RB, MGAM, NELL2, TRPC1, ZBTB16, ZNF462

HNF-4

10

CCR7, CPVL, CSNK2A2, HBB, KLF15, MGAM, NELL2, NR2F6, PTGS2, ZNF462

Pax-4

8

ADGRE3, CSNK2A2, FAM210B, NELL2, NR2F6, PDGFA, SYNC, ZBTB16

Evi-1

6

CPVL, GBP5, HBB, NELL2, PTGS2, ZNF462

Nkx2–5

6

CCR7, GBP5, MGAM, NELL2, TRPC1, ZBTB16

PPI network

To obtain the interaction between the proteins encoded by DEGs and other proteins, PPI network was explored and visualize by Cytoscape. PPI networks of the top 10 up-regulated and the top 10 down-regulated DEGs were presented in Fig. 8. As Fig. 8 shows, the network consisted of 539 nodes and 567 edges. The red and green diamonds indicate the up- and down-regulated genes in AAA, respectively. The blue ellipses present the proteins that interacted with those proteins encoded by DEGs. The top three proteins with a high degree were casein kinase 2 alpha 2 (CSNK2A2, degree = 184), zinc finger and BTB domain containing 16 (ZBTB16, degree = 113) and forkhead box O1 (FOXO1, degree = 53).
Fig. 8
Fig. 8

The PPI networks of the top 20 DEGs. All the diamonds are proteins encoded by the top 20 DEGs and the blue ellipses represent other proteins. The red and green colors represent up-regulation and down-regulation, respectively

Validation of CCR7 and PDGFA in AAA

In order to validate the expression of CCR7 and platelet derived growth factor subunit A (PDGFA), we assessed the protein expression of CCR7 and PDGFA in AAA through the immunohistochemistry (Fig. 9 and Fig. 10). The result showed that CCR7 was obviously up-regulated in AAA compared with the control, which was consistent with the bioinformatic consequence. However, PDGFA was significantly up-regulated in AAA compared with the control, which was not in line with the bioinformatic result.
Fig. 9
Fig. 9

The IHC staining of CCR7 in AAA. CCR7 protein expression level detected by immunohistochemistry and photographs was amplified 10 × 20 multiples. Bar = 100 μm. a and c were the case samples from two patients with AAA; b and d were the control sample from two normal individuals. *P<0.05 vs control

Fig. 10
Fig. 10

The IHC staining of PDGFA in AAA. PDGFA protein expression level detected by immunohistochemistry and photographs was amplified 10 × 20 multiples. Bar = 100 μm. a and c were the case samples from two patients with AAA; b and d were the control sample from two normal individuals. **P<0.01 vs control

ROC curve analysis

In order to access the discriminatory ability of the NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA among AAA tissues and adjacent non-tumor tissues generated from GEO database, ROC curve analyses were conducted and AUC were calculated. As Fig. 11 shown, the AUC of all these genes was more than 0.8. For AAA diagnosis, the sensitivity and specificity of these genes were very high.
Fig. 11
Fig. 11

The ROC curve analyses of NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA between AAA and healthy controls

Discussion

In spite of improvement to surgical techniques that have been made in AAA treatment, morbidity and mortality after operations are still common. AAA seriously influences the life quality of patients and brings a heavy burden on the family. Therefore, it is urgent to elucidate AAA pathogenesis mechanism for developing novel diagnose and therapeutic target.

TFs are key regulatory factors in gene expression. The construction of regulatory networks between TFs and target genes is helpful in understanding the biological regulatory mechanism in the development of AAA. In this study, we found NELL2, CCR7, MGAM and HBB were significantly expressed genes with the most degree under the regulation of TFs including HNF-4, Oct-1 and Pax-4. NELL2 is a neural tissue-enriched protein in mammal and it is a receptor for vascular endothelial growth factor-A, which plays an important role in angiogenesis. It is reported that the mRNA expression of NELL2 is up-regulated in benign prostate hyperplasia and prostate cancer [22]. In addition, NELL2 is regarded as the potential biomarker for bladder cancer [23]. CCR7 is a pro-inflammatory cytokine and is found in human atherosclerotic plaques [24]. It is found that expression of CCR7 is dramatically down-regulated in human carotid atherosclerotic plaques [25]. MGAM is found down-regulated and considered as a candidate serum biomarker in colorectal cancer [26]. Additionally, MGAM is a significantly mutated gene in lung adenocarcinoma [27]. HBB is suggested as a potential biomarker in the plasma sample of patients with AAA [28]. In this study, we found that NELL2, CCR7, MGAM and HBB were up-regulated in the AAA tissues, which played crucial roles in the carcinogenesis of AAA.

