Xiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174
https://doi.org/10.1186/s12881-020-01108-9

RESEARCH

Open Access

Deep vein thrombosis inhibitor may play atherapeutic role in post-stroke patients
Xixi Xiang1, Di Yuan2, Peiyan Kong1, Ting Chen1, Han Yao1, Shijia Lin1, Xi Zhang1*

and Hongbao Cao3,4,5*

From 11th International Young Scientists School “Systems Biology and Bioinformatics” – SBB-2019
Novosibirsk, Russia. 24-28 June 2019

Abstract
Background: Deep vein thrombosis (DVT) is associated with stroke. Here, we hypothesize that genes associatedwith DVT may also play roles in the development of stroke.
Methods: we firstly conducted large-scale literature based disease-gene relationship data analysis to explore thegenes implicated with DVT and stroke. Further, a mega-analysis was conducted for each of these genes that werelinked to DVT but not stroke, using 11 independent stroke RNA expression datasets (176 stroke cases and 102
healthy controls). Then, a multiple linear regression (MLR) model was employed to study possible influential factors
on the gene expression levels in stroke. After that, a functional pathway analysis was performed to identify thepotential biological linkage between stroke and the target genes suggested by mega-analysis.
Results: Over 81.10% genes implicated with DVT also suggested an association with stroke. Among the 24 DVTspecific genes, one DVT-inhibiting gene, SP1, presented significantly increased expression in stroke (LFC = 1.34, pvalue = 0.0045). Pathway analysis showed that SP1 may play a therapeutic role in post-stroke patients by promoting
multiple of stroke-inhibitors. Moreover, geographical region was indicated as an influential factor on the expression
levels of SP1 in stroke samples (p-value = 0.037).
Conclusion: Our results suggested that DVT inhibitor SP1 could be a novel therapeutic target gene for post-stroke
treatment. Further study of the potential relations between SP1 and stroke was guaranteed.
Keywords: Deep vein thrombosis, Stroke, Mega-analysis, Pathway analysis, Multiple linear regression analysis

Background
Deep vein thrombosis (DVT) has been well-known to be
associated with stroke [1, 2], which is also supported bymany clinical studies [3–5]. The genetic and genomic
variants implicated in stroke patients and DVT susceptibilities may partially explain the association between* Correspondence: zhangxxi@sina.com; caohon2010@gmail.com
1
State Key Laboratory of Trauma, Burns and Combined Injury; Medical Centerof Hematology, The Second Affiliated Hospital of Army Medical University,
Key Subject of Chongqing, No. 83 Xinqiao Street, Shapingba District,
Chongqing 400037, PR China
3
Department of Psychiatry, First Hospital/First Clinical Medical College of
Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
Full list of author information is available at the end of the article

DVT and stroke. For instance, the expression and activity of matrix metalloproteinase-9 (MMP9) were significantly increased during stroke in human [6] and wereassociated with the early phase of DVT resolution [7].
These previous findings have paved a solid foundation
for the study of associations between stroke and DVT.
Previously, several studies indicated the possible connection between DVT and stroke. For example, researchers from Danish found a strong linkage between
hospital admission for venous thromboembolism andstroke through a 20-year cohort study. They reported
that patients having the DVT are more likely to get
attacked by stroke, especially in the first year after© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Xiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174

having a DVT [8]. Here, we explore the relation between
DVT and stroke at the genetic level, trying to identify
novel common genes that associate with both diseases.
We hypothesize that genes of DVT with increased activity or expression levels may also play roles for the etiological development of stroke.

