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Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis

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Objective This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets. Methods Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks. Results A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors. Conclusions We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.
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Guoetal. BMC Medical Genomics (2022) 15:145
https://doi.org/10.1186/s12920-022-01257-1
RESEARCH
Identication ofhub genes andregulatory
networks inhistologically unstable carotid
atherosclerotic plaque bybioinformatics
analysis
Julong Guo1, Yachan Ning2, Zhixiang Su1, Lianrui Guo1* and Yongquan Gu1*
Abstract
Objective: This study identified underlying genetic molecules associated with histologically unstable carotid athero-
sclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets.
Methods: Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset
(GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially
expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontol-
ogy (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was
constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794
dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were
also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory
networks.
Results: A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes,
which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle,
chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14,
FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were
involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We
also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory net-
work, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-
hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors.
Conclusions: We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a
crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and thera-
peutic targets.
Keywords: Atherosclerosis, Unstable carotid artery plaque, Bioinformatics, Potential biomarker, Therapeutic target
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Introduction
Carotid artery stenosis, most commonly caused by ath-
erosclerosis, is a main reason of ischemic stroke [1].
However, there is increasing evidence that in addition
to atherosclerotic stenosis, unstable plaques also play an
Open Access
*Correspondence: lianruiguo@sina.com; gu15901598209@aliyun.com
1 Department of Vascular Surgery, Xuanwu Hospital, Capital Medical
University, No. 45 Changchun Street, Xicheng District, Beijing 100053, China
Full list of author information is available at the end of the article
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Page 2 of 11
Guoetal. BMC Medical Genomics (2022) 15:145
important role in promoting symptomatic stroke [2, 3].
In advanced atherosclerosis, plaques may have a large
necrotic lipid core, a weak fibrous cap,intraplaque neo-
vascularization and hemorrhage. ese histological
changes make the plaque unstable and prone to rupture,
which is more likely to result in thrombosis and embo-
lization of plaque material, thereby leading to vascular
occlusion and subsequent ischemic stroke [3, 4]. More-
over, unstable plaques cause more severe neurological
damage in patients with acute cerebral infarction than
stable plaques [5]. erefore, clarifying the pathogenesis
associated with unstable plaque formation is significant
and may reveal potential biomarkers and therapeutic
targets.
With the further research of the pathogenesis, bioin-
formatics analysis has become an essential technique
for discovering genetic alteration in various diseases. By
analyzing microarray or sequencing data, the differen-
tially expressed genes (DEGs) of unstable carotid plaques
can be found. e functions and pathways involved in
DEGs were then predicted by functional enrichment
analysis. Further construction of protein–protein inter-
action (PPI) network can understand their interactions
and screen out the hub genes related to unstable carotid
plaques. Finally, prediction of miRNAs, transcription fac-
tors (TFs) and drugs targeting hub genes can make the
pathogenesis network more comprehensive.
Several unstable carotid plaque datasets based on clini-
cal and/or histological criteria have been published. In
fact, unstable carotid plaques identified by clinical cri-
teria alone, also known as symptomatic carotid stenosis,
do not necessarily meet the histological criteria [6]. Some
studies have pooled the datasets of unstable plaques
that meet symptomatic or histological criteria, which
may produce inaccurate results. In this study, three
transcriptome datasets and one non-coding RNA data-
set met histological criteria of unstable carotid plaques
were selected from the Gene Expression Omnibus
(GEO, http:// www. ncbi. nlm. nih. gov/ geo/) database and
the ArrayExpress (https:// www. ebi. ac. uk/ array expre ss/)
database for bioinformatics analysis, hoping to provide
more insights into unstable carotid plaques.
Methods
e study flow was shown in Fig.1.