The interaction among proteins determines the characteristic of the cell, tissue and individual. The study of PPI is a useful way to find the potential drug target of AAA. Herein, we found three genes including CSNK2A2, ZBTB16 and FOXO1 were for the most degree in the PPI network. CSNK2A2 is found to be correlated with ovarian cancer patient survival. Furthermore, the down-regulation of CSNK2A2 will decrease the proliferation of ovarian cancer cells [29]. ZBTB16, also called PLZF, plays an important role in oncogenesis and is first identified in acute promyelocytic leukemia [30]. Based on a microarray study, ZBTB16 is found to be down-regulated in AAA [31]. FOXO1 is a transcription factor and plays roles in diverse physiological processes including Akt-dependent cell proliferation and apoptosis [32]. Additionally, FOXO1 is also involved in energy metabolism and autophagy [33]. In our study, we found down-regulated expression of CSNK2A2, ZBTB16 and FOXO1. It is worth mentioning that ZBTB16 and FOXO1 were also involved in the pathways in cancer according to the KEGG analysis. In addition, PDGFA was one of the top ten down-regulated genes and also involved in the pathways in cancer. PDGFA is expressed in vascular smooth muscle cells and has been involved in migration and proliferation of vascular smooth muscle cells [34]. Moreover, the importance of PDGFA in the arterial system has been demonstrated on account of that fact that the proliferation of arterial vascular smooth muscle cells was strongly stimulated by PDGFA [35]. Moreover, an in situ hybridization study has demonstrated mRNA for PDGFA in atherosclerotic plaques [36]. In this study, we found that PDGFA was down-regulated in AAA. However, the IHC result was not consistent with the bioinformatic analysis. The small sample size may account for the discrepancy. In a word, CSNK2A2, ZBTB16 and FOXO1 played a crucial role in the oncogenesis of AAA and could be considered as drug targets of AAA.

Apart from the cancer signal pathway, vascular smooth muscle contraction was another signal pathway identified that associated with AAA. Vascular smooth muscle cells have been shown to play an important synthetic role in vascular remodeling [37, 38]. It is pointed out that vascular smooth muscle cells are the main component of the aortic media and the dysfunction plays a key role in different arterial diseases, such as AAA [39]. In addition, vascular smooth muscle cell activation is the main hallmark of atherosclerosis, which is a risk factor of AAA [4043]. Several down-regulated genes were significantly involved in the signal pathway, such as AGTR1, CALD1, EDNRA, MYH11, RAMP1, ROCK1 and ROCK2.

Angiotensin II receptor type 1 (AGTR1) is a cardiovascular risk gene. Jones, G. T et al. found that AGTR1 was remarkably associated with AAA [44]. In addition, the 1166A > C polymorphism in AGTR1 has been demonstrated to be associated with AAA [45, 46]. It is noted that AGTR1 blockers (ARBs) have been investigated for prevention or delay of aortic dilatation [47]. It is reported that the expression of caldesmon 1 (CALD1) is increased in aortas, which protects from aneurysm. This suggested that importance of CALD1 in maintaining vascular integrity in AAA. Endothelin receptor type A (EDNRA) is primarily located in the vascular smooth muscle cells and mediates vasoconstriction and proliferation [48]. It has been reported that EDNRA on chromosome 4q31 is related to intracranial aneurysm [49]. It is found that heterozygous mutation of myosin heavy chain 11 (MYH11) results in the early and severe decrease in the aortic wall elasticity [50]. Additionally, it has been demonstrated the relationship between MYH11 genetic and epigenetic and thoracic aortic aneurysms and dissections [51, 52]. Receptor activity modifying protein 1 (RAMP1) is a member of a family of calcitonin receptor modifying proteins and is thought to play an important role in regulating blood pressure by vascular relaxation. Tsujikawa K et al. found that ramp1-deficient mice exhibited elevated blood pressure [53]. It is pointed out that the mRNA levels of RAMP1 are decreased in AAA [31]. It is found that the expression of Rho associated coiled-coil containing protein kinase 1 (ROCK1) and Rho associated coiled-coil containing protein kinase 1 (ROCK2) was increased at the AAA lesion compared with control [54]. Thus it can be seen that AGTR1, CALD1, EDNRA, MYH11, RAMP1, ROCK1 and ROCK2 played an important role in vascular smooth muscle contraction, which was significantly associated with AAA.

