Abstract
Expanding data suggest that glioblastoma is accountable for the growing
prevalence of various forms of stroke formation, such as ischemic
stroke and moyamoya disease. However, the underlying deterministic
details are still unspecified. Bioinformatics approaches are designed
to investigate the relationships between two pathogens as well as fill
this study void. Glioblastoma is a form of cancer that typically occurs
in the brain or spinal cord and is highly destructive. A stroke occurs
when a brain region starts to lose blood circulation and prevents
functioning. Moyamoya disorder is a recurrent and recurring arterial
disorder of the brain. To begin, adequate gene expression datasets on
glioblastoma, ischemic stroke, and moyamoya disease were gathered from
various repositories. Then, the association between glioblastoma,
ischemic stroke, and moyamoya was established using the existing
pipelines. The framework was developed as a generalized workflow to
allow for the aggregation of transcriptomic gene expression across
specific tissue; Gene Ontology (GO) and biological pathway, as well as
the validation of such data, are carried out using enrichment studies
such as protein–protein interaction and gold benchmark databases. The
results contribute to a more profound knowledge of the disease
mechanisms and unveil the projected correlations among the diseases.
Keywords: glioblastoma, ischemic stroke, moyamoya, bioinformatics,
association, GSEA, pathway, orthology
1. Introduction
Glioblastoma, generally regarded as glioblastoma-multiforme (GBM), is
the most deadly form of cancer in the brain region throughout the world
[[40]1]. Percival Bailey and Harvey Cushing introduced the name
glioblastoma multiforme in 1926, emphasizing the hypothesis that the
cancerous cells arise from Gila, fundamental drug precursors
(glioblasts). Additionally, it is an utterly volatile presentation
caused by necrosis, hemorrhage, and cysts (multiform) [[41]2]. GBM has
vague signs or symptoms initially. Headaches, mood changes, fatigue,
and symbols close to those of a stroke are all possible symptoms
[[42]3]. Symptoms sometimes escalate quickly, leading to
unconsciousness [[43]4]. Recent studies suggest that astrocytes, brain
stem cells, and oligodendrocyte progenitor cells could all represent
the disease’s biological origin cell [[44]5,[45]6]. Glioblastoma stem
cells have already been observed in patients with GBM, exhibiting
features similar to progenitor cells. Their involvement, along with the
dispersed form of glioblastomas, makes surgical removal impossible and
is thus thought to be a potential source of resistance to medical
therapies and a strong recurrence risk [[46]7]. The disease affects
around three out of every 100,000 people annually, but the rate can be
even higher in certain areas [[47]8]. Glioma has the highest mortality
rate of the different forms of brain tumors, is an unusual curable
shape, is immune to chemotherapy and radiotherapy, and has a poor
prognosis [[48]9].
Ischemic stroke victims are more likely to acquire a brain tumor, most
often a glioma, due to the ridiculous effects of ischemia as well as
the effects of hypoxia on a cell’s functional and metabolic state
[[49]10]. When blood circulation to a portion of the brain is
eventually cut off, an ischemic stroke occurs, which leads to the loss
of neurological control. Ischemia may also occur when blood circulation
to a particular part of the brain is inadequate to satisfy
physiological demands [[50]11,[51]12]. Thus, this causes a lack of
oxygen in the brain (cerebral hypoxia) and, as a result, brain tissue
dies (cerebral infarction/ischemic stroke) [[52]13]. On the other hand,
proliferating cell density, metastasis, and a general prothrombotic
propensity correlated with tumors raise the likelihood of ischemic
infarctions [[53]9]. Several theories have been suggested to explain
why ischemic infarction and brain tumors, especially glioma, occur
together. The most prevalent mechanism highlights how both situations
are prone to hypoxia [[54]14]. Cerebral ischemia, for example, induces
blood flow congestion and predisposes to hypoxia [[55]15]. At the same
time, a quickly increasing malignant mass has a hypoxic heart owing to
the intensified need for oxygen from rapidly dividing cells [[56]16].
Other possible pathways in the interplay between the two systems have
been suggested by researchers, including astrocyte-activation [[57]17],
angiogenesis, reactive-gliosis [[58]18], and various modifications in
the tumor microhabitat [[59]19]; some of which are primarily caused by
cerebral ischemia as a result of quick glioma growth [[60]9]. Moreover,
frequent removal surgery (operation of any tissue or part of an organ)
to treat gliomas raises the possibility of ischemic injury [[61]20].
Moyamoya disease is a form of arterial occlusive disease that most
frequently damages the brain’s carotid arteries. More specifically,
carotid arteries narrow down or become blocked in the brain region,
limiting blood supply to the brain [[62]21]. It is identified by the
angiographic characteristics of bilateral central carotid artery
stenosis and unwanted expansion of the favoring veins in the brain’s
center [[63]22]. While the cause of primary moyamoya disease is
unclear, moyamoya disease may be brought about by a number of different
pathogenic reasons. Internal carotid arteries might become blocked as a
result of intracranial basal tumors or radiotherapy, resulting in the
formation of moyamoya-type vessels [[64]23,[65]24]. Hence, this is
related to “leptomeningeal artery end-to-end anastomoses”, “transdural
anastomoses”, and ’telangiectatic collaterals”, which are the most
popular inside and outside areas of the basal ganglia [[66]25]. In
recent research studies, the relationship between glioblastoma and mm
has been described [[67]25,[68]26].
In conclusion, there is convincing evidence that GBM, I. stroke, and mm
have pathologically and medically significant connections, although
this connection has not been thoroughly investigated. Since the
etiology of GBM, I. stroke, and mm are complicated, and their risk
factors differ in specific ways, the underlying relationship’s in
biological aspects, and molecular mechanisms are still unknown. Despite
their strong therapeutic relevance, GBM, I. stroke, and mm are very
complicated disorders in terms of their clinical manifestations, making
them challenging to analyze using traditional hypothesis-driven
endocrinology analysis. Furthermore, there is still a scarcity of
bioinformatics research on the issues discussed. The objective of this
project was to find certain connections among diseases since knowing
the existence of these connections could provide valuable insights into
the diseases’ mechanisms. Therefore, this prompted us to design a
bioinformatics framework to identify the essence of the interaction,
such as gene expression and dysregulation, signaling pathways, and
protein–protein interactions analyzed from disease-affected tissues.