Data source
ree gene expression profiles were downloaded. e
matrix data of GSE41571 (microarray) and the Fragments
Per Kilobase Million (FPKM) data of GSE120521 (RNA-
sequencing) were obtained from the GEO database, and
the processed data of E-MTAB-2055 (microarray) was
downloaded from the ArrayExpress database. GSE41571,
including 5 ruptured plaques and 6 stable plaques, was
assayed at the platform of GPL570. GSE120521, con-
taining 4 unstable plaques and 4 stable plaques, was
measured at the platform of GPL16791. e platform of
A-MEXP-931 was used by E-MTAB-2055 which included
25 ruptured plaques and 22 stable plaques. GSE111794
is a microRNA expression profile downloaded from
the GEO database. It used GPL16384 platform to assay
9 unstable and 9 stable plaques. e local research eth-
ics committee authorized construction of these three
datasets.
Fig. 1 Flow chart
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Guoetal. BMC Medical Genomics (2022) 15:145
Identication ofDEGs
NetworkAnalyst [7] (https:// www. netwo rkana lyst. ca/),
a comprehensive gene-centric platform supporting gene
expression profiling, was used to identify the DEGs of
GSE41571 and GSE120521. e DEGs of E-MTAB-2055
were screened directly from its processed data. DEGs
were defined as genes with p-value < 0.05 and |log fold
change (FC)|> 1. e screening results were visualized
using volcano maps drawn by GraphPad Prism 9.0.0. An
online drawing tool (http:// bioin forma tics. psb. ugent. be/
webto ols/ Venn/) was used to select the common DEGs
(co-DEGs) of three datasets and draw a Venn diagram.
Functional enrichment analysis
To further investigate the potential functions of these co-
DEGs, the list of co-DEGs was uploaded to Metascape [8]
(https:// metas cape. org/) for Gene Ontology (GO) [9] and
Kyoto Encyclopedia of Genes andGenomes (KEGG) [10]
pathway enrichment analysis. Metascape is a web-based
portal leveraging over 40 independent knowledgebases to
provide a comprehensive gene list annotation and analy-
sis resource for users [8]. Cellular compartments (CC),
biological processes (BP), molecular function (MF) and
KEGG pathway were selected for analysis. e threshold
for P-value was set at 0.05 and the minimum enrichment
score was 1.5. e visualization results were generated by
Metascape and GraphPad Prism 9.0.0.
PPI network andhub gene identication
e Search Tool for the Retrieval of Interacting Genes
[11] (STRING, v11.5, https:// cn. string- db. org/) is a data-
base of known and predicted PPIs. After uploading the
co-DEGs list to the STRING database, PPI results were
obtained with required interaction score > 0.4. Cytoscape
(v3.9.0) [12] was then used to construct a visual PPI net-
work and identify the hub genes. In order to avoid inac-
curate screening results caused by using a single rank
method, maximal clique centrality (MCC), maximum
neighborhood component (MNC), node connect degree
(Degree), edge percolated component (EPC), closeness
and radiality rank methods were used for screening top
10 genes, respectively. All of these rank methods are
included in Cytoscape’s application named Cytohubba
(v0.1) [13]. eir results were integrated, then the genes
with the top 10 frequencies were identified as the hub
genes.
Prediction ofmiRNA/TF‑hub gene regulatory network
e miRNAs targeting hub genes were predicted using
miRNet [14] (v2.0, https:// www. mirnet. ca/), which is
a miRNA-centric network visual analytics platform.
e miRNA-hub gene data were collected from the
well-annotated database miRTarBase v8.0 [15], DIANA-
TarBase v8.0 [16], and miRecords [17]. e differentially
expressed miRNAs of GSE111794 were analyzed using
GEO2R, and then intersected with the predicted results
of miRNet 2.0 to identify meaningful miRNA. TFs were
also predicted in miRNet, using JASPAR database[18]
resources. e top5 TFs were selected to construct the
regulatory network. Finally, we visualized the miRNA/
TF-hub gene regulatory network using Cytoscape 3.9.0.
