Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202202

Research Article

Open Access

1 International Center for Agricultural Research

in the Dry Areas (ICARDA), Cairo, Egypt.
2 Genetics Department, Faculty of Agriculture,

Mansoura University, Mansoura, Egypt.

* To whom correspondence should be
addressed: khaledhelmy444@gmail.com

Editor: Hatem Zayed, College of Health and
Sciences, Qatar University, Doha, Qatar.

Reviewer(s):
Santosh K Maurya, Molecular Signaling &
Drug Discovery Laboratory, Department of
Biochemistry, Central University of Punjab,
Bathinda, Punjab, India.

Akhilesh Maurya, Indian Institute of Information
Technology Allahabad, Devghat, Jhalwa,
Prayagraj, Uttar Pradesh 211015, India.

Received: May 1, 2022

Accepted: July 1, 2022

Published: July 15, 2022

Citation: Mousa KH, Nassar AE . Identification
of hub genes and potential molecular mechanisms
associated with inflammatory bowel diseases
using meta-analysis of gene expression
data. 2022 July 15;5:bs202202

Copyright: © 2022 Mousa and Nassar. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

Data Availability Statement: All relevant data are
within the paper and supplementary materials.

Funding: The authors have no support or
funding to report.

Competing interests: The authors declare
that they have no competing interests.

Identification of hub genes and potential molecular mechanisms asso-
ciated with inflammatory bowel diseases using meta-analysis of gene
expression data

Khaled H. Mousa*1,2
>< `, Ahmed E. Nassar1,2

>< `

Abstract
Inflammatory bowel diseases (IBDs), which primarily include Crohn’s disease (CD)

and ulcerative colitis (UC), are chronic recurrent diseases of the gastrointestinal tract with
increasing prevalence and incidence worldwide. In this study, we aimed to identify key
factor genes that control the progression of inflammatory bowel disease, identify common
and unique nodal genes, examine gene-protein interactions, assess current advances in the
published literature on inflammatory bowel disease, and examine the impact of various
biological pathways. Gene expression profiles were obtained from the Gene Expression
Omnibus (GEO) database. We performed gene expression analysis to identify deferentially
expressed genes. Subsequently, GO and KEGG pathway enrichment analyzes and protein-
protein interaction network analyzes (PPI) of DEGs were performed. Text mining was
used to examine the frequency of genes in the published IBD literature. Four GEO
databases (GSE156044, GSE159751, GSE159008, and GSE102746) were downloaded
from GEO databases. A total of 368 DEGs were identified. The results of GO term analysis
showed that DEGs were mainly involved in the activity of cytokine receptors, integral
components of the plasma membrane, and cytokine-mediated signaling. KEGG pathway
analysis showed that DEGs were mainly enriched in bile secretion, mineral absorption, and
cytokine-cytokine receptor interaction. The results of PPI analysis showed that about 10
genes were the key genes for the occurrence of CED. Text mining revealed the existence of
399 genes associated with CED. Our results suggest a possible link between CED and other
diseases such as triple negative breast cancer (TNBC) and lung adenocarcinoma (LUAD),
and provide new insights into the mechanisms of inflammatory bowel disease and new
treatment targets.

Keywords: Inflammatory bowel diseases, Deferentially expressed genes, Hub genes, Text mining

Introduction
Inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis

(UC), is a chronic inflammatory disease of the gastrointestinal tract that is becoming increasingly

common worldwide [1]. Inflammatory bowel diseases (IBDs) are the most common gastrointestinal

(GI) diseases, affecting 0.3% to 0.5% of the world population [2]. Ulcerative colitis (CD) is confined

to the mucosa of the colon, always involving the rectum, and may continuously extend to more

proximal parts of the intestine [3]. Crohn’s disease (CD), on the other hand, is a transmural,

progressive inflammatory disease that can affect any part of the gastro-intestinal tract (GI). As a result,

complications such as strictures, fistulas, and abscesses are more common in CD [4]. Patients with

these conditions have symptoms such as bloating, abdominal pain, and altered bowel habits (diarrhea

and/or constipation), and approximately 40% of IBD patients have an irritable bowel

Irritable Bowel Syndrome (IBS)-like symptoms [5]. IBD affects both children and adults, with

15-20% of patients diagnosed in childhood [6]. Inflammatory bowel disease is caused by a variety of

agents, including genetic, immune system, microbial, and environmental factors [7].