Results were then validated using experimentally validated gold
benchmark databases and literature such as DisGeNET, db-GaP, and
Rare-Diseases-AutoRIF.
2. Substances, Procedures, and Methods
2.1. Collected Datasets
The datasets included for the analysis were derived from the National
Center for Biotechnology Information (NCBI), a well-known Gene
Expression Omnibus (GEO) database. Each disease’s query returns a
series of datasets. If the dataset is obtained from a non-human species
and does not meet two criteria for each group, such as control samples
(healthy) and case samples (patients), it is not preferred for our
study. Additionally, we discarded repeated datasets, unfavorable
formatting, or insignificant experimental emphasis. We also excluded
datasets with sample sizes smaller than our preselected cutoff sample
size of three for each group. Linear regression is used to analyze the
transcriptomic differential expression of the selected GEO datasets,
and a linear model may have appropriate analytical strength when the
sample size for either the healthy or the patient is three, or higher
than three [[69]27]. In addition, we concentrated on a particular cell
or tissue type in light of its influence on the course of a disease.
This method resulted in selecting three strongly important datasets for
glioblastoma, I. stroke, and moyamoya (mm) as well as suitable for the
analysis. The datasets for glioblastoma and ischemic stroke are
RNA-seq, and the dataset for moyamoya disease is micro-array. As no
RNA-seq datasets met our requirements, we used the microarray dataset
for moyamoya. We looked for datasets with the lowest amount of biases
and distortion for this study. For the analysis, we selected
transcriptome RNA-seq/microarray datasets of human participants with
the accession numbers [70]GSE106804, [71]GSE56267, and [72]GSE131293,
which included both healthy and diseased patients.
The glioblastoma dataset ([73]GSE106804) included gene expression data
from the Extracellular Vesicle of 13 glioblastoma patients and 6
healthy controls [[74]28]. GBM is constantly in contact with its
underlying tumor microenvironment (TME). Extracellular vesicle has a
significant effect on the GBM tumor microenvironment, paving the path
for the development of GBM [[75]29,[76]30]. Hence, we selected the
dataset. The I. stroke ([77]GSE56267) dataset included gene expression
evidence from the cortical tissue of seven I. stroke patients and six
healthy controls, whereas cortical neurons depict important intact
genome information regarding I. stroke patients [[78]31]. The moyamoya
dataset ([79]GSE131293), the only microarray data, included gene
expression results from three patients and three stable controls’
neural crest stem cells [[80]32].
2.2. Preprocessing and Distinction of Differentially Expressed Genes
As mentioned earlier, the datasets were collected from NCBI. We
performed differential expression analysis to detect the genes that are
noticeably expressed in patients’ samples compared to healthy samples.
We performed differential expression analysis (DEA) of RNA-seq raw
count data using DESeq2, an R package. The internal normalization
technique was carried out using DESeq2 and determined the geometric
mean of every gene across all samples. Then, the negative binomial
distribution, a linear model, was calculated for each gene, considering
variability among samples. Finally, notable genes were filtered using
the Wald test and we automatically removed low-expressed/outlier genes
using Cook’s distance [[81]33]. For microarray data, we used Limma,
also a linear model, for DEA, which performed a t-test to find the
importance of every gene over samples [[82]34]. The code for DEA was
implemented in R and can be accessed through our Github repository:
[83]https://github.com/hiddenntreasure/glioblastoma, accessed on 11
July 2022.
We used the Z-score transformation (
[MATH:
Zmn
:MATH]
) for each disease phenotype to make the gene expression data more
comparable. The equation for this transformation is
[MATH:
Zmn=gmn−X¯σm<
/mi> :MATH]
(1)
where
[MATH: σm :MATH]
indicates standard deviation and
[MATH:
gmn
:MATH]
suggests the magnitude of the gene (m) in the sample (n). Thus, this
allows us to directly measure the expression of genes across samples
and types of cells from various disorders.
We discarded the genes with missing or null values. Two parameters are
deployed to derive the most significant/biomarker genes accountable for
the emergence of a disease. First, the p-value should be less than
0.05; secondly, the absolute value of the log2-fold change is either 1
or greater/less than 1. Genes with a logFC greater than one are highly
expressed compared to the other genes and are known as upregulated
(up-reg) genes, whereas downregulated (down-reg) genes are lower
expressed in contrast to gene expression arrays and logFC is less than
1. We have several significant genes for each disease that are
differentially dysregulated and significantly liable for developing a
disease. Then, we identified shared genes between a pair: glioblastoma
and I. stroke, as well as glioblastoma and moyamoya.
The prevalent genes in these two pairs of diseases were then used to
build a gene-disease network (GDN), and different neighbors were found
using Jaccard coefficient methods [[84]35], which is the co-occurrence
score. In contrast, the edge (connection among genes) predicts the
correlation coefficient rate for the nodes (genes):
[MATH:
E(m,n)=N(Gm∩Gn)N(Gm∩Gn) :MATH]
(2)
G indicates the total number of genes represented as nodes, and E
denotes the number of connections among genes represented as edges. To
cross-check illness comorbidity relationships, we used the R programs
comoR [[85]36] and POGO [[86]37].
2.3. Enrichment Analysis for Significant Gene Ontology and Molecular Pathway
Selection
Previously, gene expression profiling generally consisted of a group of
genes corresponding to either healthy or affected samples, enlisted in
a list L as per their differential expression. A meaningful
understanding of this list was extracted. However, in a given
biological process, it may provide an insufficient number or an
excessive number of statistically significant genes that might
fluctuate from one dissertation to another for a given batch of genes
[[87]38,[88]39]. However, enrichment analysis denotes a normalized set
of genes that employs previously identified molecular pathways or gene
expression arrays. Moreover, it defined the group of genes associated
with the different genotypes (phenotypes) hypothesis [[89]40].