Prediction ofdrug‑hub gene regulatory network
e drugs targeting all hub genes were predicted using
the Drug Gene Interaction Database [19] (DGIdb, v4.2.0,
https:// dgidb. genome. wustl. edu/). DGIdb used a com-
bination of expert curation and text-mining to mine
drug-gene interactions from DrugBank [20], PharmGKB
[21], Drug Target Commons [22] and others. e
Cytoscape3.9.0 was used to construct the drug–gene reg-
ulatory network.
Results
Identication ofDEGs
DEGsbetween unstable and stable carotid plaques were
identified. Seven hundred and ninety-six DEGs were
found in GSE41571, including 266 up-regulated and 530
down-regulated DEGs (Fig. 2A). ere were 796 DEGs
in GSE41571, including 266 up-regulated and 530 down-
regulated DEGs (Fig. 2B). And we obtained 418 DEGs
in E-MTAB-2055, including 222 up-regulated and 196
down-regulated DEGs (Fig.2C). After integration, a total
of 105 co-DEGs including 42 up-regulated and 63 down-
regulated DEGs overlapped in three datasets (Fig.2D, E).
GO andKEGG enrichment analysis
All 105 co-DEGs were uploaded to Metascape database
for functional enrichment analysis. e first 20 repre-
sentative enriched terms (one for each cluster) are shown
in Fig.3A and Additional file1: TableS1. To further cap-
ture the relationships between these terms, Metascape
presented a network where terms with a similarity > 0.3
are connected by edges (Fig.3B, C). e top 3 enriched
terms were collagen-containing extracellular matrix,
focal adhesion and actin filament bundle, all of which
belong to CC (Figs.3A, 4A). As summarized in Table1,
co-DEGs were mostly enriched in BP of extracellular
matrix organization, leukocyte migration and circulatory
system (Fig.4B). For MF, these co-DEGs were particularly
enriched in extracellular matrix structural constituent,
actin binding and calcium ion binding (Fig.4C). KEGG
pathway analysis revealed co-DEGs were primarily
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Guoetal. BMC Medical Genomics (2022) 15:145
enriched in chemokine signaling pathway and regulation
of actin cytoskeleton (Fig.4D).
PPI network andhub gene identication
A PPI network with 105 nodes and 116 interacting pairs
was constructed (Fig. 5A). e top10 DEGs were iden-
tified as hub genes by six rank methods of Cytoscape
Cytohubba (Table 2). Among them, four genes (HCK,
C1QC, TPM1 and CD14) were selected in all six meth-
ods, and FMOD and PLS3 with the lowest frequency
appeared in the results of four methods. e identified 10
hub genes included 6 up-regulated genes (HCK C1QC,
CD14, FCER1G, LCP1 and RAC2) and 4 down-regulated
genes (TPM1, MYH10, FMOD and PLS3) (Fig.5B). All
of the hub genes were present in the top enriched func-
tional terms and may play critical roles in atheroscle-
rotic carotid unstable plaques. Furthermore, HCK and
RAC2 were enriched in chemokine signaling pathway,
MYH10 and RAC2 were enriched in regulation of actin
cytoskeleton.
miRNA/TF‑hub gene regulatory network
Two hundred and twenty-four miRNAs targeting hub
genes were predicted using miRNet. ere were 736 dif-
ferentially expressed miRNAs in the GSE111794 dataset,
among which 12 miRNAs overlapped with the miRNet
results (Fig.6A), including 1 up-regulated and 11 down-
regulated miRNAs targeting five hub genes (TPM1,
MYH10, PLS3, LCP1 and FMOD). e Top5 TFs tar-
geting hub genes were predicted to be GATA2, TP53,
FOXC1, FOXL1 and JUN. en a miRNA/TF-hub gene
regulatory network was constructed (Fig.6B). PLS3 was
the most targeted hub genes. PLS3 was targeted by six
miRNAs and all five screened TFs.
Drug‑hub gene regulatory network
As shown in Fig. 7, 25 potential drugs were found for
4 up-regulated hub genes using the DGIdb database.
Among them, HCK was targeted by 20 drugs including 10
inhibitors. Drugs targeted the other 6 hub genes were not
predicted.