Highlights in BioScience Page 1 of 8 July 2022|Volume 5

https://doi.org/10.36462/H.BioSci.202202
https://creativecommons.org/licenses/by/4.0/
khaledhelmy444@gmail.com
hindu8446@gmail.com
http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

Malnutrition is a common problem inIBD patients [8]. Malnu-
trition inIBD patients is multi-factorial and has been associated
with malabsorption, caloric restriction, pharmacological treat-
ment, loss of nutrients in the gastrointestinal tract, and increased
energy consumption [9]. Inflammatory bowel disease (IBD) is a
lifelong condition that has no cure. Patients suffering from IBD
may present with symptoms of common mental disorders such as
anxiety and depression [10]. The prevalence of CD and UC can
be attributed to a variety of factors, including geographic loca-
tion, poor nutrition, genetics, and an ineffective immune response
[11]. Studies of familial clustering and twin pairs have provided
evidence that genetic factors contribute to the development of
IBD. Studies of discordant twin pairs with IBD have pointed to
some genetically independent factors underlying pathogenesis
[12]. Genetic research on inflammatory bowel disease (IBD)
has identified genes and pathways in inflammatory pathology.
[13]. Most available IBD therapies suppress the immune system,
which increases the risk of infections and some cancers and does
not benefit all patients [14]. To reduce the prevalence of IBD, the
IBD community should focus on prevention of the disease [15].
Differential gene expression (DGE) analysis is one of the most
common applications of RNA sequencing (RNA-seq) data. This
method can be used to elucidate differentially expressed genes
under two or more conditions, and it is used in many applications
for the analysis of RNA-seq data. [16]. Using differential gene
expression, nodal genes have been identified in Alzheimer’s dis-
ease (AD) [17], gastric cancer (GC) [18], breast cancer (BC) [19],
tuberculosis (TB) [20], lung adenocarcinoma [21], colon cancer
(CRC) [22]. In this study, we used bioinformatics approaches to
identify differentially expressed genes (DEGs) between normal
and inflamed tissues. These DEGs were also analyzed, including
GO and KEGG enrichment analysis of DEGs, construction of
protein-protein interaction networks (PPI), identification of key
genes associated with CED, and identification of genes common
to Crohn’s disease (CD) and ulcerative colitis (UC). The biolog-
ical functions and key signaling pathways of these DEGs are
discussed. The major genes discovered in previous work were
reviewed.

Materials and Methods
Gene expression data

We downloaded the original gene expression profiles GSE156-
044, GSE159751, GSE159008 and GSE102746 from the Na-
tional Center for Biotechnology Information (NCBI) Gene Ex-
pression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/)
[23]. A total of 40 normal tissues (control) and 31 inflamed
tissues (patients) were downloaded. We excluded 27 inflamed
tissues in GSE159008. We selected samples of normal tissues
and inflamed tissues of different types of inflammatory bowel
disease only.

Identification of DEGs
Raw data were subjected to differentially gene expression

analysis using the R package limma. Gene expression was an-

alyzed with the package limma in R and displayed as a heat
map. Genes with P.Val 0.05 were considered as deferentially
expressed.

Gene enrichment analysis
Gene set enrichment (GSE) is the most effective method

for determining the underlying biological functions of various
genes or proteins [24]. To determine the functions of overlapping
DEGs, GO functional and KEGG pathway enrichment of proteins
encoded by candidate genes were analyzed and these genes were
annotated using the Enrichr online analysis database (available
online: https://maayanlab.cloud/Enrichr/). Enrichr is a gene set
search engine that can be used to query hundreds of thousands of
annotated gene sets. This platform provides several methods for
calculating gene set enrichment, and the results are visualized in
several interactive ways. [25].

PPI network construction and analysis
Protein-protein interactions was assessed using the database

STRING (Search Tool for the Retrieval of Interacting Genes;
http://string-db.org/cgi/input.pl). Using the STRING database
and the Cytoscape programme, a PPI network of DEGs was pro-
duced (version 3.9.0). Additionally, the Cytoscape program is
built on a network. Each node represents a gene, protein, or other
biological molecule, and the connections between nodes repre-
sent how these biological molecules interact. This model can be
used to determine how proteins encoded by DEGs interact with
one another and with other proteins in pathways in inflammatory
bowel diseases (IBDs).

Text-mining
The importance of text mining in service management is

growing with the expansion of access to Big Data via digital
platforms that enable such services [26]. Text mining tools
are commonly used to extract information about disease-related
genes, proteins, molecular interactions, and signaling pathways
[27]. Previous research has documented the use of these tools
in studying regulatory mechanisms for various types of diseases,
including inflammatory bowel disease [28]. In the current study,
we obtained IBD-related abstracts from the PubMed database
by searching for ’inflammatory bowel disease’ using the esearch
tool and downloading these data using the efetch tool, which is
responsible for retrieving records in the desired format [29]. This
tool is provided by Entrez Direct (EDirect) software [30]. We
counted genes associated with IBDs from the published literature
using Python, and these genes were illustrated using the R pack-
age. These genes had been studied extensively in the context of
IBD.

Results
Identification of DEGs

A total of 93, 361, 907, and 1123 DEGs were identified
from GSE156044, GSE159008, GSE102746, and GSE159751,
respectively, according to differential gene expression analysis.