Methods
To test our hypothesis, we organized the workflow asfollows. First, a large-scale literature-based mining effort
for DVT- and stroke-gene sets was undertaken. Then,for each of the DVT-specific genes, a mega-analysis andwas conducted with 11 publicly available stroke expression datasets retrieved from Gene Expression Omnibus(GEO) (https://www.ncbi.nlm.nih.gov/geo/). For thesegenes that showed significant expression change acrossanalyzed datasets, a literature-based functional pathway
analysis was conducted, then predictions on their pathogenic significance in stroke were made. In addition, amultiple linear regression (MLR) model was employed
to study the possible influence of sample size, sample organism, population region, and study date on the gene
expression levels in stroke.
Large-scale literature based disease-gene relationshipdata analysis

Relation data for both DVT and stroke were extracted
from existing literature and analyzed using Pathway Studio (www.pathwaystudio.com) and then were downloaded
into a genetic database DVT_Stroke, hosted at http://database.gousinfo.com. The downloadable format of the database in excel is available at http://gousinfo.com/database/
Data_Genetic/DVT_Stroke.xlsx. Besides the list of analyzed genes (DVT_Stroke→DVT_ genes, Stroke_ genes,
and DVT_specific genes), supporting references for eachdisease-gene relation are presented at DVT_Stroke→DVT_relation and Stroke_relation, including titles of
the references and the sentences describing identified
disease-gene relationships. The information could be used
to locate a detailed description of an association of a candidate gene with DVT and/or Stroke.
Stroke RNA expression datasets selection for megaanalysis

All expression datasets were searched at GEO through a
keyword ‘stroke’. Then, the following standards were applied to do the further filter: 1) The entry type is series; 2)
The study type is RNA expression by array; and 3) the
studies are performed according to case-control design.
To note, the selection of the data covers all stroke expression array datasets from GEO, which is owned by
the National Institutes of Health (NIH of USA). The
datasets are publicly available, and no permission orconfirmation is needed from any individual investigators.

Page 2 of 6

Moreover, datasets extraction has no selection bias interms of publication journals, owner affiliations, and authors. In addition, the original data rather than the processed results of each dataset were used to perform theanalysis in this study, which makes the process a megaanalysis rather than meta-analysis to avoid possible noisecaused by individual data process.
Mega-analysis models

Both the fixed-effect model and random-effects model[9] were employed to study the effect size of DVT
related genes in case of stroke. For each of the strokeexpression dataset, the log fold change (LFC) was calculated for the stroke samples and used as the index ofeffect size in mega-analysis. The expression data werenormalized and log2-transformed if not done in the original dataset. Results from both models were reported
and compared. The heterogeneity of the mega-analysis
was analyzed to study the variance within and between
different studies. In the case that the total variance Q is
equal to or smaller than the expected between-study
variance df, the statistic ISq = 100% x (Q-df)/Q will be
set as 0, and a fixed-effect model was selected for themega-analysis. Otherwise, a random-effects model was
selected. The Q-p represents the probability that the
total variance is coming from within-study only. All analysis was conducted by using MATLAB (R2017a) megaanalysis package.
Results from mega-analysis and significant genes wereidentified following the criteria: p < 0.005 and effect size(LFC) > 1 or < − 1 and presented in the DVT_Stroke→Mega-analysis. The discussion will be focused on thegene that satisfies the significant criteria.
Multiple linear regression analysis for the risk factors on
strokeWe applied a multiple linear regression analysis to study
the possible influence of four factors on the gene expression change in stroke: sample size, sample organism,
population region, and study age. P-values and 95% confidence interval (CI) were reported for each of the factors. The analysis was done in Matlab (R 2017a) withthe ‘regress’ statistical analysis package.
Functionality literature-based pathway analysis

A literature-based functional pathway analysis was conducted with an aim to identify the potential biologicallinkage between stroke and the target genes selectedfrom mega-analysis. The pathway analysis was
performed using the ‘Shortest Path’ module of Pathway
Studio (www.pathwaystudio.com). A follow-up megaanalysis was conducted to validate the performance ofthe genes involved in this functional pathway.

Xiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174

Results

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Table 2 Gene that passed the significant criteria for being
involved in both DVT and stroke

DVT-stroke implicated genes

Pathway Studio guided literature data-mining for thegenes associated with DVT yielded 127 genes, while
stroke was associated with 1220 genes. A significant
overlap between DVT- and stroke-genes (103 genes; pvalue = 2.04e-81), only 18.89% of the DVT-related genes(24 genes) have not been implicated in stroke. The fulllist of DVT- and stroke-genes, and 24 DVT alone genes
are presented in DVT_Stroke→DVT_genes, stroke_
genes, and DVT_specific genes.

SP1

Gene Name
Mega-analysis

Multiple linear regression analysis

Mega-analysis results

As shown in Table 1, a total of 11 independent RNA expression datasets qualified the filter criteria were utilized
for the mega-analysis (176 cases VS. 102 healthy controls),
which were distributed in six different countries, three samples organisms, and study age ranged from 6 to 13 years.
For each of the 24 DVT-specific genes, a log foldchange (LFC) was estimated from the majority of the 11
studies. However, only one gene, SP1, satisfied the significance criteria (p < 0.005 and effect size (LFC) > 1).
Specifically, SP1 presented significant increased LFC incase of stroke in mega-analysis. The effect sizes and related statistics results were presented in Table 2. Heterogeneity analysis results showed that, for SP1, there wassignificant between-study variance for mega-analysis(ISq = 96.1, p-value-Q < 0.001), and therefore, a randomeffects model was selected (Fig. 1).
MLR analysis results

Results from the MLR models showed that population
region (country) was a significant influencing factor forthe expression fold change of SP1 (p-value = 0.037), as
shown in Table 2. Sample size and study age were notsignificant influential risk factors. It should also be notedthat the sample organisms of the nine datasets were

Random Effects Model

Yes

Datasets included

9

LFC

1.34

STD of LFC

0.51

p-value

0.0045ISQ

96.11

p-value-Q< 0.001

Sample Size

0.96

Population Region

0.037

Sample Organism

0.095

Study Age

0.12

different, including Rattus norvegicus, Mus musculus,
and Homo sapiens (Table 1). However, different sample
organisms presented no significant impact on the expression fold change of SP1 (p-value = 0.095).
Literature-based pathway analysis results

According to the previous large-scale literature-based
approach selected for identification of novel strokerelated genes, no prior direct relations to the pathogenesis of stroke were known for SP1. However, Pathway
Studio-guided the ‘shortest path’ revealed multiple pathways connecting SP1 and stroke (Fig. 2), which were
supported by at least one reference (See DVT_Stroke→ShortestPath). The pathways indicated two potential
mechanisms that SP1 may play a protective role against
the development of stroke: 1) SP1 promotes strokeinhibitors (see Fig. 2, highlighted by green); 2) SP1 inhibits stroke-promoters (see Fig. 2, highlighted by red).
To validate the pathways built using the ‘Shortest
path’, we conducted a meta-analysis using the 12

Table 1 Datasets selection for mega-analysis
Study Name

DatasetGEOID

Sample size (control/case)

Country

Study Age

Sample Organism

Jickling et al.,2014

GSE21136

6/24

USA

6

Rattus norvegicus

White et al.,2014

GSE30655

6/14

USA

6

Mus musculus

Buga et al.,2014

GSE55260

2/4

Germany

6

Rattus norvegicus

Stamova et al.,2014

GSE58294

23/69

USA

6

Homo sapiens

Barreto et al.,2013

GSE28731

4/6

USA

7

Mus musculus

Liu et al.,2013

GSE46267

1/6

Singapore

7

Rattus norvegicus

Krug et al.,2011

GSE22255

20/20

Portugal9

Homo sapiens

Mitsios et al.,2007

GSE9391

3/3

United Kingdom

13

Homo sapiens

Milbauer et al.,2007

GSE9877

27/20

USA

13

Homo sapiens

Wang et al.,2013

GSE38037

4/4

USA

7

Rattus norvegicus

Hori et al.,2012

GSE28201

2/2

Japan

8

Mus musculus

Xiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174

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Fig. 1 Mega-analysis results for in the case of stroke. The green bars on the right represent the weight of each datasets; the red stars on the left
are the log fold change (LFC) of SP1 within each dataset; the width of the horizontal bar on the left represents the 95% confidence level of thecorresponding LFC of SP1