EnrichR was employed to acquire a deeper insight into the biological
pathways and Gene Ontology (GO) terms associated with GBM in relation
to I. stroke and mm [[90]41]. It conducts GSEA to classify the DEGs’
corresponding pathways and GOs. Compared with a catalog of
well-annotated gene sets, such as pathway analysis, it facilitates
observing the functional relevance of the given gene set. The pathway
is the molecular biology concept, which defines an artificial condensed
process model within a cell or tissue [[91]42]. A typical pathway model
begins across an external signaling molecule by provoking a specific
receptor that triggers a string of proteins connected with each other
[[92]43]. The Gene Ontology (GO) is a computational paradigm for
representing gene (protein) functions as well as their related
connections towards other genes [[93]44]. The hierarchical arrangement
of the GO makes it possible to compare proteins annotated with
different meanings in ontology as well as have relationships with each
other. We focused on four different pathway databases: KEGG [[94]45],
BioCarta [[95]46], Reactome [[96]47], and Wiki-Pathways [[97]48]; and
biological Process (BP) from Gene Ontology (GO) domain [[98]49].
2.4. Analysis of Protein–Protein Interactions (PPIs)
The PPIs are central to all cellular/molecular mechanisms since they
constitute the physical interactions between two or even more protein
components [[99]50]. We used data from the STRING database [[100]51]
and Network Analyst [[101]52] to create PPI networks centered on the
connections among various proteins. We used the String Interactome
repository from “String-db.org” (accessed on 28 October 2014) with a
confidence level of 800 and topological criteria such as degree >15
[[102]51]. Proteins are denoted by colored circles/nodes; conversely,
connections of the proteins are characterized by edges.
2.5. Analysis of Transcription Factors (TFs) and microRNAs (miRNAs)
We discovered DEGs-TFs, which regulate the identified significant genes
(identified from transcriptomic differential analysis) not only at
their correct period but also at their suitable volume in a cell
throughout the cell’s/organism’s lifetime, and are responsible for
determining the transformation of genetic information from DNA to mRNA
at the transcription level. Furthermore, gene-miRNAs were also
discovered in order to help researchers by giving insight into the
regulatory biomolecules that determine and control RNA splicing and
expression of genes at their post-transcriptional level.
EnrichR was deployed to identify the DEGs-TFs and microRNAs [[103]41].
The DEGs-TFs relationship was identified and studied using the JASPAR
database [[104]53] and ENCODE [[105]54,[106]55], whereas miRNA-DEGs
interactions are found using a well-known database called TarBase
[[107]56] and miRTarBase [[108]57]. The topological investigation was
carried out using Cytoscape’s Network Analyzer and Network Analyst
[[109]58,[110]59].
2.6. Drug Prediction
Network Analyst was used to identify the possible medications for
treating glioblastoma and its associated diseases. The drug was
predicted using the DrugBank database version 5.0. [[111]60]. A list of
protein–drug interactions was made based on statistical importance. Two
protein–drug interactions were predicted for two pairs of cases, such
as glioblastoma and I. stroke, and glioblastoma and mm. In our study,
we utilized highly interacted shared proteins (hub proteins) found from
both pairs of cases.
2.7. Description of the Experimental Methodology
[112]Figure 1 summarizes the network-based systemic and computational
framework for evaluating differentially expressed human genes due to
the association among diseases. The R code was used to introduce the
optimized pipeline, and the implementation is accessible from our
Github repository:
[113]https://github.com/hiddenntreasure/glioblastoma, accessed on 11
July 2022.
Figure 1.
[114]Figure 1
[115]Open in a new tab
Demonstration of the work flow of our hypothesized methodology.
To identify hypothesized selective biomarkers between GBM and I. stroke
and GBM and mm, we used gene expression analyses using limma. Moreover,
we extracted signaling pathways and GO terminologies from various
databases, as well as protein–protein interactions (PPIs),
transcription factors (TFs) of genes, and gene-MicroRNAs (miRNAs) that
are related to the derived biomarkers. Our network-based method was
cross-checked with the three gold standard databases, namely, DisGeNET,
db-GaP, and Rare-Diseases-AutoRIF, to validate our biomarker genes and
pathways.
3. Result Analysis
3.1. Evaluation of Gene Expression
“Expression profiling by high-throughput sequencing” (or RNA-seq) data
of GBM was reviewed from the NCBI to categorize and comprehend the gene
enrichment that could influence the development of I. stroke and mm.
However, due to the unavailability of moyamoya’s RNA-seq data, we
collected “expression profiling by array” (or microarray) data of
moyamoya.
A well-known project called Bioconductor established R packages called
Limma and DESeq2 for microarray and RNA-seq data. We used it to perform
expression profiling and found 3585 DEGs in glioblastoma with a p-value
less than 0.05 and an absolute logFC greater than 1. Whereas 1038 genes
are upregulated due to foreign signals increasing the cellular process
factor in all genes, 2547 genes are downregulated due to the same
component decreasing markedly. Following the statistical study, we
identified the most significant DEGs for each disease, such as I.
stroke and moyamoya. [116]Table 1 illustrates that 1465 significant
DEGs were found in I. stroke, whereas the expression increased (up-reg)
in 1120 genes and expression decreased (down-reg) in 345 genes;
similarly, 1382 significant DEGs were found in mm, whereas the
expression increased (up-reg) in 715 genes and expression decreased
(down-reg) in 667 genes. The GSE accession numbers for the selected
study are [117]GSE106804 [[118]28], [119]GSE56267 [[120]31], and
[121]GSE131293 [[122]32] for glioblastoma, ischemic stroke, and
moyamoya, respectively, as shown in [123]Table 1.
Table 1.
Detailed information about the selected transcriptomic datasets from
NCBI that meet all the criteria.
Disorder Source Dataset Raw Case Control Significant Up Significant
Down
Name Tissues/Cells Accession No. Genes Samples Samples Reg. Genes Reg.