Highlights in BioScience Page 2 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

The heat map of DEGs expression is shown in Figure 1. Among
the 2484 genes with adj.P.Val ≤ 0.04, 368 DEGs were identified,
including 180 upregulated genes and 188 downregulated genes.

Biological annotation of DEGs in inflammatory bowel dis-
ease was performed using the Enrichr online database. Bars
of the graph sorted by p-value ranking. Figure 3 shows the
ten most enriched pathways from GO and KEGG analyzes for
enrichment of DEGs. GO Analysis of DEGs was divided into
three functional groups, including molecular function (MF), cel-
lular component (CC), and biological process (BP). At MF, these
DEGs were significantly enriched in cytokine receptor activity,
metal ion binding, CXCR chemokine receptor binding, icosate-
traenoic acid binding, and arachidonic acid binding (Figure 3c).
At CC, DEGs were enriched in the integral component of the
plasma membrane, the lumen of secretory granules, the lumen of
intercellular organelles, the RNA polymerase III complex, and
the alpha-beta T-cell receptor (Figure 3b). At BP, DEGs were
enriched in cytokine-mediated signaling, positive regulation of
telomerase RNA localization in the Cajal body, neutrophil degran-
ulation, neutrophil activation in immune response, and regulation
of telomerase RNA localization in the Cajal body (Figure 3a).
In KEGG signaling pathways, DEGs were enriched mainly in
bile secretion, mineral absorption, cytokine-cytokine receptor
interaction, alanine, aspartate, and glutamate metabolism, and
JAK-STAT signaling (Figure 3d). These significantly enriched
GO terms and KEGG signaling pathways will help us better
understand the key molecules involved in the progression of IBD.

PPI network construction and analysis
Genes associated with inflammatory bowel disease were sub-

jected to PPI analysis. The PPI network grouped genes based
on their interaction activity to discover genes associated with the
studied diseases, as shown in Figure 4. As a result, we obtained
42 genes with significant interactions, 10 of which were asso-
ciated with IBD. These genes were POLR2H, LCN2, TIMP1,
CXCL1, MUC1, CSF3R, S100A9, SPI1, CFTR, and FCGR3B.

Text mining
We examined 42192 abstracts and obtained the 399 most

frequent genes in the published literature associated with CED
(Figure 5). Using the online analysis tool ShinyGO. Gene Ontol-
ogy Enrichment analysis was performed according to biological
processes (BP), cellular components (CC), and molecular func-
tions (MF). In addition, the cutoff value for screening pathways
and significant functionality was set at P 0.05. Table 1 shows
the ten most enriched pathways of the Go enrichment analysis.
KEGG pathway enrichment analysis was also performed using
g:Profiler. A Venn diagram was used to classify the similarities
and differences between the DEGs of the four datasets and the
IBD-related genes identified by text mining (two genes).

Figure 1. Heat maps of gene expression data. The gene expression profiles of
the studied IBDs, (a) GSE99816(Crohn’s disease), (b) GSE156044(Crohn’s
disease), (c) GSE159008(ulcerative colitis), (d) GSE102746 (ulcerative colitis)
you can add references of every experiment.

Figure 2. Venn digram, Similarities and differences between four datasets and
399 most frequent genes in previous publications related to IBD, identified by
text mining.

Highlights in BioScience Page 3 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

Figure 3. GO molecular function and KEGG pathways analysis of DEGs.
Red bars represent the number of DEGs. Here only show the top 10 pathways:
(a) Biological processes (BP); (b) Cellular components (CC); (c) Molecular
function (MF); (d) KEGG pathway.

Discussion
Inflammatory bowel disease (IBD) is a chronic inflammatory

disease of the gastrointestinal tract [31]. Ulcerative colitis and
Crohn’s disease are the main forms of inflammatory bowel dis-
ease [32]. Inflammatory bowel disease is a long-term chronic
condition, with approximately 20% of IBD patients progress-
ing to colorectal cancer [33]. Intestinal fibrosis is a common
complication of inflammatory bowel disease, which is usually
the result of chronic inflammation [34]. Genetic studies have
identified more than 200 loci that regulate IBD risk [35]. In
patients with IBD, alterations in various immune cells and neu-
roimmune signaling pathways have been identified in the lamina
propria [36]. Therefore, it is important to investigate the molecu-
lar mechanisms of inflammation and inflammatory bowel disease
development. Bioinformatics tools provide extensive technical
support for the processing and analysis of gene expression pro-
files. As a result, bioinformatics analysis has been widely used in
recent years to identify novel therapeutic targets and diagnostic
markers [37]. In this study, four GEO datasets related to IBDs,
GSE156044, GSE159008, GSE102746 and GSE159751, were
examined using the Limma package in R statistical software and
368 DEGs were identified. The results of GO analysis, which
included MF, CC, and BP, showed that these DEGs were mainly
enriched in cytokine receptor activity, extracellular response, and
cellular response to chemical stimuli. KEGG pathway enrich-
ment analysis also showed that these DEGs were significantly
enriched in bile secretion, cytokine-cytokine receptor interac-
tion, mineral absorption, retinol metabolism, and JAK-STAT