datasets employed in this study, and encode the genes
by color (see the color bar of Fig. 2). The detailed resultsof mega-analysis of these genes were presented in DVT_
Stroke→ShortestPath_Mega. Results showed that thepathways where SP1 promoting stroke-inhibitors wereactivated, while the ones where SP1 inhibiting strokepromoters pathways were not. For the detailed megaanalysis results of the genes in Fig. 2, including p-value
and log fold change, please refer to DVT_Stroke➔SP1
promoting stroke-inhibitors.

Discussion
Our study is a novel approach to identify the not-yet described molecular pathways which associate the processof DVT and the development of stroke. By removing all
known intersections between curated genomes involvedin each of these pathophysiological processes, we ensured that 24 DVT-specific genes to stroke had not been
already described as such. Then, 11 stroke RNA arrayexpression datasets acquired from GEO (Table 1) were
utilized to test the correlation between each of these 24
genes and stroke.
Results from mega-analysis showed that expression
levels of eight DVT-alone genes were significantly changed in stroke-cases as compared to normal controls (pvalue < 0.05, see in DVT_Stroke→Mega_Analysis).

However, only one of eight DVT-alone genes, SP1,
passed the pre-selected significance of association criteria (p < 0.005 and LFC > 1) (Table 2, Fig. 1). In specialty, LFC of SP1 was 1.34 from mega-analysis,
demonstrating the changes of SP1 were increased bymore than 150% (Table 2), suggesting it was a potential
stroke biomarker and maybe possibly involved in the development of stroke. Although sample size, sample age,and sample organism of 11 datasets were used in themega-analysis, the population region (country) was the
only factor that could affect the expression of SP1 incase of stroke (p = 0.037, Table 2).
As shown in Fig. 2 (a), SP1 acts as a hub-gene constructing multiple potential pathways that could contribute to stroke based on “promoter binding” and“expression” levels. In order to confirm the pathways(edges) presented in Fig. 2, a further mega-analysis wasused to test the performance of the pathway genes inFig. 2, using the 11 datasets employed in this study(Table 1). Results showed that not all the pathways in
Fig. 2 were supported by the 11 datasets, which was
expected. The pathways built in Fig. 2 were literaturebased, which integrated information from different modality of data with varied platforms. However, majorityof the activity of the genes were confirmed from the expression datasets and strengthened the validity of theXiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174

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Fig. 2 Functional pathway between stroke and gene SP1. The pathway was firstly built by using Pathway Studio, where the relations between
any of the two entities were supported by one or more references. Then, a meta-analysis has been conducted for each of the genes presented in
the pathway using the 12 datasets employed in this study. The genes are color-coded by their expression log fold change. The genes
highlighted by green were supported by the meta-analysis results, while the red-highlighted ones were not

identified pathway. As shown in Fig. 2, SP1 promotes 7
inhibitors of stroke, including PTGS2, L10, IGF1, LEP,
ENTPD1, HSPA1A and HIF1A. However, the literaturebased pathways also suggested that SP1 could inhibit
three stroke-promoters (IL1B, TGFB1, and ACE), which
was not supported by the 11 datasets employed in thisstudy (these genes demonstrated increased expression
levels in the case of increased SP1 expression). These results suggest that SP1 is more likely to play a therapeuticrole, rather than preventive role, in the pathological development of stroke.
The protein encoded by SP1 is a zinc finger transcription factor that binds to GC-rich motifs of many promoters. It involves in many cellular processes, including
cell differentiation, cell growth, apoptosis, immune responses, response to DNA damage, and chromatin remodeling. In principle, variations in promoter sequences
can alter gene expression directly by altering a transcription factor binding site, and promoter variants with effects on the transcriptional activity of certain humangenes have been identified as disease risk factors [10].
For instance, in the SP1 → LEP→stroke pathway, SP1
binds in the promoter region for the LEP gene. As a result, altered SP1 transcriptional activity leads to promoted production of leptin [11], which ameliorates
neurological deficits and reduces infarct volumes after