Discussion
We screened 105 co-DEGs from three data sets, includ-
ing 42 up-regulated and 63 down-regulated genes.
rough enrichment analysis of co-DEGs, we identified
biological processes, cellular compartments and molecu-
lar functions associated with unstable carotid plaques.
Atherosclerosis is associated with uncontrolled extracel-
lular matrix (ECM) remodeling [23]. Reduced collagen
content in ECM is a typical feature of unstable plaques
Fig. 2 Identification of DEGs. A: Volcano plot of GSE41571; B: Volcano plot of GSE120521; C: Volcano plot of E-MTAB-2055; D: Venn diagram of
up-regulated DEGs; E: Venn diagram of down-regulated DEGs
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Guoetal. BMC Medical Genomics (2022) 15:145
[24]. Apolipoprotein E-deficient mice expressing colla-
genase-resistant collagen-I or lacking matrix metallopro-
teinase-13 (MMP-13/collagenase-3) can obtain collagen
accumulation resulting in more stable plaques [25, 26].
Focal adhesions (FAs) are key junctions between cells and
ECM and points of termination of actin filaments. FAs
play an important role in tissue remodeling, integrity and
homeostasis. e changes of FA constituents, such as the
low expression of Talin and Vinculin, can affect the tissue
remodeling and healing capabilities, which promotes the
development of vulnerable plaques [27]. Actin filament
bundles are the assemblies of actin filaments that partici-
pate in the regulation of endothelial cell (EC) adhesion
to adjacent cells and matrix. During the development of
arteriosclerosis, actin bundles change dynamically, and
eventually the decrease or even disappearance of central
actin microfilaments leads to dysfunction of cell–matrix
adhesion [28]. In addition, calcification has been found to
affect plaque stability.Common in unstable plaques are
microcalcifications that originate from matrix vesicles
Fig. 3 GO and KEGG Enrichment Analyses. (A): Bar graph of enriched terms across input gene lists, colored by p-values. B, C: Network of enriched
terms: (B) colored by cluster ID, where nodes that share the same cluster ID are typically close to each other; (C) colored by p-value, where terms
containing more genes tend to have a more significant p-value
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Guoetal. BMC Medical Genomics (2022) 15:145
Fig. 4 Bubble diagram of the functional enrichment analysis of DEGs. A: biological processes of GO enrichment; B: cellular compartments of GO
enrichment; C: molecular function of GO enrichment; D: KEGG pathway analysis
Table 1 Top 3 terms of GO and top 2 terms of KEGG pathway functional enrichment analysis of each category
GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, CC Cellular compartment, BP Biological processes, MF Molecular function
Category Term Log P Count Hub genes
GO CCs GO:0062023: collagen-containing extracellular matrix 15.26 19 C1QC, FMOD
GO CCs GO:0005925: focal adhesion 9.64 14 HCK, LCP1, RAC2
GO CCs GO:0032432: actin filament bundle 9.51 8 LCP1, MYH10, PLS3, TPM1
GO BPs GO:0030198: extracellular matrix organization 9.8 12 FMOD, LCP1
GO BPs GO:0050900: leukocyte migration 8.05 10 FCER1G, HCK
GO BPs GO:0003013: circulatory system process 7.76 13 TPM1
GO MFs GO:0005201: extracellular matrix structural constituent 9.3 10 FMOD
GO MFs GO:0003779: actin binding 8.35 13 LCP1, MYH10, PLS3, TPM1
GO MFs GO:0005509: calcium ion binding 6.73 14 LCP1, PLS3
KEGG hsa04062: Chemokine signaling pathway 4.24 6 HCK, RAC2
KEGG hsa04810: Regulation of actin cytoskeleton 4 6 MYH10, RAC2
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Guoetal. BMC Medical Genomics (2022) 15:145
rich in calcium-binding protein [29, 30]. e infiltration
and activation of leukocytes within lesions also contrib-
ute to plaque rupture. For example, invariant natural
killer T (iNKT) cells can promote rupture by activating
inflammatory cells and up-regulating MMP-2 in vascu-
lar tissue [31]. In our study, the top2 enriched pathways
are chemokine signaling pathway and regulation of actin
cytoskeleton, each of which has been mentioned in previ-
ous studies to be involved in the progression of athero-
sclerotic plaque [3234].