Genes
Glioblastoma Extracellular GSE-106804 59,171 13 6 1038 2547
Vesicle
Ischemic Stroke Cortical ischemic stroke tissue GSE-56267 28,089 7 6
1120 345
Moyamoya Neural crest GSE-131293 54,675 3 3 715 667
stem cell
[124]Open in a new tab
Due to the proper data availability, we took the dataset from three
different cells: extracellular vesicle, cortical ischemic stroke
tissue, and neural crest stem cell for GBM, I. stroke, and mm,
respectively ([125]Table 1, column 3). However, the findings still show
insightful outcomes for our projected hypothesis. Column 2 in
[126]Table 1 demonstrates the RNA sequencing technology used to
identify the transcriptomic data for each disease in our study. The
number of samples for both cases and controls is an essential
identifier in identifying associations among diseases because the
increasing number of samples enhances the computational power of a
dataset. In our study, moyamoya has only three samples, both for
control and case, which is the least, whereas the other two diseases
have at least six samples for either side. The overall up- and
downregulated genes are quite balanced for moyamoya, though not for GBM
and I. stroke.
3.2. Identified Enriched Pathways and Gene Ontology Terminologies
Pathway enrichment analysis was implemented to better understand the
molecular mechanisms/processes that underlie all complicated diseases.
Using EnrichR [[127]41], a bioinformatics resource, we conducted a
comparison-based enrichment analysis to classify overexpressed pathways
in our relationship (GBM and I. stroke; or GBM and MM), and the
analysis was performed on top of three different databases
(Wikipathways (human-2019) [[128]61], BioCarta (2016) [[129]41], and
KEGG (human-2019) [[130]62]) in our experiment. The pathway enrichment
experiments were performed using the common DEGs between GBM and its
associated diseases (I. stroke and mm). We carried out regulatory
research to learn more about the molecular mechanisms that play a role
in this comorbidity. Our research identified overexpressed pathways in
which DEGs are identified in various disorders and categorized them
based on their functional importance. Manual curation was used to limit
pathways considered greatly enriched in the typical DEG sets with
p-value criteria. The criteria denote that the p-value must be less
than 0.05. EnrichR discovered major pathways from KEGG, WikiPathways,
and BioCarta databases that are significantly linked to DEGs that are
common between GBM and I. stroke pair and GBM and mm pairs. Using the
shared 50 genes between GBM and mm, we obtained 149 shared pathways,
among which 20 are significant, considering the p-value (<0.05).
Similarly, 59 genes are common between GBM and I. stroke; we obtained
217 signaling pathways common between them, and 68 are highly expressed
(significant pathways). Thus, ascending sorting of p-value implied
retrieving the top 15 significant pathways between (a) GBM and I.
stroke—[131]Table 2 and (b) GBM and mm—[132]Table 3.
Table 2.
List of top-15 highly-expressed pathways between GBM and I. stroke.
Pathway Name p-Value Database
Phagosome 4.6 × 10^−6 KEGG-orthologs
Staphylococcus aureus infection 4.21 × 10^−5 KEGG-orthologs
Photodynamic therapy induced HIF-1 survival signaling 0.000159
Wiki-Pathways
Leukocyte transendothelial migration 0.000292 KEGG-orthologs
Intestinal immune network for IgA production 0.000346 KEGG-orthologs
Antigen Processing and Presentation 0.0005171 BioCarta
Complement and Coagulation Cascades WP558 0.000605 Wiki-Pathways
Lung fibrosis WP3624 0.00077 Wiki-Pathways
Cell adhesion molecules (CAMs) 0.000777 KEGG-orthologs
IL 4 signaling pathway 0.000818 BioCarta
Inflammatory bowel disease (IBD) 0.000845 KEGG-orthologs
miR-509-3p alteration of YAP1/ECM axis 0.001055 Wiki-Pathways
Serotonin and anxiety WP3947 0.00105 Wiki-Pathways
Leishmaniasis 0.001232 KEGG
Th1 and Th2 cell differentiation 0.00230 KEGG-orthologs
[133]Open in a new tab
Table 3.
List of top-15 highly-expressed pathways between GBM and moyamoya.
Pathway Name p-Value Database
Serotonin and anxiety 0.000813 Wiki-Pathways
Propanoate metabolism 0.002895921 KEGG-orthologs
GPCRs, Class A Rhodopsin-like WP455 0.003858665 Wiki-Pathways
Leucine, valine, and isoleucine degradation 0.00642032 KEGG-orthologs
Amyotrophic lateral sclerosis 0.007222503 KEGG-orthologs
Neuroactive ligand-receptor interaction 0.010018848 KEGG-orthologs
Cytosolic DNA sensing pathway 0.010854501 KEGG-orthologs
D4-GDI Signaling Pathway 0.014908285 BioCarta
Pertussis 0.015517163 KEGG-orthologs
Peroxisome 0.018324143 KEGG-orthologs
Salmonella infection 0.019588053 KEGG-orthologs
Cardiac Protection Against ROS 0.027165345 BioCarta
C-type lectin receptor signaling pathway 0.027901009 KEGG-orthologs
Antigen Processing and Presentation 0.029598762 BioCarta
AMPK signaling pathway 0.0362706 KEGG-orthologs
[134]Open in a new tab
We also discovered highly expressed Gene Ontology (GO) terms,
especially for identifying molecular events associated with a disease.
Therefore, popular DEGs between two diseases were employed to obtain
the list of GOs associated with a disease. The Enrichr was used to find
GO terms enriched by shared DEGs. Enrichr introduces biological
processes (BP-2016) that are linked to DEGs so that they can be grouped
into functional categories [[135]63,[136]64]. Hence, this helps us
learn more about the molecular processes and biological relevance of
DEGs. It was then narrowed down to only those processes and terms with
a relative p-value below 0.05. Between GBM and mm, 503 GO terminologies
are shared, where 138 are significant GO terms (p-value < 0.05).
Likewise, GBM and I. stroke have 652 shared GO terms, among which 193
are significant. [137]Table 4 and [138]Table 5 summarize the biological
processes discovered, representing only the top 15 GO terms of BP-2016
for both pairs (a) GBM and I. stroke and (b) GBM and mm.
Table 4.
List of significant GO terminologies that are common between GBM and I.
stroke.