pathways. This gene set enrichment analysis provides insights
into the molecular mechanism of IBD progression. We used Cy-
toscape to create a PPI network according to DEGs and identified
42 genes with significant interaction, 10 of which are associated
with IBD. They are POLR2H, LCN2, TIMP1, CXCL1, MUC1,
CSF3R, S100A9, SPI1, CFTR, and FCGR3B. POLR2H is over-
expressed in organoids of rectal tumors. POLR2H is the gene
encoding the common H subunit of RNA polymerases I, II and
III [38]. LCN2 is secreted into the intestinal lumen at high levels
and has a significant impact on controlling both the composition
of the gut microbiome and host inflammation [39]. Short-term
consumption of a high-fat diet (HFD) significantly increases in-
testinal Lcn2 expression and secretion into the intestinal lumen.
Lcn2 deficiency accelerates the development of HFD-induced
intestinal inflammation [40]. TIMP1 plays an important role in
promoting tumorigenesis [41] and acts as a prognostic biomarker
for ulcerative colitis-related colorectal cancer [42]. CXCL1 en-
hances myeloid cell-mediated immunosuppression during acute
colitis and has been linked to tumorigenesis in various tumor
types [43]. MUC1 is a heterodimeric protein that enhances the re-
sponse to inflammation. Expression of MUC1 is associated with
activation of inflammatory pathways and development of colitis
[44]. CSF3R is an important regulator of proliferation and dif-
ferentiation of myeloid cells. Mutations in the CSF3R gene have
been found in patients with chronic neutrophilic leukemia (CNL)
and acute myeloid leukemia (AML) [45]. Recent research has
shown that mutations in the CSF3R gene play an oncogenic role
in the development of hematologic malignancies [46]. S100A9
is a myeloid-related protein involved in inflammatory processes
and various carcinogens [47]. SPI1 is a gene cluster containing
39 genes encoding T3SS-1. SPI1 genes are responsible for host
cell invasion and host inflammatory response. T3SS-1 encoded
by SPI1 provides effector proteins required for intestinal invasion
and the development of enteritis [48]. CFTR is a protein located
at the apical membrane of epithelial cells and has been associ-
ated with cystic fibrosis disease. Cystic fibrosis is a potentially
fatal disease that causes severe damage to the lungs and digestive
system [49]. FcY receptors (FcYRs), are expressed by six genes,
one of which is FCGR3B [50]. FCGR3B increases the risk of in-
flammatory diseases such as systemic lupus erythematosus (SLE)
and rheumatoid arthritis (RA) [51].

Text mining is concerned with obtaining information about
biological entities such as genes, proteins, and phenotypes, or
more broadly, biological pathways [52]. Text mining was used to
extract genes associated with CED from the PubMed database.
We obtained the 399 most frequent genes in the published litera-
ture associated with CED. Comparison between the DEGs of the
four datasets and the text mining results revealed 21 overlapping
genes that had been intensively studied. By examining the PPI
network of these 21 genes, we discovered four genes that interact
highly with each other (PTPRC, LCN2, CXCL1, and S100A9 ).
These genes suggest that there may be a potential link between
them and other diseases. In our study, the results of text mining

Highlights in BioScience Page 4 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

Figure 4. PPI network of significant genes associated with IBD diseases. (a) Protein-protein interaction network of DEGs. (b) Clustering of genes according to
their interaction activity.

Figure 5. Text Mining of genes associated with IBDs in published literature. 399 most frequent genes in previous publications related to IBD, identified by text
mining.

Highlights in BioScience Page 5 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

partially agreed with the results of DEG analysis, but they also
provided some unique conclusions. PTPRC was associated with
triple negative breast cancer (TNBC) [53]. PTPRC may be a
potential prognostic marker in lung adenocarcinoma (LUAD),
which could affect the immune function of T cells and other
immune cells by participating in the regulation of tumor microen-
vironment (TME) immune activity [54]. LCN2 is an acute-phase
response protein that is upregulated in CNS disease or injury and
serves as a regulatory enhancer in neuroinflammation [55]. Dur-
ing lymphangiogenesis, CXCL1 enables tumor cells to migrate
into lymphatic vessels, leading to lymph node metastasis [56].
A growing body of research suggests that S100A9, also known
as myeloid-related proteins 8 and 14, plays an important role
in inflammation and inflammation-related tissue damage [57].
S100A9 has been reported to be regulated by pathogen infection
and to play a role in the progression of various cancers, includ-
ing hepatitis B virus (HBV)-related hepatocellular carcinoma
(HCC) [58]. In summary, the aim of this study was to describe
IBD-related gene expression and systematically investigate the
pathways and networks of IBD-related genes using bioinformat-
ics to reveal beneficial targets and pathways in the pathogenesis
of IBD.