After constructing the PPI network, we identified
10 hub genes of unstable carotid plaque, including 6
up-regulated genes (HCK, C1QC, CD14, FCER1G, LCP1
and RAC2) and 4 down-regulated genes (TPM1, MYH10,
PLS3 and FMOD). MiRNAs, TFs and drugs targeting
these hub genes were also predicted.
HCK, a member of the SRC family of cytoplasmic
tyrosine kinases (SFKs), is confined to the hematopoietic
system, such as cells of myeloid and B lymphocyte line-
ages [35]. HCK promotes monocyte/macrophage intrava-
sation by participating in a broad spectrum of processes
including monocyte/macrophage proliferation, migration
and endothelial adhesion, which is an essential mecha-
nism of atherosclerosis involving a series of signaling
Fig. 5 Protein–protein interaction network of co-DEGs (A) and the hub genes (B). Orange represents the up-regulated co-DEGs. Cyan represents
down-regulated co-DEGs. Rectangles represent co-DEGs except for hub genes. Circles represent hub genes
Table 2 Top 10 DEGs screened by different rank methods and the result of hub genes
MCC maximal clique centrality, MNC maximum neighborhood component, Degree: node connect degree, EPC edge percolated component
MCC MNC Degree EPC Closeness Radiality Hub genes
HCK HCK HCK HCK C1QC C1QC HCK
PLS3 CD14 C1QC CD14 HCK CD33 C1QC
CD14 PLS3 TPM1 RAC2 TPM1 CD14 TPM1
TPM1 C1QC CD14 LCP1 CD14 HCK CD14
MYH10 FCER1G MYH10 C1QC RAC2 FCER1G MYH10
C1QC TPM1 PLS3 FCER1G FCER1G FERMT2 RAC2
LCP1 MYH10 CDH2 MYH10 MYH10 RAC2 FCER1G
FCER1G OGN LCP1 TPM1 LCP1 S100A8 LCP1
FMOD LCP1 FMOD PLS3 CD33 TPM1 FMOD
OGN RAC2 RAC2 S100A8 FMOD FMOD PLS3
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Guoetal. BMC Medical Genomics (2022) 15:145
Fig. 6 Prediction of miRNA/TF-hub gene regulatory network. A: Venn diagram of miRNAs predicted by miRNet and differentially expressed in
GSE111794; B: miRNA/TF-hub gene regulatory network. Orange circles represent up-regulated hub genes. Cyan circles represent down-regulated
hub genes. Pink triangles represent up-regulated miRNAs. Green triangles represent down-regulated miRNAs. Gray squares represent
transcription factors
Fig. 7 Drug-hub gene regulatory network. Orange circles represent up-regulated hub genes. Yellow dots represent inhibitor drugs. Purple dots
represent drugs without specified interaction type
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Guoetal. BMC Medical Genomics (2022) 15:145
pathwaysinitiated by integrin, immune and growth fac-
tors, Fcy and chemokine receptors [36]. In addition,
HCK can facilitate ECM degradation by phosphoryl-
ating the Wiskott-Aldrich syndrome protein (WASP)
[37], which may contribute to plaque instability. How-
ever, Medina etal. indicated that atherosclerotic plaques
showed reduced size in HCK/FGR double knockout
mice but presented vulnerable phenotype characterized
by necrotic core expansion, and significant reductions
in collagen and fibrous cap thickness [36]. is may be
due to a more complex response caused by the complete
absence of HCK or the simultaneous deletion of FGR
which is another member of SFKs, but it deserves fur-
ther study. RAC2, a member of the Rho GTPases, is only
found in cells of myeloid origin and is a crucial factor in
leukocyte chemotaxis, as well as involved in the regula-
tion of actin and microtubule cytoskeletal dynamics and
adhesion [38]. RAC2 has been demonstrated to partici-
pate in reactive oxygen species (ROS) production [39],
which induces the release of MMPs leading to plaque
vulnerability by degrading the fibrous wall of atheroma-
tous plaques and the basal membrane of endothelial cells
[40]. MYH10 encodes a constituent protein of non-mus-
cle myosin II, which is an actin-dependent motor protein
and plays fundamental roles in cell adhesion and migra-
tion [41]. Kim etal. suggested that structural changes in
the actomyosin network and defective ECM remodeling
due to MYH10 deficiency contribute to the pathogenesis
of emphysema [42], which may also contribute to unsta-
ble atherosclerotic plaques.