Biological Process p-Value GO Id
Platelet degranulation 0.0000000607 GO:0002576
Regulated exocytosis 0.000000204 GO:0045055
Cytokine-mediated signaling pathway 0.00000144 GO:0019221
Extracellular matrix organization 0.00000383 GO:0030198
Regulation of endopeptidase activity 0.0000421 GO:0052548
Neutrophil degranulation 0.0000602 GO:0043312
Neutrophil activation involved in immune response 0.0000638 GO:0002283
Neutrophil mediated immunity 0.0000676 GO:0002446
Replicative senescence 0.000432 GO:0090399
Neutrophil migration 0.000606 GO:1990266
Positive regulation of DNA damage response,
signal transduction by p53 class mediator 0.00071 GO:0043517
Negative regulation of peptidase activity 0.000737 GO:0010466
Interferon-gamma-mediated signaling pathway 0.001049284 GO:0060333
Positive regulation of signal transduction by p53 class mediator
0.00105588 GO:1901798
Defense response to fungus 0.00105588 GO:0050832
[139]Open in a new tab
Table 5.
List of significant GO terminologies that are common between GBM and
moyamoya.
Biological Process p-Value GO Id
B cell activation involved in immune response 0.000469 GO:0002312
Post-transcriptional gene silencing by RNA 0.000913 GO:0035194
Gene silencing by miRNA 0.002719256 GO:0035195
Fatty acid biosynthetic process 0.006162594 GO:0006633
Cell morphogenesis 0.009570011 GO:0000902
Regulation of viral genome replication 0.010854501 GO:0045069
Monocarboxylic acid biosynthetic process 0.012560892 GO:0072330
Lipid biosynthetic process 0.014004804 GO:0008610
Positive regulation of action potential 0.014908285 GO:0045760
Positive regulation of cardiac muscle contraction 0.014908285
GO:0060452
Astrocyte activation 0.014908285 GO:0048143
Negative regulation of type I
interferon-mediated signaling pathway 0.014908285 GO:0060339
Acetyl-CoA biosynthetic process 0.014908285 GO:0006085
Regulation of hematopoietic stem cell differentiation 0.015517163
GO:1902036
Regulation of hematopoietic progenitor cell differentiation 0.015905777
GO:1901532
[140]Open in a new tab
3.3. Protein–Protein Interactions (PPIs) Analysis
With the use of online-based tools such as STRING and Network Analyst,
we built putative PPI networks utilizing our enriched common disease
genes. PPIs try to compensate for the organism’s so-called
interactomics, in which abnormal PPIs cause numerous illnesses. One or
more typically linked protein subnetworks are reported to be
represented by two diseases. PPI analysis revealed strongly interacting
proteins employing topological criteria, such as a degree higher than
15°. [141]Figure 2A shows the PPI network between GBM and mm. The
network includes 59 nodes (genes) and 29 edges; the PPI network’s
enriched p-value is 0.232. [142]Figure 2B demonstrates the PPI network
for GBM and I. stroke, where there are 55 nodes and 65 edges, where the
PPI-network’s enriched p-value is 1.11 × 10^−16.
Figure 2.
[143]Figure 2
[144]Open in a new tab
Protein–protein interactions found using the shared significant genes.
(A) PPI between Glioblastoma and moyamoya. (B) PPI between
glioblanstoma and Ischemic stroke.
The cytoHubba module was used to explore the most significant
hub-proteins based on the simplified PPI networks developed previously
[[145]65]. We found 14 hub proteins between GBM and mm using four
cytoHubba algorithms, and they are MCC, DMNC, Degree, and EPC (as shown
in [146]Figure 3, 11 hub proteins are shared by all the algorithms:
CASP1, PSMA3, PSMA4, TNPO1, PSMA2, MEFV, PSMA6, PSMB9, PSMB1, PYCARD,
and YME1L1, and three are shared by Degree and MCC: AK7, POLR3B, and
POLR3E.
Figure 3.
[147]Figure 3
[148]Open in a new tab
Hub proteins identified using four different cytoHubba algorithms
between Glioblastoma and Moyamoya.
Similarly, we found 26 hub proteins between GBM and I. stroke, as shown
in [149]Figure 4. All the four cytoHubba algorithms share 21 hub
proteins: COL1A1, ANXA2, PPBP, SPARC, TIMP1, SERPINE1, PECAM1, HLA-DRA,
CXCR4, ALOX5AP, S100A12, BCL2A1, HLA-DQA1, LCP2, GNB5, S100A8, PLEK,
ARHGEF9, LCP1, IL2RG, and SLA; two are shared by Degree, MCC, and EPC:
TREML1 and F11R; two are shared by Degree, EPC, and DMNC: SERPINA1 and
NCF2; and only one is shared by Degree and EPC: ANKRD1. Although
further research into the activities of these newly discovered hub
proteins is needed, they might be potential therapeutic targets.
Figure 4.
[150]Figure 4
[151]Open in a new tab
Hub proteins identified using four different cytoHubba algorithms
between glioblastoma and Ischemic stroke.
3.4. Determination of the DEGs’ Transcriptional and Post-Translational
Regulators
Transcription factors (TFs) are nothing but proteins that govern the
expression of the identified significant genes in our case. In other
words, the transcriptional process converts genes into RNA or protein
products. Transcription factors are found in all living organisms and
regulate gene expression. TF genes are significant because they
regulate a variety of biological processes [[152]66,[153]67]. miRNA
plays a vital role in cellular processes and biochemical and molecular
functions [[154]68]. As a result, changes in miRNA levels (enriched
miRNA) may affect metabolic processes, signal transmission, and
transcription [[155]69]. According to this study, microRNAs play a role
in various diverse biological characteristics related to glioblastoma,
such as cell growth, incursion, glioma stem cell activity, and
angiogenesis (blood vessel formation) [[156]70]. Additionally, miRNA
functions may aid in elucidating the dysregulated signaling pathways
and provide insight into the development of novel therapeutic and
diagnostic procedures [[157]71].