In order to access the discriminatory ability of identified genes in the TFs and PPI network, eight genes including NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA were applied to ROC curve analyses among AAA tissues and adjacent non-tumor tissues in GEO database. Our result showed that the AUC of all these genes was more than 0.8, especially HBB (AUC: 0.906), CSNK2A2 (AUC: 0.945), ZBTB16 (AUC: 0.950), FOXO1 (AUC: 0.940) and PDGFA (AUC: 0.930). This suggested that NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA may have value in diagnosis of the development of AAA.

Conclusions

In summary, we found a series of DEGs in AAA. Among which, eight genes including NELL2, CCR7, MGAM, HBB, CSNK2A2, ZBTB16, FOXO1 and PDGFA could be used for the diagnosis biomarkers of AAA. Especially, CSNK2A2, ZBTB16 and FOXO1 could be considered as drug targets in the therapy of AAA. In addition, vascular smooth muscle contraction was an important signal pathway identified in this study, which played crucial roles in the aortic angiogenesis of AAA. There are limitations to our study. Firstly, the sample size in the IHC experiment is small and large numbers of tissue samples are needed to further validate the identified DEGs. Secondly, biological function of identified DEGs is not investigated, some in vivo or in vitro experiments are needed to further study the molecular mechanism of AAA. Thirdly, the sample size of normal individuals in the selected dataset is less than that of the patient group. In further studies, it is better to sample equal numbers of individuals in both groups in order to reduce the false positive/negative rate for up−/down-regulated DEGs detection.

Abbreviations

AAA: 

abdominal aortic aneurysm

AGTR1: 

angiotensin II receptor type 1

ARBs: 

AGTR1 blockers

AUC: 

area under the curve

CALD1: 

caldesmon 1

CCR7: 

C-C motif chemokine receptor 7

CSNK2A2: 

casein kinase 2 alpha 2

DAB: 

diaminobenzidine

DEGs: 

differentially expressed genes

EDNRA: 

endothelin receptor type A

FDR: 

false discovery rate

FOXO1: 

forkhead box O1

GEO: 

gene expression omnibus

HBB: 

hemoglobin subunit beta

IHC: 

immunohistochemical

MGAM: 

maltase-glucoamylase

MYH11: 

myosin heavy chain 11

NELL2: 

neural EGFL like 2

PDGFA: 

platelet derived growth factor subunit A

PPI: 

protein-protein interaction

RAMP1: 

receptor activity modifying protein 1

ROC: 

receiver operating characteristic

ROCK1: 

Rho associated coiled-coil containing protein kinase 1

ROCK2: 

Rho associated coiled-coil containing protein kinase 1

TFs: 

transcriptional factors

ZBTB16: 

zinc finger and BTB domain containing 16

Declarations

Acknowledgements

Not Applicable.

Funding

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Authors’ contributions

LW and JH drafted and revised the manuscript. LW, HN and GY performed the experiment and analyzed the data. JH designed the subject of the manuscript. All authors have read and agreed to the submission of the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All participating individuals provided written informed consent with the approval of the ethics committee of the First Affiliated Hospital of Wenzhou Medical University (2017147).

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Department of pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
(2)
Department of vascular surgery, The First Affiliated Hospital of Wenzhou Medical University, NO.3, YuanXi Lane, Lucheng District, Wenzhou, Zhejiang, 325000, China

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