e other 7 hub genes were not enriched in the top 2
KEGG pathways. C1QC encodes the C-chain polypep-
tide of serum complement subcomponent C1q, a defi-
ciency of which is associated with lupus erythematosus
and glomerulonephritis. Lubbers et al. demonstrated
that C1q can induce changes in ECM collagen expression
[43]. C1QC was thought to regulate immune-competent
cells involved in the progression of atherosclerosis [44].
CD14 is a differentiated antigen preferentially expressed
on monocytes/macrophages and associated with inflam-
mation and immune response. Inflammation can cause
plaque disruption through processes such as endothelial
cell death and activation of MMPs [45], but the role of
CD14 needs to be further elucidated. It was found that
the increase of CD14 + monocytes in coronary athero-
sclerosis patients was significantly correlated with the
severity [46]. FCER1G is involved in encoding high affin-
ity immunoglobulin epsilon receptors and has repeatedly
been found to be overexpressed in the progression of ath-
erosclerosis and unstable plaques. Although LCP1 (also
named PLS2) and PLS3 are both actin binding proteins,
LCP1 is overexpressed in unstable plaques, while PLS3
is underexpressed. LCP1 is essential for the degradation
of ECM by macrophages.When LCP1 was inhibited by
nanobodies, actin turnover was hampered and matrix
degradation was significantly decreased [47]. PLS3 defi-
ciency has been reported to cause osteoporosis and
neurodegeneration [48]. TPM1 is a member of the tro-
pomyosin family. Simoneau etal. proposed that TPM1 is
required to maintain the endothelial barrier integrity and
demonstrated that phosphorylation at Ser283 of TPM1
protects against oxidative stress-related endothelial bar-
rier dysfunction [49]. FMOD encodes fibromodulin. In
arteriosclerosis lesions, FMOD has stimulatory or stabi-
lizing effects on collagen, smooth muscle cell prolifera-
tion, plaque lipids, inflammatory and proinflammatory
cytokines [50].
In present study, we predicted 12 miRNAs that might
be associated with unstable carotid plaques, several
of which have been conducted the related studies. For
example, mir-1910-5p was found to be highly expressed
under oxidative stress [51], so it may be involved in ROS
induced plaque destruction. Ye etal. demonstrated that
lncRNA myocardial infarction associated transcript
(MIAT) regulates the size of atherosclerotic necrotic core
through the sponging mir-149-5p, while large necrotic
core is a marker of unstable plaques [52]. A circulating
microRNAs study observed that plasma mir-30e-5p was
positively correlated with the volume of necrotic core in
coronary plaques [53], but we found that mir-30e-5p was
low expressed in unstable plaque samples. Whether this
conflict is caused by different sample types or other rea-
sons still needs further exploration. In addition, another
study suggested that circulating mir-330-3p can be used
to distinguish the plaque phenotype in patients with ST-
segment elevation myocardial infarction [54]. TFs can
affect gene expression and may play a role in unstable
plaques. erefore, we predicted the top five suspected
TFs to improve the regulatory network of unstable
plaques.
Several related drugs were predicted, including ten
and one inhibitors of HCK and CD14. ese drugs may
be able to prevent or delay the progression of atheroscle-
rotic plaque rupture. e eleven predicted inhibitors are
mainly kinase inhibitors, which have been widely used
in the treatment of malignant tumors. Several of them,
such as nintedanib and bosutinib, have also been shown
to ameliorate the development of atherosclerosis [55, 56].