In [158]Figure 5, we visualize the DEGs-TFs and DEGs-miRNAs that
controlled the gene expression and multiple BP in a patient with
glioblastoma and/or moyamoya. The transcription- and
post-transcription-level regulatory genes between GBM and mm include
PCCA, YME1L1, PEX26, XYLT2, ZNF71, POLR3E, ZNF76, NRF1, TFDP1, ZNF610,
ZNF101, TNPO1, CNOT7, TMCO3, TGIF2, CEP57L1, IRF1, MAPK13, CREB3L1,
SOX13, LIMD1, RNF8, PSMB9, and FAM111A. The miRNAs, non-coding gene
products, similar for GBM and mm include: miR-522-5p, miR-1-3p,
miR-146a-5p, miR-6499-3p, miR-34a-5p, miR-7977, miR-6778-3p, miR-107,
miR-374a-5p, miR-16-5p, miR-27a-3p, miR-124-3p, miR-128-3p, miR-155-5p,
miR-92a-3p, miR-24-3p, and miR-455-3p. [159]Figure 6 represents the
TF-gene and gene-miRNA that regulate mechanisms of gilobastoma and I.
stroke. TF-genes that are included between GBM and I. stroke are PPARG,
TGIF2, NFIC, SRF, IRF2, GATA3, GABPA, GATA4, NCF2, MT2A, HOXA5,
ALOX5AP, KDM1A, MLKL, SSRP1, S100A8, NFKB1, GABRA1, CXCR4, TMEM71, YY1,
RCOR2, HS3ST3B1, BAZ1A, F11R, PCSK5, E2F1, SERPINA1, SREBF2, PRRC2A,
LIMS1, GATA2, FOXA1, ARHGEF9, TP53, CREB1, ZEB1, GNB5, FOXC1, SMAD5,
PSMB9, ARL17A, IRF1, PNP, MLX, ANXA2, HMG20B, SERPINE1, FOXL1, TFDP1,
MTHFD2, COL1A1, HDGF, ZNF76, ATF1, and CREB3L1. The miRNAs are
miR-27a-3p, miR-6817-3p, miR-124-3p, miR-1-3p, miR-16-5p, miR-129-2-3p,
miR-129-5p, miR-26b-5p, miR-122-5p, miR-355-5p, miR-6778-3p, and
miR-192-5p.
Figure 5.
[160]Figure 5
[161]Open in a new tab
Visualization of the DEGs-TFs and miRNAs interactions between
glioblastoma and moyamoya using various databases: JASPER and ENCODE
for TF-gene; TarBase and miRTarBase for gene-miRNA.
Figure 6.
[162]Figure 6
[163]Open in a new tab
Representation of the DEGs-TFs and miRNAs interactions between
glioblastoma and I. stroke using various databases: JASPER and ENCODE
for TF-gene; TarBase and miRTarBase for gene-miRNA.
3.5. Analysis of the Predicted Drugs
We predicted drugs using shared proteins that resulted from our
analysis. A web tool called Network Analyst was employed, which
collected data from the DrugBank 5.0 database. We utilized 26 hub
proteins shared between GBM and I. stroke to discover the drugs as
represented in [164]Figure 7B. The protein–drug interaction
([165]Figure 7A) has ten nodes, including two genes (ANXA2 and
SERPINE1) and eight chemical compounds (Alteplase, Tenecteplase,
Urokinase, Plasmin, Troglitazone, Drotrecogin alfa, Anistreplase, and
Reteplase). Similarly, we used 14 shared hub proteins from GBM and mm
for drug prediction, as shown in [166]Figure 7A. It involves nine
nodes, including CASP1 gene and eight chemical compounds (VX-765,
IDN-6556, LAX-101, Pralnacasan, Minocycline,
3-[6-[(8-HYDROXY-QUINOLINE-2-CARBONYL)-AMINO]-2-THIOPHEN-2-YL-HEXANOYLA
MINO]-4-OXO-BUTYRI ACID,
3-[2-(2-BENZYLOXYCARBONYLAMINO-3-METHYL-BUTYRYLAMINO)-PROPIONYLAMINO]-4
-OXO-PENTANOIC ACID, and
1-METHYL-3-TRIFLUOROMETHYL-1H-THIENO[2,3-C]PYRAZOLE-5-CARBOXYLIC ACID
(2-MERCAPTO-ETHYL)-AMIDE).
Figure 7.
[167]Figure 7
[168]Open in a new tab
This figure shows the drug–protein interaction. (A) Glioblastoma and
moyamoya. (B) Glioblastoma and ischemic stroke.
3.6. Validation of Transcriptomic Potential DEGs
We validated our potential DEGs of transcriptomic analysis by using
literature-based disease-gene association datasets such as DisGeNET
[[169]72], dbGaP [[170]73], and Rare-Diseases-AutoRIF. The data were
created and validated using the previous study, including the biomarker
genes corresponding to diseases. In order to assess the shared genes’
statistical significance and validate our findings, we employed EnrichR
[[171]41], an online program. EnrichR utilized the shared genes with
the disease-associated gene database to discover the relevant data.
Even though EnrichR gives disease-gene information for a variety of
disorders, we only take into account the information pertaining to the
diseases we identified.
We further confirmed our findings by reviewing previous publications
that discovered biomarkers for the diseases. The literature associated
with each gene is included in [172]Table 6 and [173]Table 7.
Ultimately, we created a diseasome network of GBM and its associated
neurological and vascular disorders, as shown in [174]Figure 8. We
developed this association map from the gold benchmark database and
previous literature review using Cytoscape [[175]58].
Table 6.
Transcriptomic analysis identifies potential target genes in GBM and mm
that have been verified by previous research.
Gene Gliobastoma Moyamoya
CASP1 Chen et al., [[176]74]—2022 Kang et al., [[177]75]—2010
GABRA1 D’Urso et al., [[178]76]—2012 -
MLYCD Avsar [[179]77]—2021 -
CARD14 - Constantin et al., [[180]78]—2010
RNF213 Bao et al., [[181]79]—2014 Fujimura et al., [[182]80]—2014
LOXL2 Zhang et al., [[183]81]—2020 -
HCAR1 Longhitano et al., [[184]82]—2021 -
FPR2 Yang et al., [[185]83]—2020 -
[186]Open in a new tab
Table 7.
Transcriptomic analysis identifies potential target genes in GBM and I.
stroke that have been verified by previous research.