References
1. John Gubatan, Steven Levitte, Akshar Patel, Tatiana Balaba-

nis, Mike T Wei, and Sidhartha R Sinha. Artificial intelligence
applications in inflammatory bowel disease: emerging technolo-
gies and future directions. World journal of gastroenterology,
27(17):1920, 2021.

2. Arnau Vich Vila, Floris Imhann, Valerie Collij, Soesma A Jankiper-
sadsing, Thomas Gurry, Zlatan Mujagic, Alexander Kurilshikov,
Marc Jan Bonder, Xiaofang Jiang, Ettje F Tigchelaar, et al. Gut
microbiota composition and functional changes in inflammatory
bowel disease and irritable bowel syndrome. Science translational
medicine, 10(472):eaap8914, 2018.

3. Gabrielle Wark, Dorit Samocha-Bonet, Simon Ghaly, and Mark
Danta. The role of diet in the pathogenesis and management
of inflammatory bowel disease: a review. Nutrients, 13(1):135,
2021.

4. Amritpal Dhaliwal, Jonathan I Quinlan, Kellie Overthrow, Car-
olyn Greig, Janet M Lord, Matthew J Armstrong, and Sheldon
C Cooper. Sarcopenia in inflammatory bowel disease: A narra-
tiveoverview. Nutrients, 13(2):656, 2021.

5. Natalia Pedersen, Dorit Vedel Ankersen, Maria Felding, Henrik
Wachmann, Zsuzsanna V´egh, Line Molzen, Johan Burisch, Jens
Rikardt Andersen, and Pia Munkholm. Low-fodmap diet reduces
irritable bowel symptoms in patients with inflammatory bowel
disease. World journal of gastroenterology, 23(18):3356, 2017.

6. Alastair Forbes, Johanna Escher, Xavier H´ebuterne, Stanis-law
Klek, Zeljko Krznaric, St´ephane Schneider, Raanan Shamir,
Kalina Stardelova, Nicolette Wierdsma, Anthony E Wiskin, et
al. Espen guideline: Clinical nutrition in inflammatory bowel
disease. Clinical Nutrition, 36(2):321– 347, 2017.

7. Jordan E Axelrad, Ken H Cadwell, Jean-Frederic Colombel, and
Shailja C Shah. Systematic review: gastrointestinal infection

and incident inflammatory bowel disease. Alimentary pharma-
cology & therapeutics, 51(12):1222–1232, 2020.

8. Jing Liu, Xiaolong Ge, Chunhui Ouyang, Dongxu Wang, Xiaoqi
Zhang, Jie Liang, Weiming Zhu, Qian Cao, et al. Prevalence of
malnutrition, its risk factors, and the use of nutrition support in
patients with inflammatory bowel disease. Inflammatory bowel
diseases, 2022.

9. Alicja Ewa Ratajczak, Anna Maria Rychter, Agnieszka Zawada,
Agnieszka Dobrowolska, and Iwona Krela-Ka´zmierczak. Do
only calcium and vitamin d matter? micronutrients in the diet of
inflammatory bowel diseases patients and the risk of osteoporosis.
Nutrients, 13(2):525, 2021.

10. Brigida Barberio, Mohammad Zamani, Christopher J Black,
Edoardo V Savarino, and Alexan- der C Ford. Prevalence of
symptoms of anxiety and depression in patients with inflamma-
tory bowel disease: a systematic review and meta-analysis. The
Lancet Gastroenterology Hepatology, 2021.

11. Seyed Saeid Seyedian, Forogh Nokhostin, and Mehrdad Dargahi
Malamir. A review of the di- agnosis, prevention, and treatment
methods of inflammatory bowel disease. Journal of medicine and
life, 12(2):113, 2019.

12. Juan Du, Jie Yin, Haojie Du, and Jiawei Zhang. Revisiting an
expression dataset of discordant inflammatory bowel disease twin
pairs using a mutation burden test reveals cyp2c18 as a novel
marker. Frontiers in Genetics, 12:1020, 2021.

13. Daniel B Graham and Ramnik J Xavier. Pathway paradigms re-
vealed from the genetics of in- flammatory bowel disease. Nature,
578(7796):527–539, 2020.

14. Benjamin M Scott, Cristina Gutierrez-Vazquez, Liliana M San-
marco, Jessica A da Silva Pereira, Zhaorong Li, Agustin Plasen-
cia, Patrick Hewson, Laura M Cox, Madelynn O’Brien, Steven K
Chen, et al. Self-tunable engineered yeast probiotics for the
treatment of inflammatory bowel disease. Nature Medicine,
27(7):1212–1222, 2021.