Some limitations exist in our study. First of all,
our analysis was based on four datasets from public
data, whose grouping criteria may differ slightly, even
though they all meet the histological criteria. In addition,
only one miRNA dataset was used without cross-valida-
tion of other datasets, which may cause bias. Finally, we
predicted the relevant molecules only by bioinformatic
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Guoetal. BMC Medical Genomics (2022) 15:145
analysis, the true and complete regulatory mecha-
nisms need to be verified by further in-vivo and in-vitro
experiments.
Conclusions
In conclusion, the rupture of carotid atherosclerotic
plaque involves extremely complex molecular mecha-
nisms and regulatory networks. Bioinformatics analysis
was used to identify 105 DEGs that are mainly enriched
in the chemokine signaling pathway and the regulation
of actin cytoskeleton pathway to participate in the for-
mation of histologically unstable carotid atherosclerotic
plaques. We screened 10 hub genes and predicted miR-
NAs and TFs targeting them to construct the regulatory
network.ese molecules may play a crucial role in the
progression of unstable carotid plaques and serve as
potential biomarkers and therapeutic targets.
Abbreviations
DEG: Differentially expressed gene; PPI: Protein–protein interaction; TF: Tran-
scription factor; GEO: Gene Expression Omnibus; FPKM: Fragments per kilo-
base million; FC: Fold change; GO: Gene ontology; KEGG: Kyoto Encyclopedia
of Genes and Genomes; CC: Cellular compartments; BP: Biological processes;
MF: Molecular function; STRING: The Search Tool for the Retrieval of Interacting
Genes; MCC: Maximal clique centrality; MNC: Maximum neighborhood com-
ponent; EPC: Edge percolated component; DGIdb: The Drug Gene Interaction
Database; ECM: Extracellular matrix; MMP: Matrix metalloproteinase; FA: Focal
adhesion; EC: Endothelial cell; SFKs: The SRC family of cytoplasmic tyrosine
kinases; WASP: Wiskott–Aldrich syndrome protein; ROS: Reactive oxygen spe-
cies; MIAT: Myocardial infarction associated transcript.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12920- 022- 01257-1.
Additional le1: TableS1. Top 20 terms (one per cluster) of functional
enrichment analysis.
Acknowledgements
We would like to thank all the research staff who made it possible to perform
this study.
Author contributions
YG and LG contributed to conception and design of the study. JG, YN and ZS
organized the database and performed the statistical analysis. JG wrote the
manuscript and prepared figures. All authors have read and agreed to the
published version of the manuscript.
Funding
This study was supported by the National Key R&D Program of China
(No.2021YFC2500500).
Availability of data and materials
Publicly available datasets were analyzed in this study. This data can be found
here: https:// www. ncbi. nlm. nih. gov/ geo/; https:// www. ebi. ac. uk/ array expre ss/.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University,
No. 45 Changchun Street, Xicheng District, Beijing 100053, China. 2 Depart-
ment of Intensive Care Medicine, Xuanwu Hospital, Capital Medical University,
Beijing, China.
Received: 4 April 2022 Accepted: 28 April 2022
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Background: Atherosclerosis leads to the occurrence of cardiovascular diseases. However, the molecular mechanisms that contribute to atherosclerotic plaque rupture are incompletely characterized. We aimed to identify the genes related to atherosclerotic plaque progression that could serve as novel biomarkers and interventional targets for plaque progression. Methods: The datasets of GSE28829 in early vs. advanced atherosclerotic plaques and those of GSE41571 in stable vs. ruptured plaques from Gene Expression Omnibus (GEO) were analyzed by using bioinformatics methods. In addition, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the expression level of core genes in a mouse atherosclerosis model. Results: There were 29 common differentially expressed genes (DEGs) between the GSE28829 and GSE41571 datasets, and the DEGs were mainly enriched in the chemokine signaling pathway and the Staphylococcus aureus infection pathway (P<0.05). We identified 6 core genes (FPR3, CCL18, MS4A4A, CXCR4, CXCL2, and C1QB) in the protein-protein interaction (PPI) network, 3 of which (CXCR4, CXCL2, and CCL18) were markedly enriched in the chemokine signaling pathway. qRT-PCR analysis showed that the messenger RNA levels of two core genes (CXCR4 and CXCL2) increased significantly during plaque progression in the mouse atherosclerosis model. Conclusions: In summary, bioinformatics techniques proved useful for the screening and identification of novel biomarkers of disease. A total of 29 DEGs and 6 core genes were linked to atherosclerotic plaque progression, in particular the CXCR4 and CXCL2 genes.