Gene Gliobastoma I. Stroke
SPARC Golembieski et al., 1999 [[187]84] Baumann et al., 2009 [[188]85]
C1R Ma et al., 2021 [[189]86] Mitaki et al., 2021 [[190]87]
PPBP Lei et al., 2021 [[191]88] Katnik et al., 2016 [[192]89]
PECAM1 Warrier et al., 2021 [[193]90] Beom et al., 2015 [[194]91]
TIMP1 Aaberg-Jessen et al., 2009 [[195]92] Worthmann et al., 2010
[[196]93]
COL1A1 Sun et al., 2018 [[197]94] Choi et al., 2019 [[198]95]
FCAR Hassan et al., 2017 [[199]96] -
MT2A Sun et al., 2018 [[200]94] -
MTHFD2 Han et al., 2019 [[201]97] Kasiman 2012 [[202]98]
LCP2 Li et al., 2016 [[203]99] Li et al., 2021 [[204]100]
ALOX5AP Liu et al., 2020 [[205]101] Bie et al., 2021 [[206]102]
F11R Hattermann et al., 2014 [[207]103] -
CXCR4 Cornelison et al., 2018 [[208]104] Bang et al., 2012 [[209]105]
ANXA2 Tu et al., 2019 [[210]106] Li et al., 2021 [[211]107]
IL2RG Ogawa et al., 2018 [[212]108] -
PSMB9 - Chen et al., 2021 [[213]109]
PLEK Hoelzinger et al., 2005 [[214]110] Zeng et al., 2015 [[215]111]
SERPINE1 Seker et al., 2019 [[216]112] Bruno et al., 2021 [[217]113]
BIRC5 Kim et al., 2016 [[218]114] Chon et al., 2016 [[219]115]
HLA-DQA1 Urup et al., 2016 [[220]116] Zou et al., 2002 [[221]117]
BCL2A1 - Lin et al., 2021 [[222]118]
NCF2 Wang et al., 2020 [[223]119] Zhou et al., 2021 [[224]120]
GNB5 Xie et al., 2018 [[225]121] Jung et al., 2018 [[226]122]
GABRA1 D’Urso et al., 2012 [[227]76] Feng et al., 2021 [[228]123]
PLA2R1 Maruyama et al., 2021 [[229]124] Berchtold et al.,
2021 [[230]125]
HLA-DRA Basta et al., 1998 [[231]126] Liu et al., 2021 [[232]127]
[233]Open in a new tab
Figure 8.
[234]Figure 8
[235]Open in a new tab
Diseasome network for our study, where rectangle nodes define the
diseases and ellipses nodes define the genes associated with
corresponding disease. (A) Diseasome network between GBM and MM. (B)
Diseasome network between GBM and I. stroke.
4. Discussion
According to current research, it is clear that glioblastoma is the
most aggressive type of brain cancer. It is also known that
glioblastoma is responsible for an increased risk of developing
ischemic stroke [[236]9]. Similarly, moyamoya disease develops in brain
tumor patients due to cranial irradiation during radiation therapy
[[237]128]. Thus, it is possible that ischemic stroke and moyamoya may
be formed in glioblastoma patients.
Hence, our study aims to identify genetic relationships between
glioblastoma and ischemic stroke as well as glioblastoma and moyamoya.
Thus, doctors should be concerned about ischemic stroke and moyamoya in
glioblastoma patients. The bioinformatics approach may comprehensively
understand the molecular mechanisms in the specified disease
progression. In this study, we carried out an investigation on
transcriptomic profiles of ischemic stroke, moyamoya, and glioblastoma
(as shown [238]Figure 1). Moreover, we predicted the therapeutic drugs
for the associations.
To determine if any significant dysregulation existed, we performed
differential expression analysis (DEA) followed by identifying shared
genes for glioblastoma, moyamoya, and glioblastoma, ischemic stroke (as
shown in [239]Figure 9 and [240]Figure 10, respectively). We also
demonstrated diseasome network (in [241]Figure 8), pathways
(represented in [242]Table 2 and [243]Table 3), Gene Ontology (GO) (as
shown in [244]Table 4 and [245]Table 5), protein–protein interactions
(in [246]Figure 2), hub–protein interactions (in [247]Figure 3 and
[248]Figure 4, respectively), drug–protein interactions (shown in
[249]Figure 7), and transcription factor gene interactions and gene
miRNA interactions (represented in [250]Figure 5 and [251]Figure 6
separately). In addition, a transcriptomic dataset (RNA-Seq) was
collected from ischemic stroke, moyamoya, and glioblastoma patients and
healthy individuals (as shown in [252]Table 1). We also verified our
candidate genes by previous literature published in various journals
(as shown in [253]Table 6 and [254]Table 7, respectively). The flow
diagram of our methodology is visually represented and outlined with
proper direction in [255]Figure 1.
Figure 9.
[256]Figure 9
[257]Open in a new tab
Representation of the significant genes found to be common for
glioblastoma and moyamoya by transcriptomic-based investigation. (A)
Venn diagram shows the significant common biomarker genes. (B) Log-fold
changes and p-value combined to generate a bubble plot for the common
significant genes. (C) Heatmap that demonstrates the LogFC. (D) Heatmap
that demonstrates the p-value.
Figure 10.
[258]Figure 10
[259]Open in a new tab
Representation of the significant genes found to be common for
glioblastoma and I. stroke by transcriptomic-based investigation. (A)
Venn diagram shows the significant common biomarker genes. (B) Log-fold
changes and p-value combined to generate a bubble plot for the common
significant genes. (C) Heatmap that demonstrates the LogFC. (D) Heatmap
that demonstrates the p-value.
At first, we focused on eight hub genes, named CXCR4, ANXA2, SPARC,
SERPINA1, NCF2, COL1A1, LCP2, and IL2RG, that are highly expressed in
glioblastoma and ischemic stroke (as shown in [260]Figure 4).