15. Gilaad G Kaplan and Joseph W Windsor. The four epidemiologi-
cal stages in the global evolution of inflammatory bowel disease.
Nature Reviews Gastroenterology Hepatology, 18(1):56–66,
2021.

16. Adam McDermaid, Brandon Monier, Jing Zhao, Bingqiang Liu,
and Qin Ma. Interpretation of differential gene expression results
of rna-seq data: review and integration. Briefings in bioinfor-
matics, 20(6):2044–2054, 2019.

17. Min Zhu, Longfei Jia, Fangyu Li, and Jianping Jia. Identifica-
tion of kiaa0513 and other hub genes associated with alzheimer
disease using weighted gene coexpression network analysis. Fron-
tiers in genetics, 11:981, 2020.

18. Yun Ji, Lu Gao, Can Zhang, Xu Sun, Liping Dai, Zhenyu Ji,
Jianying Zhang, Zhida Zhang, Wei Cao, Yang Zhao, et al. Iden-
tification of the hub genes and prognostic indicators of gastric
cancer and correlation of indicators with tumor-infiltrating im-
mune cell levels. Journal of Cancer, 12(13):4025, 2021.

19. Ming Wu, Yujie Zhao, Nanxi Peng, Zuo Tao, and Bo Chen. Iden-
tification of chemoresistance- associated micrornas and hub genes
in breast cancer using bioinformatics analysis. Investigational
New Drugs, 39(3):705–712, 2021.

Highlights in BioScience Page 6 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

20. Tiancheng Zhang, Guihua Rao, and Xiwen Gao. Identification of
hub genes in tuberculosis via bioinformatics analysis. Computa-
tional and Mathematical Methods in Medicine, 2021, 2021.

21. Shuaiqun Wang, Xiaoling Xu, and Wei Kong. Identification of
hub genes associated with lung adenocarcinoma based on bioin-
formatics analysis. Computational and Mathematical Methods in
Medicine, 2021, 2021.

22. Xuan Yang, Wangao Wei, Shisheng Tan, Linrui Guo, Song Qiao,
Biao Yao, and Zi Wang. Identifi- cation and verification of hcar3
and insl5 as new potential therapeutic targets of colorectal cancer.
World journal of surgical oncology, 19(1):1–12, 2021.

23. Emily Clough and Tanya Barrett. The gene expression omnibus
database. In Statistical genomics, pages 93–110. Springer, 2016.

24. Dechao Bu, Haitao Luo, Peipei Huo, Zhihao Wang, Shan Zhang,
Zihao He, Yang Wu, Lianhe Zhao, Jingjia Liu, Jincheng Guo, et al.
Kobas-i: intelligent prioritization and exploratory visualization
of biological functions for gene enrichment analysis. Nucleic
acids research, 49(W1):W317–W325, 2021.

25. Zhuorui Xie, Allison Bailey, Maxim V Kuleshov, Daniel JB
Clarke, John E Evangelista, Sherry L Jenkins, Alexander Lach-
mann, Megan L Wojciechowicz, Eryk Kropiwnicki, Kathleen
M Jagodnik, et al. Gene set knowledge discovery with enrichr.
Current protocols, 1(3):e90, 2021.

26. Sunil Kumar, Arpan Kumar Kar, and P Vigneswara Ilavarasan.
Applications of text mining in services management: A sys-
tematic literature review. International Journal of Information
Management Data Insights, 1(1):100008, 2021.

27. Wei Ma, Bei Shi, Fangkun Zhao, Yunfei Wu, and Feng Jin. Sys-
tematic analysis of breast atypical hyperplasia-associated hub
genes and pathways based on text mining. European Journal of
Cancer Prevention, 28(6):507, 2019.

28. Farhat Khan, Aleksandar Radovanovic, Takashi Gojobori, and
Mandeep Kaur. Ibddb: a manually curated and text-mining-
enhanced database of genes involved in inflammatory bowel
disease. Database, 2021, 2021.

29. Jonathan Kans. Entrez direct: E-utilities on the unix command
line. In Entrez Programming Utilities Help [Internet]. National
Center for Biotechnology Information (US), 2022.

30. Vincent F Scalfani. Using ncbi entrez direct (edirect) for small
molecule chemical information searching in a unix terminal. Jour-
nal of Chemical Education, 98(12):3904–3914, 2021.

31. Qingdong Guan. A comprehensive review and update on the
pathogenesis of inflammatory bowel disease. Journal of Im-
munology Research, 2019, 2019.

32. Christopher Andrew Lamb, Nicholas A Kennedy, Tim Raine,
Philip Anthony Hendy, Philip J Smith, Jimmy K Limdi, Bu’Hussain
Hayee, Miranda CE Lomer, Gareth C Parkes, Christian Selinger,
et al. British society of gastroenterology consensus guidelines on
the management of inflammatory bowel disease in adults. Gut,
68(Suppl 3):s1–s106, 2019.