Article
Long noncoding RNAs (lncRNAs) have been increasingly accepted to function importantly in human diseases by serving as competing endogenous RNAs (ceRNAs). To date, the ceRNA mechanisms of lncRNAs in the progression of atherosclerosis (AS) remain largely unclear. On the basis of ceRNA theory, we implemented a multistep computational analysis to construct an lncRNA-mRNA network for AS progression (ASpLMN). The probe reannotation method and microRNA-target interactions from databases were systematically integrated. Three lncRNAs (GS1-358P8.4, OIP5-AS1, and TUG1) with central topological features in the ASpLMN were firstly identified. By using subnetwork analysis, we then obtained two highly clustered modules and one dysregulated module from the ASpLMN network. These modules, sharing three lncRNAs (GS1-358P8.4, OIP5-AS1, and RP11-690D19.3), were significantly enriched in biological pathways such as regulation of actin cytoskeleton, tryptophan metabolism, lysosome, and arginine and proline metabolism. In addition, random walking in the ASpLMN network indicated that lncRNA RP1-39G22.7 and MBNL1-AS1 may also play an essential role in the pathology of AS progression. The identified six lncRNAs from the aforementioned steps could distinguish advanced- from early-staged AS, with a strong diagnostic power for AS occurrence. In conclusion, the results of this study will improve our understanding about the ceRNA-mediated regulatory mechanisms in AS progression, and provide novel lncRNAs as biomarkers or therapeutic targets for acute cardiovascular events.
Article
Background Plaque rupture (PR) and plaque erosion (PE) are the two major pathological phenotypes in acute coronary syndrome. Since microRNAs have been found to be involved in the mechanisms of PR and PE, we investigated the diagnostic utility of microRNAs in differentiating between patients with PR and patients with PE. Methods MicroRNA sequencing was performed on plasma from 21 patients with PR, 20 patients with PE and 17 healthy control subjects (HCs). 24 miRNAs were selected for validation in 20 PR patients and 20 PE patients and 8 miRNAs were further validated in an independent replication cohort (82 patients with PR, 84 patients with PE and 59 HCs) by applying quantitative real-time polymerase chain reaction. Then we analyzed pathways associated with significant miRNAs in PR. Results MiR-744-3p, miR-324-3p and miR-330-3p were significantly upregulated in the PR group compared with the PE group (Log10miR-744-3p: 0.26[‐−0.28—1.57] versus −0.41[−0.83—-0.03], padj < 0.001; Log10miR-324-3p: 0.40[−0.09—0.84] versus −0.12[−0.53—0.29], padj < 0.001; Log10miR-330-3p: 0.34[0.08—0.93] versus −0.07[−0.65—0.22], padj < 0.001), The area under the receiver operating characteristic curve for the combination of these three miRNAs in distinguishing between PR from PE in training and test set was 0.764 (0.679–0.850, sensitivity = 86.2%, specificity = 54.4%, P < 0.001) and 0.768 (0.637–0.898, sensitivity,65.4%, specificity:80.0%, P = 0.001), respectively. Conclusion A set of circulating microRNAs (miR-744-3p, miR-330-3p, and miR-324-3p) is associated with PR and has clinical utility as a diagnostic marker for distinguishing the plaque phenotype in STEMI patients.