Astrocytes, neurons, bone marrow-derived cells, neural progenitor
cells, and microglia1 all have CXCR4, and CXCR4 expression is regulated
in a variety of clinical situations, including brain I. stroke
[[261]129]. There are many other elements of brain tumor biology where
CXCR4 is responsible for developing glioblastoma, including
cancer-related cells’ ability to resist radiotherapy and chemotherapy,
and there are migration and production of the blood supply to the tumor
[[262]130]. As a potential candidate for invasion-boosting, and
enriched in its initial stage of developing a brain tumor, SPARC has
been found and described [[263]84]. SERPINA1 was shown to be expressed
in glioma tissue samples [[264]131]. The latest study demonstrated
six-fold enrichment of SERPINA1 in human atherosclerotic in contrast to
healthy ones to verify the involvement of SERPINA1 in atherosclerosis
[[265]132]. SERPINE1 has been discovered as a regulator of GBM cell
dispersion. Prevention of GBM tumor growth and invasiveness in the
brain was achieved by knocking down the SERPINE1 [[266]112]. Both the
CGA cancer database and clinical evidence reveal that relatively high
enrichment of NCF2 genes is associated with a bad outcome in
glioblastoma patients [[267]119]. Overexpression or knockdown of COL1A1
was used to examine the effect on glioma cell proliferation of COL1A1
[[268]133]. LCP2 and IL2RG are not reported. In future research, these
genes can be further studied to prevent ischemic stroke in GBM
patients. Similarly, a hub gene named CASP1 was found between GBM and
moyamoya, as shown in [269]Figure 3. CASP1 plays an important role in
upregulating the development of glioma [[270]134,[271]135]. Researchers
can work on this gene to avoid developing moyamoya disease in GBM
patients.
For pathways, there are two pathways, named antigen processing and
presentation and serotonin and anxiety, that are common in
glioblastoma, ischemic stroke, and moyamoya. In IDH-wildtype gliomas,
the antigen processing and presentation (APP) score is linked with the
immunological score [[272]136]. Antigen processing and presentation, DC
pathway, cytokine pathway, and IL-12 pathway were increased in the
intracranial arteries of patients with mm in this study [[273]137]. The
serotonin and anxiety pathway is known as the monoaminergic system
[[274]138]. In addition, ischemic brain injury alters this route, and
the monoaminergic system may be a potential therapeutic target for
stroke [[275]139]. Therefore, these four pathways can be a therapeutic
target in order to prevent ischemic stroke and moyamoya associated with
glioblastoma patients. In addition, a pathway named leukocyte
transendothelial migration is activated, which is validated by a
previous study between glioblastoma and ischemic stroke. It is possible
that an aberrant immunological condition and the development of GBM are
associated, and the leukocyte transendothelial migratory pathway might
be an indicator of that [[276]140].
According to the information presented above, our technique has the
potential to disclose some of the essential mechanisms that underlie
disease, as well as generate unique theories about disease mechanisms
and identify new biomarkers for disease. Genetic data analysis is
expected to be crucial for improving predictive medicine and uncovering
pathways connecting with glioblastoma, ischemic stroke, and moyamoya,
as well as identifying potential therapeutic targets.
We made an effort to use prior research to validate each of our
findings. However, there is still a need for more in vivo and in vitro
research. Due to their complexity, the doctor must be concerned about
ischemic stroke and moyamoya in glioblastoma patients. Moreover, the
prevention of ischemic stroke and moyamoya can be made possible by
inactivating mentioned pathways using the predicted drug.
A few limitations open the way for further research, such as the
availability of brain-related data from living organisms. Moreover,
more specific clinical- and gene-level research is required to better
understand the complications by analyzing the candidate biomarkers
found in this work.
5. Conclusions
The current study used a statistical technique on the transcriptomic
data to uncover the shared significant genes that are highly enriched
among glioblastoma, ischemic stroke, and moyamoya patients. The study
of the significant gene sets revealed the associated dysregulated
pathways that were also highly enriched. Protein–protein interactions,
regulatory TFs from the survey of TF–gene interactions, and miRNAs from
gene–miRNA interactions were obtained by comparing the overlapped DEGs
with distinct biomolecular interaction networks and databases. Most of
the transcription factors and microRNAs discovered in this study are
novel; no prior studies have implicated these genes or pathways in
developing these disorders or their connections. More studies still
need to be performed to validate these molecular signature biomarkers.
This study looked at candidate genes at protein and RNA levels, such as
TFs, mRNAs and miRNAs, the pathway, and the GO terminologies. Finally,
we predicted the potential drugs for the associations. Moreover, the
results were validated using gold benchmark databases and published
literature. These results show that genes in glioblastoma are more or
less active in people with ischemic stroke and moyamoya, which could
help explain these diseases. It also demonstrates how to find
functional relationships between ischemic stroke and moyamoya,
explaining why they are linked to glioblastoma.
Author Contributions
The author contributions are as follows: Conceptualization, M.K.I.,
M.R.I. and M.A.A.; methodology, M.K.I.; software, M.H.R. and M.K.I.;
validation, M.K.I., M.Z.I. and M.R.I.; formal analysis, M.K.I. and
M.R.I.; investigation, M.K.I., K.R.A. and M.Z.I.; resources, M.K.I. and
M.A.A.; data curation, M.K.I., M.R.I. and M.A.A.; writing—original
draft preparation, M.R.I. and M.K.I.; writing—review and editing,
M.Z.I., M.H.R., K.R.A., M.A.A. and M.A.M.; visualization, M.R.I. and
M.A.A.; supervision, M.A.M. and M.H.R.; project administration, M.A.M.,
M.A.R. and B.K.; funding acquisition, M.A.R. and B.K. All authors have
read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Links of our dataset are included in this manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was supported by Korea Institute of Oriental Medicine
(grant number KSN2021240), Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education (NRF-2020R1I1A2066868), the National Research Foundation of
Korea (NRF) grant funded by the Korea government (MSIT) (No.
2020R1A5A2019413), a grant of the Korea Health Technology R&D Project
through the Korea Health Industry Development Institute (KHIDI), funded
by the Ministry of Health & Welfare, Republic of Korea (grant number:
HF20C0116), and a grant of the Korea Health Technology R&D Project
through the Korea Health Industry Development Institute (KHIDI), funded
by the Ministry of Health & Welfare, Republic of Korea (grant number:
HF20C0038).
Footnotes
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References