stroke [12]. In another pathway, SP1 → IL10 → stroke,
SP1 positively regulates the transcription of IL10 [13],which has been successfully used as a therapeutic mediator to reduce post-stroke secondary neuroinflammation [14]. More pathways have been revealed inFig. 2 with detailed presented in DVT_Stroke→ShortestPath. These pathways got support from the 11 datasets
employed in this study. These results suggested the possible mechanisms through which SP1 plays a post-stroke
therapeutic role.
Although the discussion was mainly focused on thegene SP1 that presented significant expression change inthe case of stroke, other genes with minor expression
variances may also worth a closer look, including PF4(LFC: 0.79; p-value< 10–3), CYP4V2(LFC: 0.72; pvalue = 0.046). Literature based pathway analysis suggested that these two genes may related to stroke
through multiple pathways (see DVT_Stroke: PF4_CYP4V2). However, further study using experimental
data are needed to validate these pathways. In addition,
8 out of the 24 DVT-specific genes were not included in
the 11 stroke expression data collected in this study, andtherefore were not reported in the mega-analysis (see
DVT_Stroke: Mega_Analysis). Analysis with datasets including these genes are needed to explore their potential
role in stroke.

Xiang et al. BMC Medical Genetics 2020, 21(Suppl 1):174

Conclusion
The presented results support the hypothesis that genes
associated with DVT may also play roles in the etiological development of stroke. Enhanced activity of SP1
may contribute to the therapeutic effect in post-stroke
patients by promoting multiple of stroke inhibitors. Further investment is needed to test the conclusion of thisstudy.
Abbreviations
DVT: Deep vein thrombosis; GEO: Gene Expression Omnibus; MLR: multiple
linear regression; LFC: log fold change
Acknowledgements
Not applicable.

Page 6 of 6

3.

4.

5.

6.

7.

8.

About this supplement
This article has been published as part of BMC Medical Genetics Volume 21
Supplement 1, 2020: Selected Topics in “Systems Biology and Bioinformatics”- 2019: medical genetics. The full contents of the supplement are available
online at https://bmcmedgenet.biomedcentral.com/articles/supplements/
volume-21-supplement-1.

9.

Authors’ contributions
XZ, HC and XX designed the study and collected data; XX, DY, PK, TC, HY
and SL contributed to the analysis of the data and paper writing; All authors
contributed to the revise and final approval of the manuscript.

11.

Funding
Authors received no funding for this study. The corresponding authors takethe responsibility for all publication cost.

10.

12.

13.
Availability of data and materials
All data supporting the findings of this study are available from thecorresponding author in response to a reasonable request.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
Author Hongbao Cao was employed by company Elsevier Inc. The remaining
authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.
Author details
1State Key Laboratory of Trauma, Burns and Combined Injury; Medical Centerof Hematology, The Second Affiliated Hospital of Army Medical University,
Key Subject of Chongqing, No. 83 Xinqiao Street, Shapingba District,
Chongqing 400037, PR China. 2Department of Educational Technology,
College of Basic Medical Sciences, Army Medical University, Chongqing
400038, China. 3Department of Psychiatry, First Hospital/First Clinical Medical
College of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China.
4
School of Systems Biology, George Mason University, Fairfax, Virginia 22030,
USA. 5Department of Genomics Research, RD Solutions, Elsevier Inc.,
Rockville, MD 20852, USA.
Published: 22 October 2020
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