33. Wenna Yang, Ping Zhao, Xia Li, Lanping Guo, and Wenyuan
Gao. The potential roles of nat- ural plant polysaccharides in
inflammatory bowel disease: A review. Carbohydrate Polymers,
277:118821, 2022.

34. Geert D’Haens, Florian Rieder, Brian G Feagan, Peter DR Hig-
gins, Julian Pan´es, Christian Maaser, Gerhard Rogler, Mark
L¨owenberg, Robbert van der Voort, Massimo Pinzani, et al.
Chal- lenges in the pathophysiology, diagnosis, and management
of intestinal fibrosis in inflammatory bowel disease. Gastroen-
terology, 162(1):26–31, 2022.

35. Roberta Caruso, Bernard C Lo, and Gabriel Nu´n~ez. Host–microbiota
interactions in inflammatory bowel disease. Nature Reviews Im-
munology, 20(7):411–426, 2020.

36. Ida Schoultz and Asa V Keita. Cellular and molecular therapeutic
targets in inflammatory bowel disease—focusing on intestinal
barrier function. Cells, 8(2):193, 2019.

37. Jiani Huang, Fang Wen, Wenjie Huang, Yingfeng Bai, Xiaona
Lu, and Peng Shu. Identification of hub genes and discovery of
promising compounds in gastric cancer based on bioinformatics
analysis. Biomarkers in Medicine, 14(12):1069–1084, 2020.

38. Alba Costales-Carrera, Asunci´on Fern´andez-Barral, Pilar Bustamante-
Madrid, Orlando Dom´ınguez, Laura Guerra-Pastri´an, Ram´on
Cantero, Luis Del Peso, Aurora Burgos, Antonio Barb´achano,
and Alberto Mun~oz. Comparative study of organoids from
patient-derived normal and tumor colon and rectal tissue. Can-
cers, 12(8):2302, 2020.

39. Andreas Zollner, Andreas Schmiderer, Simon J Reider, Georg
Oberhuber, Alexandra Pfister, Bernhard Texler, Christina Watschinger,
Robert Koch, Maria Effenberger, Tim Raine, et al. Faecal biomark-
ers in inflammatory bowel diseases: Calprotectin versus lipocalin-
2—a comparative study. Journal of Crohn’s and Colitis, 15(1):43–54,
2021.

40. Xiaoxue Qiu, Marissa G Macchietto, Xiaotong Liu, You Lu,
Yiwei Ma, Hong Guo, Milena Saqui- Salces, David A Bernlohr,
Chi Chen, Steven Shen, et al. Identification of gut microbiota
and microbial metabolites regulated by an antimicrobial peptide
lipocalin 2 in high fat diet-induced obesity. International Journal
of Obesity, 45(1):143–154, 2021.

41. Guohe Song, Shifeng Xu, Hong Zhang, Yupeng Wang, Chao
Xiao, Tao Jiang, Leilei Wu, Tao Zhang, Xing Sun, Lin Zhong, et
al. Timp1 is a prognostic marker for the progression and metas-
tasis of colon cancer through fak-pi3k/akt and mapk pathway.
Journal of Experimental Clinical Cancer Research, 35(1):1–12,
2016.

42. Ru Huang, Kaijing Wang, Lei Gao, and Wei Gao. Timp1 is a
potential key gene associated with the pathogenesis and prognosis
of ulcerative colitis-associated colorectal cancer. OncoTargets
and therapy, 12:8895, 2019.

43. Zhen-Yu Liu, Ming Zheng, Yi-Ming Li, Xin-Yu Fan, Jian-Chao
Wang, Zhu-Chun Li, Hai-Jiao Yang, Jing-Min Yu, Jian Cui, Jian-
Li Jiang, et al. Rip3 promotes colitis-associated colorectal cancer
by controlling tumor cell proliferation and cxcl1-induced immune
suppression. Theranos- tics, 9(12):3659, 2019.

44. Wei Li, Ning Zhang, Caining Jin, Mark D Long, Hasan Rajabi,
Yota Yasumizu, Atsushi Fushimi, Nami Yamashita, Masayuki
Hagiwara, Rongbin Zheng, et al. Muc1-c drives stemness in
progres- sion of colitis to colorectal cancer. JCI insight, 5(12),
2020.

45. Amanda Lance, Lawrence J Druhan, C Greer Vestal, Nury M
Steuerwald, Alicia Hamilton, Mathew Smith, Andrea Price, Elise

Highlights in BioScience Page 7 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/


Mousa and Nassar, 2022 Hub genes and potential molecular mechanisms associated with inflammatory bowel diseases

Tjaden, Andee N Fox, and Belinda R Avalos. Altered ex- pression
of csf3r splice variants impacts signal response and is associated
with srsf2 mutations. Leukemia, 34(2):369–379, 2020.

46. Haijiao Zhang, Cody Coblentz, Kevin Watanabe-Smith, Sophie
Means, Jasmine Means, Julia E Maxson, and Jeffrey W Tyner.
Gain-of-function mutations in granulocyte colony–stimulating
fac- tor receptor (csf3r) reveal distinct mechanisms of csf3r ac-
tivation. Journal of Biological Chemistry, 293(19):7387–7396,
2018.

47. Fatemeh Shabani, Alireza Farasat, Majid Mahdavi, and Nematol-
lah Gheibi. Calprotectin (s100a8/s100a9): a key protein between
inflammation and cancer. Inflammation Research, 67(10):801–812,
2018.

48. Lixin Lou, Peng Zhang, Rongli Piao, and Yang Wang. Salmonella
pathogenicity island 1 (spi-1) and its complex regulatory network.
Frontiers in cellular and infection microbiology, 9:270, 2019.

49. Shanshan Kong and Weiqiang Zhang. The function of macro-
molecular complex of cftr-nherf2-lpa2 in inflammatory responses
of intestinal epithelial cells. bioRxiv, page 186023, 2019.

50. Jonathan C Strefford, Malgorzata Nowicka, Chantal E Harg-
reaves, Cathy Burton, Andrew Davies, Rosalind Ganderton, Wolf-
gang Hiddemann, Chisako Iriyama, Wolfram Klapper, Kate V
Latham, et al. Single-nucleotide fcγ receptor polymorphisms do
not impact obinutuzumab/rituximab out- come in patients with
lymphoma. Blood advances, 5(15):2935–2944, 2021.

51. Raheleh Rahbari, Luciana W Zuccherato, German Tischler, Be-
linda Chihota, Hasret Ozturk, Sara Saleem, Eduardo Tarazona-
Santos, Lee R Machado, and Edward J Hollox. Understand-
ing the genomic structure of copy-number variation of the low-
affinity fcγ receptor region allows confirmation of the association
of fcgr3b deletion with rheumatoid arthritis. Human mutation,
38(4):390–399, 2017.

52. David Westergaard, Hans-Henrik Stærfeldt, Christian Tønsberg,
Lars Juhl Jensen, and Søren Brunak. A comprehensive and quan-
titative comparison of text-mining in 15 million full-text articles
versus their corresponding abstracts. PLoS computational biol-
ogy, 14(2):e1005962, 2018.

53. Ji-Yeon Kim, Hae Hyun Jung, Insuk Sohn, Sook Young Woo,
Hyun Cho, Eun Yoon Cho, Jeong Eon Lee, Seok Won Kim, Seok
Jin Nam, Yeon Hee Park, et al. Prognostication of a 13-immune-
related-gene signature in patients with early triple-negative breast
cancer. Breast Cancer Research and Treatment, 184(2):325–334,
2020.

54. Jie Wei, Dalang Fang, and Weijie Zhou. Ccr2 and ptprc are
regulators of tumor microenvironment and potential prognostic
biomarkers of lung adenocarcinoma. Annals of Translational
Medicine, 9(18), 2021.

55. Xuankang Wang, Xin Li, Xiaoshuang Zuo, Zhuowen Liang, Tan
Ding, Kun Li, Yangguang Ma, Penghui Li, Zhijie Zhu, Cheng Ju,
et al. Photobiomodulation inhibits the activation of neurotoxic
microglia and astrocytes by inhibiting lcn2/jak2-stat3 crosstalk af-
ter spinal cord injury in male rats. Journal of neuroinflammation,
18(1):1–20, 2021.

56. Jan Korbecki, Katarzyna Barczak, Izabela Gutowska, Dariusz
Chlubek, and Irena Baranowska- Bosiacka. Cxcl1: Gene, pro-
moter, regulation of expression, mrna stability, regulation of activ-

ity in the intercellular space. International Journal of Molecular
Sciences, 23(2):792, 2022.

57. Boying Zhao, Renfu Lu, Jianjun Chen, Ming Xie, Xingji Zhao,
and Lingwen Kong. S100a9 blockade prevents lipopolysaccharide-
induced lung injury via suppressing the nlrp3 pathway. Res-
piratory Research, 22(1):1–11, 2021.

58. Liang Duan, Rui Wu, Xiuyu Zhang, Ding Wang, Yan You, Yun-
yuan Zhang, Lan Zhou, and Weixian Chen. Hbx-induced s100a9
in nf-κb dependent manner promotes growth and metastasis of
hepatocellular carcinoma cells. Cell death disease, 9(6):1–14,
2018.

Highlights in BioScience Page 8 of 8 July 2022|Volume 5

http://bioscience.highlightsin.org/

	Abstract
	Introduction
	Materials and Methods
	Gene expression data
	Identification of DEGs
	Gene enrichment analysis
	PPI network construction and analysis
	Text-mining

	Results
	Identification of DEGs
	PPI network construction and analysis
	Text mining

	Discussion
	References